diff --git a/desktop_env/providers/aws/manager.py b/desktop_env/providers/aws/manager.py index 09d5172..57b33f6 100644 --- a/desktop_env/providers/aws/manager.py +++ b/desktop_env/providers/aws/manager.py @@ -40,9 +40,9 @@ DEFAULT_REGION = "us-east-1" # todo: public the AMI images IMAGE_ID_MAP = { "us-east-1": { - (1920, 1080): "ami-0d23263edb96951d8" + # (1920, 1080): "ami-0d23263edb96951d8" # For CoACT-1, uncomment to use the following AMI - # (1920, 1080): "ami-0b505e9d0d99ba88c" + (1920, 1080): "ami-0b505e9d0d99ba88c" }, "ap-east-1": { (1920, 1080): "ami-06850864d18fad836" diff --git a/mm_agents/maestro/__init__.py b/mm_agents/maestro/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mm_agents/maestro/cli_app_maestro.py b/mm_agents/maestro/cli_app_maestro.py new file mode 100644 index 0000000..3a1f631 --- /dev/null +++ b/mm_agents/maestro/cli_app_maestro.py @@ -0,0 +1,346 @@ +import argparse +from ast import arg +import datetime +import io +import logging +import os +import platform +import sys +import time +from pathlib import Path +from dotenv import load_dotenv +from PIL import Image +from gui_agents.maestro.controller.main_controller import MainController +# Import analyze_display functionality +from gui_agents.utils.analyze_display import analyze_display_json, aggregate_results, format_output_line +from gui_agents.utils.common_utils import show_task_completion_notification +from desktop_env.desktop_env import DesktopEnv +from gui_agents.utils.common_utils import ImageDataFilter, SafeLoggingFilter + +env_path = Path(os.path.dirname(os.path.abspath(__file__))) / '.env' +if env_path.exists(): + load_dotenv(dotenv_path=env_path) +else: + parent_env_path = Path(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) / '.env' + if parent_env_path.exists(): + load_dotenv(dotenv_path=parent_env_path) + +logger = logging.getLogger() +logger.setLevel(logging.DEBUG) + +datetime_str: str = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + +log_dir = "runtime" +os.makedirs(os.path.join(log_dir, datetime_str), exist_ok=True) + +file_handler = logging.FileHandler( + os.path.join(log_dir, datetime_str, "agents3.log"), encoding="utf-8" +) +debug_handler = logging.FileHandler( + os.path.join(log_dir, datetime_str, "agents3_debug.log"), encoding="utf-8" +) +stdout_handler = logging.StreamHandler(sys.stdout) + +# Add dedicated doubao API log handler +doubao_handler = logging.FileHandler( + os.path.join(log_dir, datetime_str, "doubao_api.log"), encoding="utf-8" +) + +# Create dedicated doubao API logger +doubao_logger = logging.getLogger("doubao_api") +doubao_logger.setLevel(logging.DEBUG) +doubao_logger.addHandler(doubao_handler) + +file_handler.setLevel(logging.INFO) +debug_handler.setLevel(logging.DEBUG) +stdout_handler.setLevel(logging.INFO) +doubao_handler.setLevel(logging.DEBUG) + +# Add SafeLoggingFilter to prevent format errors from third-party libraries (like OpenAI) +safe_filter = SafeLoggingFilter() +debug_handler.addFilter(safe_filter) + +# Also apply SafeLoggingFilter to OpenAI library loggers +try: + import openai + openai_logger = logging.getLogger('openai') + openai_logger.addFilter(safe_filter) + httpx_logger = logging.getLogger('httpx') + httpx_logger.addFilter(safe_filter) + logger.info("SafeLoggingFilter applied to third-party libraries (OpenAI, HTTPX)") +except ImportError: + logger.info("SafeLoggingFilter applied to main handlers only (OpenAI not available)") + pass + +if os.getenv('KEEP_IMAGE_LOGS', 'false').lower() != 'true': + image_filter = ImageDataFilter() + debug_handler.addFilter(image_filter) + logger.info("Image data filtering enabled - image data in debug logs will be filtered") +else: + logger.info("Image data filtering disabled - debug logs will contain complete image data") + +formatter = logging.Formatter( + fmt="\x1b[1;33m[%(asctime)s \x1b[31m%(levelname)s \x1b[32m%(module)s/%(lineno)d-%(processName)s\x1b[1;33m] \x1b[0m%(message)s" +) +file_handler.setFormatter(formatter) +debug_handler.setFormatter(formatter) +stdout_handler.setFormatter(formatter) +doubao_handler.setFormatter(formatter) + +logger.addHandler(file_handler) +logger.addHandler(debug_handler) +logger.addHandler(stdout_handler) + + +def auto_analyze_execution(timestamp_dir: str): + """ + Automatically analyze execution statistics from display.json files after task completion + + Args: + timestamp_dir: Directory containing the execution logs and display.json + """ + import time + + try: + # Analyze the display.json file for this execution + display_json_path = os.path.join(timestamp_dir, "display.json") + + # Wait for file to be fully written + max_wait_time = 10 # Maximum wait time in seconds + wait_interval = 0.5 # Check every 0.5 seconds + waited_time = 0 + + while waited_time < max_wait_time: + if os.path.exists(display_json_path): + # Check if file is still being written by monitoring its size + try: + size1 = os.path.getsize(display_json_path) + time.sleep(wait_interval) + size2 = os.path.getsize(display_json_path) + + # If file size hasn't changed in the last 0.5 seconds, it's likely complete + if size1 == size2: + logger.info(f"Display.json file appears to be complete (size: {size1} bytes)") + break + else: + logger.info(f"Display.json file still being written (size changed from {size1} to {size2} bytes)") + waited_time += wait_interval + continue + except OSError: + # File might be temporarily inaccessible + time.sleep(wait_interval) + waited_time += wait_interval + continue + else: + logger.info(f"Waiting for display.json file to be created... ({waited_time:.1f}s)") + time.sleep(wait_interval) + waited_time += wait_interval + + if os.path.exists(display_json_path): + logger.info(f"Auto-analyzing execution statistics from: {display_json_path}") + + # Analyze the single display.json file + result = analyze_display_json(display_json_path) + + if result: + # Format and log the statistics + output_line = format_output_line(result) + logger.info("=" * 80) + logger.info("EXECUTION STATISTICS:") + logger.info("Steps, Duration (seconds), (Input Tokens, Output Tokens, Total Tokens), Cost") + logger.info("=" * 80) + logger.info(output_line) + logger.info("=" * 80) + + # Also print to console for immediate visibility + print("\n" + "=" * 80) + print("EXECUTION STATISTICS:") + print("Steps, Duration (seconds), (Input Tokens, Output Tokens, Total Tokens), Cost") + print("=" * 80) + print(output_line) + print("=" * 80) + else: + logger.warning("No valid data found in display.json for analysis") + else: + logger.warning(f"Display.json file not found at: {display_json_path} after waiting {max_wait_time} seconds") + + except Exception as e: + logger.error(f"Error during auto-analysis: {e}") + + +def run_agent_maestro(params: dict): + """ + Run the maestro controller with the given instruction + + Args: + controller: The NewController instance to run + instruction: The instruction/task to execute + max_steps: Maximum number of steps to execute + """ + + backend = params["backend"] + user_query = params["query"] + max_steps = params["max_steps"] + current_platform = params["current_platform"] + env = params["env"] + env_password = params["env_password"] + + import time + + logger.info(f"Starting maestro execution with instruction: {user_query}") + + total_start_time = time.time() + # Ensure necessary directory structure exists + timestamp_dir = os.path.join(log_dir, datetime_str) + cache_dir = os.path.join(timestamp_dir, "cache", "screens") + state_dir = os.path.join(timestamp_dir, "state") + + os.makedirs(cache_dir, exist_ok=True) + os.makedirs(state_dir, exist_ok=True) + + # registry = Registry(global_state) + + # Initialize NewController (which includes all other components) + controller = MainController( + platform=current_platform, + backend=backend, + user_query=user_query, + max_steps=max_steps, + env=env, + env_password=env_password, + log_dir=log_dir, + datetime_str=datetime_str + ) + + try: + # Set the user query in the controller + controller.execute_main_loop() + + # Check task status after execution to determine if task was successful + task = controller.global_state.get_task() + if task and task.status == "fulfilled": + # Task completed successfully + logger.info("Task completed successfully") + show_task_completion_notification("success") + elif task and task.status == "rejected": + # Task was rejected/failed + logger.info("Task was rejected/failed") + show_task_completion_notification("failed") + else: + # Task status unknown or incomplete + logger.info("Task execution completed with unknown status") + show_task_completion_notification("completed") + + except Exception as e: + logger.error(f"Error during maestro execution: {e}") + # Show error notification + show_task_completion_notification("error", str(e)) + raise + + finally: + total_end_time = time.time() + total_duration = total_end_time - total_start_time + logger.info(f"Total execution time: {total_duration:.2f} seconds") + + # Auto-analyze execution statistics after task completion + auto_analyze_execution(timestamp_dir) + + +def main(): + parser = argparse.ArgumentParser(description='Maestro CLI Application') + parser.add_argument( + '--backend', + type=str, + default='lybic', + help='Backend to use (e.g., lybic, pyautogui, pyautogui_vmware)') + parser.add_argument('--query', + type=str, + default='', + help='Initial query to execute') + parser.add_argument('--max-steps', + type=int, + default=50, + help='Maximum number of steps to execute (default: 50)') + parser.add_argument( + '--lybic-sid', + type=str, + default=None, + help='Lybic precreated sandbox ID (if not provided, will use LYBIC_PRECREATE_SID environment variable)') + args = parser.parse_args() + + env = None + env_password = "" + + # Set platform to Windows if backend is lybic + if args.backend == 'lybic': + current_platform = 'Windows' + # Initialize hardware interface + backend_kwargs = {"platform": current_platform} + if args.lybic_sid is not None: + backend_kwargs["precreate_sid"] = args.lybic_sid + logger.info(f"Using Lybic SID from command line: {args.lybic_sid}") + else: + logger.info("Using Lybic SID from environment variable LYBIC_PRECREATE_SID") + + elif args.backend == 'pyautogui_vmware': + env_password = "password" + current_platform = os.getenv("USE_PRECREATE_VM", "Windows") + if current_platform == "Ubuntu": + path_to_vm = os.path.join("vmware_vm_data", "Ubuntu0", "Ubuntu0.vmx") + elif current_platform == "Windows": + path_to_vm = os.path.join("vmware_vm_data", "Windows0", "Windows0.vmx") + else: + raise ValueError(f"USE_PRECREATE_VM={current_platform} is not supported. Please use Ubuntu or Windows.") + + env = DesktopEnv( + path_to_vm=path_to_vm, + provider_name="vmware", + os_type=current_platform, + action_space="pyautogui", + require_a11y_tree=False + ) + env.reset() + + else: + current_platform = platform.system() + + logger.info(f"Running maestro on platform: {current_platform}") + logger.info(f"Using backend: {args.backend}") + + logger.info("Maestro components initialized successfully") + + params = { + "backend": args.backend, + "query": '', + "max_steps": args.max_steps, + "current_platform": current_platform, + "env": env, + "env_password": env_password + } + # if query is provided, run the agent on the query + if args.query: + logger.info(f"Executing query: {args.query}") + params["query"] = args.query + run_agent_maestro(params) + + else: + while True: + query = input("Query: ") + params["query"] = query + # Run the agent on your own device + run_agent_maestro(params) + + response = input("Would you like to provide another query? (y/n): ") + if response.lower() != "y": + break + + +if __name__ == "__main__": + """ + python gui_agents/cli_app_maestro.py --backend lybic + python gui_agents/cli_app_maestro.py --backend pyautogui --max-steps 1 + python gui_agents/cli_app_maestro.py --backend pyautogui_vmware --max-steps 1 + python gui_agents/cli_app_maestro.py --backend lybic --max-steps 15 + python gui_agents/cli_app_maestro.py --backend lybic --lybic-sid SBX-01K1X6ZKAERXAN73KTJ1XXJXAF + """ + main() \ No newline at end of file diff --git a/mm_agents/maestro/core/__init__.py b/mm_agents/maestro/core/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mm_agents/maestro/core/engine.py b/mm_agents/maestro/core/engine.py new file mode 100644 index 0000000..c4acd68 --- /dev/null +++ b/mm_agents/maestro/core/engine.py @@ -0,0 +1,1556 @@ +import os +import json +import logging +import backoff + +logger = logging.getLogger() +doubao_logger = logging.getLogger("doubao_api") +import requests +from typing import List, Dict, Any, Optional, Union +import numpy as np +from anthropic import Anthropic +from openai import ( + AzureOpenAI, + APIConnectionError, + APIError, + AzureOpenAI, + OpenAI, + RateLimitError, +) +from google import genai +from google.genai import types +from zhipuai import ZhipuAI +from groq import Groq +import boto3 +import exa_py +from typing import List, Dict, Any, Optional, Union, Tuple + +class ModelPricing: + def __init__(self, pricing_file: str = "model_pricing.json"): + self.pricing_file = pricing_file + self.pricing_data = self._load_pricing() + + def _load_pricing(self) -> Dict: + if os.path.exists(self.pricing_file): + try: + with open(self.pricing_file, 'r', encoding='utf-8') as f: + return json.load(f) + except Exception as e: + print(f"Warning: Failed to load pricing file {self.pricing_file}: {e}") + + return { + "default": {"input": 0, "output": 0} + } + + def get_price(self, model: str) -> Dict[str, float]: + # Handle nested pricing data structure + if "llm_models" in self.pricing_data: + # Iterate through all LLM model categories + for category, models in self.pricing_data["llm_models"].items(): + # Direct model name matching + if model in models: + pricing = models[model] + return self._parse_pricing(pricing) + + # Fuzzy matching for model names + for model_name in models: + if model_name in model or model in model_name: + pricing = models[model_name] + return self._parse_pricing(pricing) + + # Handle embedding models + if "embedding_models" in self.pricing_data: + for category, models in self.pricing_data["embedding_models"].items(): + if model in models: + pricing = models[model] + return self._parse_pricing(pricing) + + for model_name in models: + if model_name in model or model in model_name: + pricing = models[model_name] + return self._parse_pricing(pricing) + + # Default pricing + return {"input": 0, "output": 0} + + def _parse_pricing(self, pricing: Dict[str, str]) -> Dict[str, float]: + """Parse pricing strings and convert to numeric values""" + result = {} + + for key, value in pricing.items(): + if isinstance(value, str): + # Remove currency symbols and units, convert to float + clean_value = value.replace('$', '').replace('￥', '').replace(',', '') + try: + result[key] = float(clean_value) + except ValueError: + result[key] = 0.0 + else: + result[key] = float(value) if value else 0.0 + + return result + + def calculate_cost(self, model: str, input_tokens: int, output_tokens: int) -> float: + pricing = self.get_price(model) + input_cost = (input_tokens / 1000000) * pricing["input"] + output_cost = (output_tokens / 1000000) * pricing["output"] + return input_cost + output_cost + +# Initialize pricing manager with correct pricing file path +pricing_file = os.path.join(os.path.dirname(__file__), 'model_pricing.json') +pricing_manager = ModelPricing(pricing_file) + +def extract_token_usage(response, provider: str) -> Tuple[int, int]: + if "-" in provider: + api_type, vendor = provider.split("-", 1) + else: + api_type, vendor = "llm", provider + + if api_type == "llm": + if vendor in ["openai", "qwen", "deepseek", "doubao", "siliconflow", "monica", "vllm", "groq", "zhipu", "gemini", "openrouter", "azureopenai", "huggingface", "exa", "lybic"]: + if hasattr(response, 'usage') and response.usage: + return response.usage.prompt_tokens, response.usage.completion_tokens + + elif vendor == "anthropic": + if hasattr(response, 'usage') and response.usage: + return response.usage.input_tokens, response.usage.output_tokens + + elif vendor == "bedrock": + if isinstance(response, dict) and "usage" in response: + usage = response["usage"] + return usage.get("input_tokens", 0), usage.get("output_tokens", 0) + + elif api_type == "embedding": + if vendor in ["openai", "azureopenai", "qwen", "doubao"]: + if hasattr(response, 'usage') and response.usage: + return response.usage.prompt_tokens, 0 + + elif vendor == "jina": + if isinstance(response, dict) and "usage" in response: + total_tokens = response["usage"].get("total_tokens", 0) + return total_tokens, 0 + + elif vendor == "gemini": + if hasattr(response, 'usage') and response.usage: + return response.usage.prompt_tokens, 0 + + return 0, 0 + +def calculate_tokens_and_cost(response, provider: str, model: str) -> Tuple[List[int], float]: + input_tokens, output_tokens = extract_token_usage(response, provider) + total_tokens = input_tokens + output_tokens + cost = pricing_manager.calculate_cost(model, input_tokens, output_tokens) + + return [input_tokens, output_tokens, total_tokens], cost + +class LMMEngine: + pass + +# ==================== LLM ==================== + +class LMMEngineOpenAI(LMMEngine): + def __init__( + self, base_url=None, api_key=None, model=None, rate_limit=-1, **kwargs + ): + assert model is not None, "model must be provided" + self.model = model + self.provider = "llm-openai" + + api_key = api_key or os.getenv("OPENAI_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named OPENAI_API_KEY" + ) + + self.base_url = base_url + + self.api_key = api_key + self.request_interval = 0 if rate_limit == -1 else 60.0 / rate_limit + + if not self.base_url: + self.llm_client = OpenAI(api_key=self.api_key) + else: + self.llm_client = OpenAI(base_url=self.base_url, api_key=self.api_key) + + @backoff.on_exception( + backoff.expo, (APIConnectionError, APIError, RateLimitError), max_time=60 + ) + def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs): + """Generate the next message based on previous messages""" + response = self.llm_client.chat.completions.create( + model=self.model, + messages=messages, + max_completion_tokens=max_new_tokens if max_new_tokens else 8192, + **({} if self.model in ["o3", "o3-pro"] else {"temperature": temperature}), + **kwargs, + ) + + content = response.choices[0].message.content + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + + return content, total_tokens, cost + + +class LMMEngineLybic(LMMEngine): + def __init__( + self, base_url=None, api_key=None, model=None, rate_limit=-1, **kwargs + ): + assert model is not None, "model must be provided" + self.model = model + self.provider = "llm-lybic" + + api_key = api_key or os.getenv("LYBIC_LLM_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named LYBIC_LLM_API_KEY" + ) + + self.base_url = base_url or "https://aigw.lybicai.com/v1" + self.api_key = api_key + self.request_interval = 0 if rate_limit == -1 else 60.0 / rate_limit + + self.llm_client = OpenAI(base_url=self.base_url, api_key=self.api_key) + + @backoff.on_exception( + backoff.expo, (APIConnectionError, APIError, RateLimitError), max_time=60 + ) + def generate(self, messages, temperature=1, max_new_tokens=None, **kwargs): + """Generate the next message based on previous messages""" + response = self.llm_client.chat.completions.create( + model=self.model, + messages=messages, + max_completion_tokens=max_new_tokens if max_new_tokens else 8192, + # temperature=temperature, + **kwargs, + ) + + content = response.choices[0].message.content + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + + return content, total_tokens, cost + + +class LMMEngineQwen(LMMEngine): + def __init__( + self, base_url=None, api_key=None, model=None, rate_limit=-1, enable_thinking=False, **kwargs + ): + assert model is not None, "model must be provided" + self.model = model + self.enable_thinking = enable_thinking + self.provider = "llm-qwen" + + api_key = api_key or os.getenv("DASHSCOPE_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named DASHSCOPE_API_KEY" + ) + + self.base_url = base_url or "https://dashscope.aliyuncs.com/compatible-mode/v1" + self.api_key = api_key + self.request_interval = 0 if rate_limit == -1 else 60.0 / rate_limit + + self.llm_client = OpenAI(base_url=self.base_url, api_key=self.api_key) + + @backoff.on_exception( + backoff.expo, (APIConnectionError, APIError, RateLimitError), max_time=60 + ) + def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs): + """Generate the next message based on previous messages""" + # For Qwen3 models, we need to handle thinking mode + extra_body = {} + if self.model.startswith("qwen3") and not self.enable_thinking: + extra_body["enable_thinking"] = False + + response = self.llm_client.chat.completions.create( + model=self.model, + messages=messages, + max_completion_tokens=max_new_tokens if max_new_tokens else 8192, + temperature=temperature, + **extra_body, + **kwargs, + ) + + content = response.choices[0].message.content + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + + return content, total_tokens, cost + + +class LMMEngineDoubao(LMMEngine): + def __init__( + self, base_url=None, api_key=None, model=None, rate_limit=-1, **kwargs + ): + assert model is not None, "model must be provided" + self.model = model + self.provider = "llm-doubao" + + api_key = api_key or os.getenv("ARK_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named ARK_API_KEY" + ) + + self.base_url = base_url or "https://ark.cn-beijing.volces.com/api/v3" + self.api_key = api_key + self.request_interval = 0 if rate_limit == -1 else 60.0 / rate_limit + + self.llm_client = OpenAI(base_url=self.base_url, api_key=self.api_key) + + @backoff.on_exception( + backoff.expo, (APIConnectionError, APIError, RateLimitError), max_time=60 + ) + def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs): + """Generate the next message based on previous messages""" + + # doubao_logger.info(f"Doubao API Call - Model: {self.model}, Temperature: {temperature}, Max Tokens: {max_new_tokens}") + # doubao_logger.info(f"Doubao API Input - Messages count: {len(messages)}") + # doubao_logger.info(f"Doubao API Input - messages: {messages}") + + response = self.llm_client.chat.completions.create( + model=self.model, + messages=messages, + max_completion_tokens=max_new_tokens if max_new_tokens else 8192, + temperature=temperature, + extra_body={ + "thinking": { + "type": "disabled", + # "type": "enabled", + # "type": "auto", + } + }, + **kwargs, + ) + + content = response.choices[0].message.content + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + + # doubao_logger.info(f"Doubao API Response - Content length: {len(content) if content else 0}, Tokens: {total_tokens}, Cost: {cost}") + + # doubao_logger.info(f"Doubao API Response - Content: {content}") + + return content, total_tokens, cost + + +class LMMEngineAnthropic(LMMEngine): + def __init__( + self, base_url=None, api_key=None, model=None, thinking=False, **kwargs + ): + assert model is not None, "model must be provided" + self.model = model + self.thinking = thinking + self.provider = "llm-anthropic" + + api_key = api_key or os.getenv("ANTHROPIC_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named ANTHROPIC_API_KEY" + ) + + self.api_key = api_key + + self.llm_client = Anthropic(api_key=self.api_key) + + @backoff.on_exception( + backoff.expo, (APIConnectionError, APIError, RateLimitError), max_time=60 + ) + def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs): + """Generate the next message based on previous messages""" + if self.thinking: + response = self.llm_client.messages.create( + system=messages[0]["content"][0]["text"], + model=self.model, + messages=messages[1:], + max_tokens=8192, + thinking={"type": "enabled", "budget_tokens": 4096}, + **kwargs, + ) + thoughts = response.content[0].thinking + print("CLAUDE 3.7 THOUGHTS:", thoughts) + content = response.content[1].text + else: + response = self.llm_client.messages.create( + system=messages[0]["content"][0]["text"], + model=self.model, + messages=messages[1:], + max_tokens=max_new_tokens if max_new_tokens else 8192, + temperature=temperature, + **kwargs, + ) + content = response.content[0].text + + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + return content, total_tokens, cost + + +class LMMEngineGemini(LMMEngine): + def __init__( + self, base_url=None, api_key=None, model=None, rate_limit=-1, **kwargs + ): + assert model is not None, "model must be provided" + self.model = model + self.provider = "llm-gemini" + + api_key = api_key or os.getenv("GEMINI_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named GEMINI_API_KEY" + ) + + self.base_url = base_url or os.getenv("GEMINI_ENDPOINT_URL") + if self.base_url is None: + raise ValueError( + "An endpoint URL needs to be provided in either the endpoint_url parameter or as an environment variable named GEMINI_ENDPOINT_URL" + ) + + self.api_key = api_key + self.request_interval = 0 if rate_limit == -1 else 60.0 / rate_limit + + self.llm_client = OpenAI(base_url=self.base_url, api_key=self.api_key) + + @backoff.on_exception( + backoff.expo, (APIConnectionError, APIError, RateLimitError), max_time=60 + ) + def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs): + """Generate the next message based on previous messages""" + response = self.llm_client.chat.completions.create( + model=self.model, + messages=messages, + max_completion_tokens=max_new_tokens if max_new_tokens else 8192, + temperature=temperature, + # reasoning_effort="low", + extra_body={ + 'extra_body': { + "google": { + "thinking_config": { + "thinking_budget": 128, + "include_thoughts": True + } + } + } + }, + **kwargs, + ) + + content = response.choices[0].message.content + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + + return content, total_tokens, cost + + + +class LMMEngineOpenRouter(LMMEngine): + def __init__( + self, base_url=None, api_key=None, model=None, rate_limit=-1, **kwargs + ): + assert model is not None, "model must be provided" + self.model = model + self.provider = "llm-openrouter" + + api_key = api_key or os.getenv("OPENROUTER_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named OPENROUTER_API_KEY" + ) + + self.base_url = base_url or os.getenv("OPEN_ROUTER_ENDPOINT_URL") + if self.base_url is None: + raise ValueError( + "An endpoint URL needs to be provided in either the endpoint_url parameter or as an environment variable named OPEN_ROUTER_ENDPOINT_URL" + ) + + self.api_key = api_key + self.request_interval = 0 if rate_limit == -1 else 60.0 / rate_limit + + self.llm_client = OpenAI(base_url=self.base_url, api_key=self.api_key) + + @backoff.on_exception( + backoff.expo, (APIConnectionError, APIError, RateLimitError), max_time=60 + ) + def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs): + """Generate the next message based on previous messages""" + response = self.llm_client.chat.completions.create( + model=self.model, + messages=messages, + max_completion_tokens=max_new_tokens if max_new_tokens else 8192, + temperature=temperature, + **kwargs, + ) + + content = response.choices[0].message.content + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + + return content, total_tokens, cost + + +class LMMEngineAzureOpenAI(LMMEngine): + def __init__( + self, + base_url=None, + api_key=None, + azure_endpoint=None, + model=None, + api_version=None, + rate_limit=-1, + **kwargs + ): + assert model is not None, "model must be provided" + self.model = model + self.provider = "llm-azureopenai" + + assert api_version is not None, "api_version must be provided" + self.api_version = api_version + + api_key = api_key or os.getenv("AZURE_OPENAI_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named AZURE_OPENAI_API_KEY" + ) + + self.api_key = api_key + + azure_endpoint = azure_endpoint or os.getenv("AZURE_OPENAI_ENDPOINT") + if azure_endpoint is None: + raise ValueError( + "An Azure API endpoint needs to be provided in either the azure_endpoint parameter or as an environment variable named AZURE_OPENAI_ENDPOINT" + ) + + self.azure_endpoint = azure_endpoint + self.request_interval = 0 if rate_limit == -1 else 60.0 / rate_limit + + self.llm_client = AzureOpenAI( + azure_endpoint=self.azure_endpoint, + api_key=self.api_key, + api_version=self.api_version, + ) + self.cost = 0.0 + + # @backoff.on_exception(backoff.expo, (APIConnectionError, APIError, RateLimitError), max_tries=10) + def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs): + """Generate the next message based on previous messages""" + response = self.llm_client.chat.completions.create( + model=self.model, + messages=messages, + max_completion_tokens=max_new_tokens if max_new_tokens else 8192, + temperature=temperature, + **kwargs, + ) + content = response.choices[0].message.content + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + return content, total_tokens, cost + + +class LMMEnginevLLM(LMMEngine): + def __init__( + self, base_url=None, api_key=None, model=None, rate_limit=-1, **kwargs + ): + assert model is not None, "model must be provided" + self.model = model + self.api_key = api_key + self.provider = "llm-vllm" + + self.base_url = base_url or os.getenv("vLLM_ENDPOINT_URL") + if self.base_url is None: + raise ValueError( + "An endpoint URL needs to be provided in either the endpoint_url parameter or as an environment variable named vLLM_ENDPOINT_URL" + ) + + self.request_interval = 0 if rate_limit == -1 else 60.0 / rate_limit + + self.llm_client = OpenAI(base_url=self.base_url, api_key=self.api_key) + + # @backoff.on_exception(backoff.expo, (APIConnectionError, APIError, RateLimitError), max_tries=10) + # TODO: Default params chosen for the Qwen model + def generate( + self, + messages, + temperature=0.0, + top_p=0.8, + repetition_penalty=1.05, + max_new_tokens=512, + **kwargs + ): + """Generate the next message based on previous messages""" + response = self.llm_client.chat.completions.create( + model=self.model, + messages=messages, + max_completion_tokens=max_new_tokens if max_new_tokens else 8192, + temperature=temperature, + top_p=top_p, + extra_body={"repetition_penalty": repetition_penalty}, + ) + content = response.choices[0].message.content + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + return content, total_tokens, cost + + +class LMMEngineHuggingFace(LMMEngine): + def __init__(self, base_url=None, api_key=None, rate_limit=-1, **kwargs): + assert base_url is not None, "HuggingFace endpoint must be provided" + self.base_url = base_url + self.model = base_url.split('/')[-1] if base_url else "huggingface-tgi" + self.provider = "llm-huggingface" + + api_key = api_key or os.getenv("HF_TOKEN") + if api_key is None: + raise ValueError( + "A HuggingFace token needs to be provided in either the api_key parameter or as an environment variable named HF_TOKEN" + ) + + self.api_key = api_key + self.request_interval = 0 if rate_limit == -1 else 60.0 / rate_limit + + self.llm_client = OpenAI(base_url=self.base_url, api_key=self.api_key) + + @backoff.on_exception( + backoff.expo, (APIConnectionError, APIError, RateLimitError), max_time=60 + ) + def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs): + """Generate the next message based on previous messages""" + response = self.llm_client.chat.completions.create( + model="tgi", + messages=messages, + max_completion_tokens=max_new_tokens if max_new_tokens else 8192, + temperature=temperature, + **kwargs, + ) + + content = response.choices[0].message.content + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + + return content, total_tokens, cost + + +class LMMEngineDeepSeek(LMMEngine): + def __init__( + self, base_url=None, api_key=None, model=None, rate_limit=-1, **kwargs + ): + assert model is not None, "model must be provided" + self.model = model + self.provider = "llm-deepseek" + + api_key = api_key or os.getenv("DEEPSEEK_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named DEEPSEEK_API_KEY" + ) + + self.base_url = base_url or "https://api.deepseek.com" + self.api_key = api_key + self.request_interval = 0 if rate_limit == -1 else 60.0 / rate_limit + + self.llm_client = OpenAI(base_url=self.base_url, api_key=self.api_key) + + @backoff.on_exception( + backoff.expo, (APIConnectionError, APIError, RateLimitError), max_time=60 + ) + def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs): + """Generate the next message based on previous messages""" + response = self.llm_client.chat.completions.create( + model=self.model, + messages=messages, + max_completion_tokens=max_new_tokens if max_new_tokens else 8192, + temperature=temperature, + **kwargs, + ) + + content = response.choices[0].message.content + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + return content, total_tokens, cost + + +class LMMEngineZhipu(LMMEngine): + def __init__( + self, base_url=None, api_key=None, model=None, rate_limit=-1, **kwargs + ): + assert model is not None, "model must be provided" + self.model = model + self.provider = "llm-zhipu" + + api_key = api_key or os.getenv("ZHIPU_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named ZHIPU_API_KEY" + ) + + self.api_key = api_key + self.request_interval = 0 if rate_limit == -1 else 60.0 / rate_limit + + # Use ZhipuAI client directly instead of OpenAI compatibility layer + self.llm_client = ZhipuAI(api_key=self.api_key) + + @backoff.on_exception( + backoff.expo, (APIConnectionError, APIError, RateLimitError), max_time=60 + ) + def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs): + """Generate the next message based on previous messages""" + response = self.llm_client.chat.completions.create( + model=self.model, + messages=messages, + max_tokens=max_new_tokens if max_new_tokens else 8192, + temperature=temperature, + **kwargs, + ) + + content = response.choices[0].message.content # type: ignore + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + return content, total_tokens, cost + + + +class LMMEngineGroq(LMMEngine): + def __init__( + self, base_url=None, api_key=None, model=None, rate_limit=-1, **kwargs + ): + assert model is not None, "model must be provided" + self.model = model + self.provider = "llm-groq" + + api_key = api_key or os.getenv("GROQ_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named GROQ_API_KEY" + ) + + self.api_key = api_key + self.request_interval = 0 if rate_limit == -1 else 60.0 / rate_limit + + # Use Groq client directly + self.llm_client = Groq(api_key=self.api_key) + + @backoff.on_exception( + backoff.expo, (APIConnectionError, APIError, RateLimitError), max_time=60 + ) + def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs): + """Generate the next message based on previous messages""" + response = self.llm_client.chat.completions.create( + model=self.model, + messages=messages, + max_completion_tokens=max_new_tokens if max_new_tokens else 8192, + temperature=temperature, + **kwargs, + ) + + content = response.choices[0].message.content + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + return content, total_tokens, cost + + +class LMMEngineSiliconflow(LMMEngine): + def __init__( + self, base_url=None, api_key=None, model=None, rate_limit=-1, **kwargs + ): + assert model is not None, "model must be provided" + self.model = model + self.provider = "llm-siliconflow" + + api_key = api_key or os.getenv("SILICONFLOW_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named SILICONFLOW_API_KEY" + ) + + self.base_url = base_url or "https://api.siliconflow.cn/v1" + self.api_key = api_key + self.request_interval = 0 if rate_limit == -1 else 60.0 / rate_limit + + self.llm_client = OpenAI(base_url=self.base_url, api_key=self.api_key) + + @backoff.on_exception( + backoff.expo, (APIConnectionError, APIError, RateLimitError), max_time=60 + ) + def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs): + """Generate the next message based on previous messages""" + response = self.llm_client.chat.completions.create( + model=self.model, + messages=messages, + max_completion_tokens=max_new_tokens if max_new_tokens else 8192, + temperature=temperature, + **kwargs, + ) + + content = response.choices[0].message.content + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + return content, total_tokens, cost + + +class LMMEngineMonica(LMMEngine): + def __init__( + self, base_url=None, api_key=None, model=None, rate_limit=-1, **kwargs + ): + assert model is not None, "model must be provided" + self.model = model + self.provider = "llm-monica" + + api_key = api_key or os.getenv("MONICA_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named MONICA_API_KEY" + ) + + self.base_url = base_url or "https://openapi.monica.im/v1" + self.api_key = api_key + self.request_interval = 0 if rate_limit == -1 else 60.0 / rate_limit + + self.llm_client = OpenAI(base_url=self.base_url, api_key=self.api_key) + + @backoff.on_exception( + backoff.expo, (APIConnectionError, APIError, RateLimitError), max_time=60 + ) + def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs): + """Generate the next message based on previous messages""" + response = self.llm_client.chat.completions.create( + model=self.model, + messages=messages, + max_completion_tokens=max_new_tokens if max_new_tokens else 8192, + temperature=temperature, + **kwargs, + ) + + content = response.choices[0].message.content + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + return content, total_tokens, cost + + +class LMMEngineAWSBedrock(LMMEngine): + def __init__( + self, + aws_access_key=None, + aws_secret_key=None, + aws_region=None, + model=None, + rate_limit=-1, + **kwargs + ): + assert model is not None, "model must be provided" + self.model = model + self.provider = "llm-bedrock" + + # Claude model mapping for AWS Bedrock + self.claude_model_map = { + "claude-opus-4": "anthropic.claude-opus-4-20250514-v1:0", + "claude-sonnet-4": "anthropic.claude-sonnet-4-20250514-v1:0", + "claude-3-7-sonnet": "anthropic.claude-3-7-sonnet-20250219-v1:0", + "claude-3-5-sonnet": "anthropic.claude-3-5-sonnet-20241022-v2:0", + "claude-3-5-sonnet-20241022": "anthropic.claude-3-5-sonnet-20241022-v2:0", + "claude-3-5-sonnet-20240620": "anthropic.claude-3-5-sonnet-20240620-v1:0", + "claude-3-5-haiku": "anthropic.claude-3-5-haiku-20241022-v1:0", + "claude-3-haiku": "anthropic.claude-3-haiku-20240307-v1:0", + "claude-3-sonnet": "anthropic.claude-3-sonnet-20240229-v1:0", + "claude-3-opus": "anthropic.claude-3-opus-20240229-v1:0", + } + + # Get the actual Bedrock model ID + self.bedrock_model_id = self.claude_model_map.get(model, model) + + # AWS credentials + aws_access_key = aws_access_key or os.getenv("AWS_ACCESS_KEY_ID") + aws_secret_key = aws_secret_key or os.getenv("AWS_SECRET_ACCESS_KEY") + aws_region = aws_region or os.getenv("AWS_DEFAULT_REGION") or "us-west-2" + + if aws_access_key is None: + raise ValueError( + "AWS Access Key needs to be provided in either the aws_access_key parameter or as an environment variable named AWS_ACCESS_KEY_ID" + ) + if aws_secret_key is None: + raise ValueError( + "AWS Secret Key needs to be provided in either the aws_secret_key parameter or as an environment variable named AWS_SECRET_ACCESS_KEY" + ) + + self.aws_region = aws_region + self.request_interval = 0 if rate_limit == -1 else 60.0 / rate_limit + + # Initialize Bedrock client + self.bedrock_client = boto3.client( + service_name="bedrock-runtime", + region_name=aws_region, + aws_access_key_id=aws_access_key, + aws_secret_access_key=aws_secret_key + ) + + @backoff.on_exception( + backoff.expo, (APIConnectionError, APIError, RateLimitError), max_time=60 + ) + def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs): + """Generate the next message based on previous messages""" + + # Convert messages to Bedrock format + # Extract system message if present + system_message = None + user_messages = [] + + for message in messages: + if message["role"] == "system": + if isinstance(message["content"], list): + system_message = message["content"][0]["text"] + else: + system_message = message["content"] + else: + # Handle both list and string content formats + if isinstance(message["content"], list): + content = message["content"][0]["text"] if message["content"] else "" + else: + content = message["content"] + + user_messages.append({ + "role": message["role"], + "content": content + }) + + # Prepare the body for Bedrock + body = { + "max_completion_tokens": max_new_tokens if max_new_tokens else 8192, + "messages": user_messages, + "anthropic_version": "bedrock-2023-05-31" + } + + if temperature > 0: + body["temperature"] = temperature + + if system_message: + body["system"] = system_message + + try: + response = self.bedrock_client.invoke_model( + body=json.dumps(body), + modelId=self.bedrock_model_id + ) + + response_body = json.loads(response.get("body").read()) + + if "content" in response_body and response_body["content"]: + content = response_body["content"][0]["text"] + else: + raise ValueError("No content in response") + + total_tokens, cost = calculate_tokens_and_cost(response_body, self.provider, self.model) + return content, total_tokens, cost + + except Exception as e: + print(f"AWS Bedrock error: {e}") + raise + +# ==================== Embedding ==================== + +class OpenAIEmbeddingEngine(LMMEngine): + def __init__( + self, + embedding_model: str = "text-embedding-3-small", + api_key=None, + **kwargs + ): + """Init an OpenAI Embedding engine + + Args: + embedding_model (str, optional): Model name. Defaults to "text-embedding-3-small". + api_key (_type_, optional): Auth key from OpenAI. Defaults to None. + """ + self.model = embedding_model + self.provider = "embedding-openai" + + api_key = api_key or os.getenv("OPENAI_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named OPENAI_API_KEY" + ) + self.api_key = api_key + + @backoff.on_exception( + backoff.expo, + ( + APIError, + RateLimitError, + APIConnectionError, + ), + ) + def get_embeddings(self, text: str) -> Tuple[np.ndarray, List[int], float]: + client = OpenAI(api_key=self.api_key) + response = client.embeddings.create(model=self.model, input=text) + + embeddings = np.array([data.embedding for data in response.data]) + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + + return embeddings, total_tokens, cost + + + +class GeminiEmbeddingEngine(LMMEngine): + def __init__( + self, + embedding_model: str = "text-embedding-004", + api_key=None, + **kwargs + ): + """Init an Gemini Embedding engine + + Args: + embedding_model (str, optional): Model name. Defaults to "text-embedding-004". + api_key (_type_, optional): Auth key from Gemini. Defaults to None. + """ + self.model = embedding_model + self.provider = "embedding-gemini" + + api_key = api_key or os.getenv("GEMINI_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named GEMINI_API_KEY" + ) + self.api_key = api_key + + @backoff.on_exception( + backoff.expo, + ( + APIError, + RateLimitError, + APIConnectionError, + ), + ) + def get_embeddings(self, text: str) -> Tuple[np.ndarray, List[int], float]: + client = genai.Client(api_key=self.api_key) + + result = client.models.embed_content( + model=self.model, + contents=text, + config=types.EmbedContentConfig(task_type="SEMANTIC_SIMILARITY"), + ) + + embeddings = np.array([i.values for i in result.embeddings]) # type: ignore + total_tokens, cost = calculate_tokens_and_cost(result, self.provider, self.model) + + return embeddings, total_tokens, cost + + + +class AzureOpenAIEmbeddingEngine(LMMEngine): + def __init__( + self, + embedding_model: str = "text-embedding-3-small", + api_key=None, + api_version=None, + endpoint_url=None, + **kwargs + ): + """Init an Azure OpenAI Embedding engine + + Args: + embedding_model (str, optional): Model name. Defaults to "text-embedding-3-small". + api_key (_type_, optional): Auth key from Azure OpenAI. Defaults to None. + api_version (_type_, optional): API version. Defaults to None. + endpoint_url (_type_, optional): Endpoint URL. Defaults to None. + """ + self.model = embedding_model + self.provider = "embedding-azureopenai" + + api_key = api_key or os.getenv("AZURE_OPENAI_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named AZURE_OPENAI_API_KEY" + ) + self.api_key = api_key + + api_version = api_version or os.getenv("OPENAI_API_VERSION") + if api_version is None: + raise ValueError( + "An API Version needs to be provided in either the api_version parameter or as an environment variable named OPENAI_API_VERSION" + ) + self.api_version = api_version + + endpoint_url = endpoint_url or os.getenv("AZURE_OPENAI_ENDPOINT") + if endpoint_url is None: + raise ValueError( + "An Endpoint URL needs to be provided in either the endpoint_url parameter or as an environment variable named AZURE_OPENAI_ENDPOINT" + ) + self.endpoint_url = endpoint_url + + @backoff.on_exception( + backoff.expo, + ( + APIError, + RateLimitError, + APIConnectionError, + ), + ) + def get_embeddings(self, text: str) -> Tuple[np.ndarray, List[int], float]: + client = AzureOpenAI( + api_key=self.api_key, + api_version=self.api_version, + azure_endpoint=self.endpoint_url, + ) + response = client.embeddings.create(input=text, model=self.model) + + embeddings = np.array([data.embedding for data in response.data]) + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + + return embeddings, total_tokens, cost + + +class DashScopeEmbeddingEngine(LMMEngine): + def __init__( + self, + embedding_model: str = "text-embedding-v4", + api_key=None, + dimensions: int = 1024, + **kwargs + ): + """Init a DashScope Embedding engine + + Args: + embedding_model (str, optional): Model name. Defaults to "text-embedding-v4". + api_key (_type_, optional): Auth key from DashScope. Defaults to None. + dimensions (int, optional): Embedding dimensions. Defaults to 1024. + """ + self.model = embedding_model + self.dimensions = dimensions + self.provider = "embedding-qwen" + + api_key = api_key or os.getenv("DASHSCOPE_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named DASHSCOPE_API_KEY" + ) + self.api_key = api_key + + # Initialize OpenAI client with DashScope base URL + self.client = OpenAI( + api_key=self.api_key, + base_url="https://dashscope.aliyuncs.com/compatible-mode/v1" + ) + + @backoff.on_exception( + backoff.expo, + ( + APIError, + RateLimitError, + APIConnectionError, + ), + ) + def get_embeddings(self, text: str) -> Tuple[np.ndarray, List[int], float]: + response = self.client.embeddings.create( + model=self.model, + input=text, + dimensions=self.dimensions, + encoding_format="float" + ) + + embeddings = np.array([data.embedding for data in response.data]) + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + + return embeddings, total_tokens, cost + + + +class DoubaoEmbeddingEngine(LMMEngine): + def __init__( + self, + embedding_model: str = "doubao-embedding-256", + api_key=None, + **kwargs + ): + """Init a Doubao Embedding engine + + Args: + embedding_model (str, optional): Model name. Defaults to "doubao-embedding-256". + api_key (_type_, optional): Auth key from Doubao. Defaults to None. + """ + self.model = embedding_model + self.provider = "embedding-doubao" + + api_key = api_key or os.getenv("ARK_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named ARK_API_KEY" + ) + self.api_key = api_key + self.base_url = "https://ark.cn-beijing.volces.com/api/v3" + + # Use OpenAI-compatible client for text embeddings + self.client = OpenAI( + api_key=self.api_key, + base_url=self.base_url + ) + + @backoff.on_exception( + backoff.expo, + ( + APIError, + RateLimitError, + APIConnectionError, + ), + ) + def get_embeddings(self, text: str) -> Tuple[np.ndarray, List[int], float]: + # Log embedding request + logger.info(f"Doubao Embedding API Call - Model: {self.model}, Text length: {len(text)}") + doubao_logger.info(f"Doubao Embedding API Call - Model: {self.model}, Text length: {len(text)}") + + response = self.client.embeddings.create( + model=self.model, + input=text, + encoding_format="float" + ) + + embeddings = np.array([data.embedding for data in response.data]) + total_tokens, cost = calculate_tokens_and_cost(response, self.provider, self.model) + + # Log embedding response + logger.info(f"Doubao Embedding API Response - Embedding dimension: {embeddings.shape}, Tokens: {total_tokens}, Cost: {cost}") + doubao_logger.info(f"Doubao Embedding API Response - Embedding dimension: {embeddings.shape}, Tokens: {total_tokens}, Cost: {cost}") + + return embeddings, total_tokens, cost + + +class JinaEmbeddingEngine(LMMEngine): + def __init__( + self, + embedding_model: str = "jina-embeddings-v4", + api_key=None, + task: str = "retrieval.query", + **kwargs + ): + """Init a Jina AI Embedding engine + + Args: + embedding_model (str, optional): Model name. Defaults to "jina-embeddings-v4". + api_key (_type_, optional): Auth key from Jina AI. Defaults to None. + task (str, optional): Task type. Options: "retrieval.query", "retrieval.passage", "text-matching". Defaults to "retrieval.query". + """ + self.model = embedding_model + self.task = task + self.provider = "embedding-jina" + + api_key = api_key or os.getenv("JINA_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named JINA_API_KEY" + ) + self.api_key = api_key + self.base_url = "https://api.jina.ai/v1" + + @backoff.on_exception( + backoff.expo, + ( + APIError, + RateLimitError, + APIConnectionError, + ), + ) + def get_embeddings(self, text: str) -> Tuple[np.ndarray, List[int], float]: + import requests + + headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {self.api_key}" + } + + data = { + "model": self.model, + "task": self.task, + "input": [ + { + "text": text + } + ] + } + + response = requests.post( + f"{self.base_url}/embeddings", + headers=headers, + json=data + ) + + if response.status_code != 200: + raise Exception(f"Jina AI API error: {response.text}") + + result = response.json() + embeddings = np.array([data["embedding"] for data in result["data"]]) + + total_tokens, cost = calculate_tokens_and_cost(result, self.provider, self.model) + + return embeddings, total_tokens, cost + + +# ==================== webSearch ==================== +class SearchEngine: + """Base class for search engines""" + pass + +class BochaAISearchEngine(SearchEngine): + def __init__( + self, + api_key: str|None = None, + base_url: str = "https://api.bochaai.com/v1", + rate_limit: int = -1, + **kwargs + ): + """Init a Bocha AI Search engine + + Args: + api_key (str, optional): Auth key from Bocha AI. Defaults to None. + base_url (str, optional): Base URL for the API. Defaults to "https://api.bochaai.com/v1". + rate_limit (int, optional): Rate limit per minute. Defaults to -1 (no limit). + """ + api_key = api_key or os.getenv("BOCHA_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named BOCHA_API_KEY" + ) + + self.api_key = api_key + self.base_url = base_url + self.endpoint = f"{base_url}/ai-search" + self.request_interval = 0 if rate_limit == -1 else 60.0 / rate_limit + + @backoff.on_exception( + backoff.expo, + ( + APIConnectionError, + APIError, + RateLimitError, + requests.exceptions.RequestException, + ), + max_time=60 + ) + def search( + self, + query: str, + freshness: str = "noLimit", + answer: bool = True, + stream: bool = False, + **kwargs + ) -> Union[Dict[str, Any], Any]: + """Search with AI and return intelligent answer + + Args: + query (str): Search query + freshness (str, optional): Freshness filter. Defaults to "noLimit". + answer (bool, optional): Whether to return answer. Defaults to True. + stream (bool, optional): Whether to stream response. Defaults to False. + + Returns: + Union[Dict[str, Any], Any]: AI search results with sources and answer + """ + headers = { + 'Authorization': f'Bearer {self.api_key}', + 'Content-Type': 'application/json' + } + + payload = { + "query": query, + "freshness": freshness, + "answer": answer, + "stream": stream, + **kwargs + } + + if stream: + result = self._stream_search(headers, payload) + return result, [0, 0, 0], 0.06 + else: + result = self._regular_search(headers, payload) + return result, [0, 0, 0], 0.06 + + + def _regular_search(self, headers: Dict[str, str], payload: Dict[str, Any]) -> Dict[str, Any]: + """Regular non-streaming search""" + response = requests.post( + self.endpoint, + headers=headers, + json=payload + ) + + if response.status_code != 200: + raise APIError(f"Bocha AI Search API error: {response.text}") # type: ignore + + return response.json() + + def _stream_search(self, headers: Dict[str, str], payload: Dict[str, Any]): + """Streaming search response""" + response = requests.post( + self.endpoint, + headers=headers, + json=payload, + stream=True + ) + + if response.status_code != 200: + raise APIError(f"Bocha AI Search API error: {response.text}") # type: ignore + + for line in response.iter_lines(): + if line: + line = line.decode('utf-8') + if line.startswith('data:'): + data = line[5:].strip() + if data and data != '{"event":"done"}': + try: + yield json.loads(data) + except json.JSONDecodeError: + continue + + def get_answer(self, query: str, **kwargs) -> Tuple[str, int, float]: + """Get AI generated answer only""" + result, _, remaining_balance = self.search(query, answer=True, **kwargs) + + # Extract answer from messages + messages = result.get("messages", []) # type: ignore + answer = "" + for message in messages: + if message.get("type") == "answer": + answer = message.get("content", "") + break + + return answer, [0,0,0], remaining_balance # type: ignore + + + def get_sources(self, query: str, **kwargs) -> List[Dict[str, Any]]: + """Get source materials only""" + result, _, remaining_balance = self.search(query, **kwargs) + + # Extract sources from messages + sources = [] + messages = result.get("messages", []) # type: ignore + for message in messages: + if message.get("type") == "source": + content_type = message.get("content_type", "") + if content_type in ["webpage", "image", "video", "baike_pro", "medical_common"]: + sources.append({ + "type": content_type, + "content": json.loads(message.get("content", "{}")) + }) + + return sources, 0, remaining_balance # type: ignore + + + def get_follow_up_questions(self, query: str, **kwargs) -> List[str]: + """Get follow-up questions""" + result, _, remaining_balance = self.search(query, **kwargs) + + # Extract follow-up questions from messages + follow_ups = [] + messages = result.get("messages", []) # type: ignore + for message in messages: + if message.get("type") == "follow_up": + follow_ups.append(message.get("content", "")) + + return follow_ups, 0, remaining_balance # type: ignore + + +class ExaResearchEngine(SearchEngine): + def __init__( + self, + api_key: str|None = None, + base_url: str = "https://api.exa.ai", + rate_limit: int = -1, + **kwargs + ): + """Init an Exa Research engine + + Args: + api_key (str, optional): Auth key from Exa AI. Defaults to None. + base_url (str, optional): Base URL for the API. Defaults to "https://api.exa.ai". + rate_limit (int, optional): Rate limit per minute. Defaults to -1 (no limit). + """ + api_key = api_key or os.getenv("EXA_API_KEY") + if api_key is None: + raise ValueError( + "An API Key needs to be provided in either the api_key parameter or as an environment variable named EXA_API_KEY" + ) + + self.api_key = api_key + self.base_url = base_url + self.request_interval = 0 if rate_limit == -1 else 60.0 / rate_limit + + # Initialize OpenAI-compatible client for chat completions + self.chat_client = OpenAI( + base_url=base_url, + api_key=api_key + ) + + # Initialize Exa client for research tasks + try: + from exa_py import Exa + self.exa_client = Exa(api_key=api_key) + except ImportError: + self.exa_client = None + print("Warning: exa_py not installed. Research tasks will not be available.") + + @backoff.on_exception( + backoff.expo, + ( + APIConnectionError, + APIError, + RateLimitError, + ), + max_time=60 + ) + def search(self, query: str, **kwargs): + """Standard Exa search with direct cost from API + + Args: + query (str): Search query + **kwargs: Additional search parameters + + Returns: + tuple: (result, tokens, cost) where cost is actual API cost + """ + headers = { + 'x-api-key': self.api_key, + 'Content-Type': 'application/json' + } + + payload = { + "query": query, + **kwargs + } + + response = requests.post( + f"{self.base_url}/search", + headers=headers, + json=payload + ) + + if response.status_code != 200: + raise APIError(f"Exa Search API error: {response.text}") # type: ignore + + result = response.json() + + cost = 0.0 + if "costDollars" in result: + cost = result["costDollars"].get("total", 0.0) + + return result, [0, 0, 0], cost + + def chat_research( + self, + query: str, + model: str = "exa", + stream: bool = False, + **kwargs + ) -> Union[str, Any]: + """Research using chat completions interface + + Args: + query (str): Research query + model (str, optional): Model name. Defaults to "exa". + stream (bool, optional): Whether to stream response. Defaults to False. + + Returns: + Union[str, Any]: Research result or stream + """ + messages = [ + {"role": "user", "content": query} + ] + + if stream: + completion = self.chat_client.chat.completions.create( + model=model, + messages=messages, # type: ignore + stream=True, + **kwargs + ) + return completion + else: + completion = self.chat_client.chat.completions.create( + model=model, + messages=messages, # type: ignore + **kwargs + ) + result = completion.choices[0].message.content # type: ignore + return result,[0,0,0],0.005 diff --git a/mm_agents/maestro/core/mllm.py b/mm_agents/maestro/core/mllm.py new file mode 100644 index 0000000..3e561df --- /dev/null +++ b/mm_agents/maestro/core/mllm.py @@ -0,0 +1,566 @@ +import base64 + +import numpy as np + +from .engine import ( + LMMEngineAnthropic, + LMMEngineAzureOpenAI, + LMMEngineHuggingFace, + LMMEngineOpenAI, + LMMEngineLybic, + LMMEngineOpenRouter, + LMMEnginevLLM, + LMMEngineGemini, + LMMEngineQwen, + LMMEngineDoubao, + LMMEngineDeepSeek, + LMMEngineZhipu, + LMMEngineGroq, + LMMEngineSiliconflow, + LMMEngineMonica, + LMMEngineAWSBedrock, + OpenAIEmbeddingEngine, + GeminiEmbeddingEngine, + AzureOpenAIEmbeddingEngine, + DashScopeEmbeddingEngine, + DoubaoEmbeddingEngine, + JinaEmbeddingEngine, + BochaAISearchEngine, + ExaResearchEngine, +) + +class CostManager: + """Cost manager, responsible for adding currency symbols based on engine type""" + + # Chinese engines use CNY + CNY_ENGINES = { + LMMEngineQwen, LMMEngineDoubao, LMMEngineDeepSeek, LMMEngineZhipu, + LMMEngineSiliconflow, DashScopeEmbeddingEngine, DoubaoEmbeddingEngine + } + # Other engines use USD + USD_ENGINES = { + LMMEngineOpenAI, LMMEngineLybic, LMMEngineAnthropic, LMMEngineAzureOpenAI, LMMEngineGemini, + LMMEngineOpenRouter, LMMEnginevLLM, LMMEngineHuggingFace, LMMEngineGroq, + LMMEngineMonica, LMMEngineAWSBedrock, OpenAIEmbeddingEngine, + GeminiEmbeddingEngine, AzureOpenAIEmbeddingEngine, JinaEmbeddingEngine + } + + @classmethod + def get_currency_symbol(cls, engine) -> str: + engine_type = type(engine) + + if engine_type in cls.CNY_ENGINES: + return "￥" + elif engine_type in cls.USD_ENGINES: + return "$" + else: + return "$" + + @classmethod + def format_cost(cls, cost: float, engine) -> str: + currency = cls.get_currency_symbol(engine) + return f"{cost:.7f}{currency}" + + @classmethod + def add_costs(cls, cost1: str, cost2: str) -> str: + currency_symbols = ["$", "￥", "¥", "€", "£"] + currency1 = currency2 = "$" + value1 = value2 = 0.0 + + if isinstance(cost1, (int, float)): + value1 = float(cost1) + currency1 = "$" + else: + cost1_str = str(cost1) + for symbol in currency_symbols: + if symbol in cost1_str: + value1 = float(cost1_str.replace(symbol, "").strip()) + currency1 = symbol + break + else: + try: + value1 = float(cost1_str) + currency1 = "$" + except: + value1 = 0.0 + + if isinstance(cost2, (int, float)): + value2 = float(cost2) + currency2 = "$" + else: + cost2_str = str(cost2) + for symbol in currency_symbols: + if symbol in cost2_str: + value2 = float(cost2_str.replace(symbol, "").strip()) + currency2 = symbol + break + else: + try: + value2 = float(cost2_str) + currency2 = "$" + except: + value2 = 0.0 + + if currency1 != currency2: + print(f"Warning: Different currencies in cost accumulation: {currency1} and {currency2}") + currency = currency1 + else: + currency = currency1 + + total_value = value1 + value2 + return f"{total_value:.6f}{currency}" + +class LLMAgent: + def __init__(self, engine_params=None, system_prompt=None, engine=None): + if engine is None: + if engine_params is not None: + engine_type = engine_params.get("engine_type") + if engine_type == "openai": + self.engine = LMMEngineOpenAI(**engine_params) + elif engine_type == "lybic": + self.engine = LMMEngineLybic(**engine_params) + elif engine_type == "anthropic": + self.engine = LMMEngineAnthropic(**engine_params) + elif engine_type == "azure": + self.engine = LMMEngineAzureOpenAI(**engine_params) + elif engine_type == "vllm": + self.engine = LMMEnginevLLM(**engine_params) + elif engine_type == "huggingface": + self.engine = LMMEngineHuggingFace(**engine_params) + elif engine_type == "gemini": + self.engine = LMMEngineGemini(**engine_params) + elif engine_type == "openrouter": + self.engine = LMMEngineOpenRouter(**engine_params) + elif engine_type == "dashscope": + self.engine = LMMEngineQwen(**engine_params) + elif engine_type == "doubao": + self.engine = LMMEngineDoubao(**engine_params) + elif engine_type == "deepseek": + self.engine = LMMEngineDeepSeek(**engine_params) + elif engine_type == "zhipu": + self.engine = LMMEngineZhipu(**engine_params) + elif engine_type == "groq": + self.engine = LMMEngineGroq(**engine_params) + elif engine_type == "siliconflow": + self.engine = LMMEngineSiliconflow(**engine_params) + elif engine_type == "monica": + self.engine = LMMEngineMonica(**engine_params) + elif engine_type == "aws_bedrock": + self.engine = LMMEngineAWSBedrock(**engine_params) + else: + raise ValueError("engine_type is not supported") + else: + raise ValueError("engine_params must be provided") + else: + self.engine = engine + + self.messages = [] # Empty messages + + if system_prompt: + self.add_system_prompt(system_prompt) + else: + self.add_system_prompt("You are a helpful assistant.") + + def encode_image(self, image_content): + # if image_content is a path to an image file, check type of the image_content to verify + if isinstance(image_content, str): + with open(image_content, "rb") as image_file: + return base64.b64encode(image_file.read()).decode("utf-8") + else: + return base64.b64encode(image_content).decode("utf-8") + + def reset( + self, + ): + + self.messages = [ + { + "role": "system", + "content": [{"type": "text", "text": self.system_prompt}], + } + ] + + def add_system_prompt(self, system_prompt): + self.system_prompt = system_prompt + if len(self.messages) > 0: + self.messages[0] = { + "role": "system", + "content": [{"type": "text", "text": self.system_prompt}], + } + else: + self.messages.append( + { + "role": "system", + "content": [{"type": "text", "text": self.system_prompt}], + } + ) + + def remove_message_at(self, index): + """Remove a message at a given index""" + if index < len(self.messages): + self.messages.pop(index) + + def replace_message_at( + self, index, text_content, image_content=None, image_detail="high" + ): + """Replace a message at a given index""" + if index < len(self.messages): + self.messages[index] = { + "role": self.messages[index]["role"], + "content": [{"type": "text", "text": text_content}], + } + if image_content: + base64_image = self.encode_image(image_content) + self.messages[index]["content"].append( + { + "type": "image_url", + "image_url": { + "url": f"data:image/png;base64,{base64_image}", + "detail": image_detail, + }, + } + ) + + def add_message( + self, + text_content, + image_content=None, + role=None, + image_detail="high", + put_text_last=False, + ): + """Add a new message to the list of messages""" + + # API-style inference from OpenAI and similar services + if isinstance( + self.engine, + ( + LMMEngineAnthropic, + LMMEngineAzureOpenAI, + LMMEngineHuggingFace, + LMMEngineOpenAI, + LMMEngineLybic, + LMMEngineOpenRouter, + LMMEnginevLLM, + LMMEngineGemini, + LMMEngineQwen, + LMMEngineDoubao, + LMMEngineDeepSeek, + LMMEngineZhipu, + LMMEngineGroq, + LMMEngineSiliconflow, + LMMEngineMonica, + LMMEngineAWSBedrock, + ), + ): + # infer role from previous message + if role != "user": + if self.messages[-1]["role"] == "system": + role = "user" + elif self.messages[-1]["role"] == "user": + role = "assistant" + elif self.messages[-1]["role"] == "assistant": + role = "user" + + message = { + "role": role, + "content": [{"type": "text", "text": text_content}], + } + + if isinstance(image_content, np.ndarray) or image_content: + # Check if image_content is a list or a single image + if isinstance(image_content, list): + # If image_content is a list of images, loop through each image + for image in image_content: + base64_image = self.encode_image(image) + message["content"].append( + { + "type": "image_url", + "image_url": { + "url": f"data:image/png;base64,{base64_image}", + "detail": image_detail, + }, + } + ) + else: + # If image_content is a single image, handle it directly + base64_image = self.encode_image(image_content) + message["content"].append( + { + "type": "image_url", + "image_url": { + "url": f"data:image/png;base64,{base64_image}", + "detail": image_detail, + }, + } + ) + + # Rotate text to be the last message if desired + if put_text_last: + text_content = message["content"].pop(0) + message["content"].append(text_content) + + self.messages.append(message) + + # For API-style inference from Anthropic + elif isinstance(self.engine, (LMMEngineAnthropic, LMMEngineAWSBedrock)): + # infer role from previous message + if role != "user": + if self.messages[-1]["role"] == "system": + role = "user" + elif self.messages[-1]["role"] == "user": + role = "assistant" + elif self.messages[-1]["role"] == "assistant": + role = "user" + + message = { + "role": role, + "content": [{"type": "text", "text": text_content}], + } + + if image_content: + # Check if image_content is a list or a single image + if isinstance(image_content, list): + # If image_content is a list of images, loop through each image + for image in image_content: + base64_image = self.encode_image(image) + message["content"].append( + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/png", + "data": base64_image, + }, + } + ) + else: + # If image_content is a single image, handle it directly + base64_image = self.encode_image(image_content) + message["content"].append( + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/png", + "data": base64_image, + }, + } + ) + self.messages.append(message) + + # Locally hosted vLLM model inference + elif isinstance(self.engine, LMMEnginevLLM): + # infer role from previous message + if role != "user": + if self.messages[-1]["role"] == "system": + role = "user" + elif self.messages[-1]["role"] == "user": + role = "assistant" + elif self.messages[-1]["role"] == "assistant": + role = "user" + + message = { + "role": role, + "content": [{"type": "text", "text": text_content}], + } + + if image_content: + # Check if image_content is a list or a single image + if isinstance(image_content, list): + # If image_content is a list of images, loop through each image + for image in image_content: + base64_image = self.encode_image(image) + message["content"].append( + { + "type": "image_url", + "image_url": { + "url": f"data:image;base64,{base64_image}" + }, + } + ) + else: + # If image_content is a single image, handle it directly + base64_image = self.encode_image(image_content) + message["content"].append( + { + "type": "image_url", + "image_url": {"url": f"data:image;base64,{base64_image}"}, + } + ) + + self.messages.append(message) + else: + raise ValueError("engine_type is not supported") + + def get_response( + self, + user_message=None, + messages=None, + temperature=0.0, + max_new_tokens=None, + **kwargs, + ): + """Generate the next response based on previous messages""" + if messages is None: + messages = self.messages + if user_message: + messages.append( + {"role": "user", "content": [{"type": "text", "text": user_message}]} + ) + + if isinstance(self.engine, LMMEngineLybic): + content, total_tokens, cost = self.engine.generate( + messages, + max_new_tokens=max_new_tokens, # type: ignore + **kwargs, + ) + else: + content, total_tokens, cost = self.engine.generate( + messages, + temperature=temperature, + max_new_tokens=max_new_tokens, # type: ignore + **kwargs, + ) + + cost_string = CostManager.format_cost(cost, self.engine) + + return content, total_tokens, cost_string + +class EmbeddingAgent: + def __init__(self, engine_params=None, engine=None): + if engine is None: + if engine_params is not None: + engine_type = engine_params.get("engine_type") + if engine_type == "openai": + self.engine = OpenAIEmbeddingEngine(**engine_params) + elif engine_type == "gemini": + self.engine = GeminiEmbeddingEngine(**engine_params) + elif engine_type == "azure": + self.engine = AzureOpenAIEmbeddingEngine(**engine_params) + elif engine_type == "dashscope": + self.engine = DashScopeEmbeddingEngine(**engine_params) + elif engine_type == "doubao": + self.engine = DoubaoEmbeddingEngine(**engine_params) + elif engine_type == "jina": + self.engine = JinaEmbeddingEngine(**engine_params) + else: + raise ValueError(f"Embedding engine type '{engine_type}' is not supported") + else: + raise ValueError("engine_params must be provided") + else: + self.engine = engine + + def get_embeddings(self, text): + """Get embeddings for the given text + + Args: + text (str): The text to get embeddings for + + Returns: + numpy.ndarray: The embeddings for the text + """ + embeddings, total_tokens, cost = self.engine.get_embeddings(text) + cost_string = CostManager.format_cost(cost, self.engine) + return embeddings, total_tokens, cost_string + + + def get_similarity(self, text1, text2): + """Calculate the cosine similarity between two texts + + Args: + text1 (str): First text + text2 (str): Second text + + Returns: + float: Cosine similarity score between the two texts + """ + embeddings1, tokens1, cost1 = self.get_embeddings(text1) + embeddings2, tokens2, cost2 = self.get_embeddings(text2) + + # Calculate cosine similarity + dot_product = np.dot(embeddings1, embeddings2) + norm1 = np.linalg.norm(embeddings1) + norm2 = np.linalg.norm(embeddings2) + + similarity = dot_product / (norm1 * norm2) + total_tokens = tokens1 + tokens2 + total_cost = CostManager.add_costs(cost1, cost2) + + return similarity, total_tokens, total_cost + + def batch_get_embeddings(self, texts): + """Get embeddings for multiple texts + + Args: + texts (List[str]): List of texts to get embeddings for + + Returns: + List[numpy.ndarray]: List of embeddings for each text + """ + embeddings = [] + total_tokens = [0, 0, 0] + if texts: + first_embedding, first_tokens, first_cost = self.get_embeddings(texts[0]) + embeddings.append(first_embedding) + total_tokens[0] += first_tokens[0] + total_tokens[1] += first_tokens[1] + total_tokens[2] += first_tokens[2] + total_cost = first_cost + + for text in texts[1:]: + embedding, tokens, cost = self.get_embeddings(text) + embeddings.append(embedding) + total_tokens[0] += tokens[0] + total_tokens[1] += tokens[1] + total_tokens[2] += tokens[2] + total_cost = CostManager.add_costs(total_cost, cost) + else: + currency = CostManager.get_currency_symbol(self.engine) + total_cost = f"0.0{currency}" + + return embeddings, total_tokens, total_cost + + +class WebSearchAgent: + def __init__(self, engine_params=None, engine=None): + if engine is None: + if engine_params is not None: + self.engine_type = engine_params.get("engine_type") + if self.engine_type == "bocha": + self.engine = BochaAISearchEngine(**engine_params) + elif self.engine_type == "exa": + self.engine = ExaResearchEngine(**engine_params) + else: + raise ValueError(f"Web search engine type '{self.engine_type}' is not supported") + else: + raise ValueError("engine_params must be provided") + else: + self.engine = engine + + def get_answer(self, query, **kwargs): + """Get a direct answer for the query + + Args: + query (str): The search query + **kwargs: Additional arguments to pass to the search engine + + Returns: + str: The answer text + """ + if isinstance(self.engine, BochaAISearchEngine): + answer, tokens, cost = self.engine.get_answer(query, **kwargs) + return answer, tokens, str(cost) + + elif isinstance(self.engine, ExaResearchEngine): + # For Exa, we'll use the chat_research method which returns a complete answer + # results, tokens, cost = self.engine.search(query, **kwargs) + results, tokens, cost = self.engine.chat_research(query, **kwargs) + if isinstance(results, dict) and "messages" in results: + for message in results.get("messages", []): + if message.get("type") == "answer": + return message.get("content", ""), tokens, str(cost) + return str(results), tokens, str(cost) + + else: + raise ValueError(f"Web search engine type '{self.engine_type}' is not supported") diff --git a/mm_agents/maestro/core/model.md b/mm_agents/maestro/core/model.md new file mode 100644 index 0000000..42ea5cb --- /dev/null +++ b/mm_agents/maestro/core/model.md @@ -0,0 +1,385 @@ +# Supported Model Providers and Model Lists + +## LLM Model Providers + +### 1. OpenAI + +**Provider** + +- `openai` + +**Supported Models:** + +- `gpt-5` Window: 400,000 Max Output Tokens: 128,000 +- `gpt-5-mini` Window: 400,000 Max Output Tokens: 128,000 +- `gpt-4.1-nano` Window: 400,000 Max Output Tokens: 128,000 +- `gpt-4.1` Window: 1,047,576 Max Output Tokens: 32,768 +- `gpt-4.1-mini` Window: 1,047,576 Max Output Tokens: 32,768 +- `gpt-4.1-nano` Window: 1,047,576 Max Output Tokens: 32,768 +- `gpt-4o` Window: 128,000 Max Output Tokens: 16,384 +- `gpt-4o-mini` Window: 128,000 Max Output Tokens: 16,384 +- `o1` Window: 200,000 Max Output Tokens: 100,000 +- `o1-pro` Window: 200,000 Max Output Tokens: 100,000 +- `o1-mini` Window: 200,000 Max Output Tokens: 100,000 +- `o3` Window: 200,000 Max Output Tokens: 100,000 +- `o3-pro` Window: 200,000 Max Output Tokens: 100,000 +- `o3-mini` Window: 200,000 Max Output Tokens: 100,000 +- `o4-mini` Window: 200,000 Max Output Tokens: 100,000 + +**Embedding Models:** + +- `text-embedding-3-small` +- `text-embedding-3-large` +- `text-embedding-ada-002` + +📚 **Reference Link:** + +--- + +### 2. Anthropic Claude + +**Provider** + +- `anthropic` + +**Supported Models:** + +- `claude-opus-4-1-20250805` Context window: 200K Max output: 32000 +- `claude-opus-4-20250514` Context window: 200K Max output: 32000 +- `claude-sonnet-4-20250514` Context window: 200K Max output: 64000 +- `claude-3-7-sonnet-20250219` Context window: 200K Max output: 64000 +- - `claude-3-5-sonnet-20240620` Context window: 200K Max output: 64000 +- `claude-3-5-haiku-20241022` Context window: 200K Max output: 8192 + +📚 **Reference Link:** + +--- + +### 3. AWS Bedrock + +**Provider** + +- `bedrock` + + +**Supported Claude Models:** + +- `Claude-Opus-4` +- `Claude-Sonnet-4` +- `Claude-Sonnet-3.7` +- `Claude-Sonnet-3.5` + +📚 **Reference Link:** + +--- + +### 4. Google Gemini + +**Provider** + +- `gemini` + +**Supported Models:** + +- `gemini-2.5-pro` in: 1,048,576 out: 65536 +- `gemini-2.5-flash` in: 1,048,576 out: 65536 +- `gemini-2.0-flash` in: 1,048,576 out: 8192 +- `gemini-1.5-pro` in: 2,097,152 out: 8192 +- `gemini-1.5-flash` in: 1,048,576 out: 8192 + +**Embedding Models:** + +- `gemini-embedding-001` + +📚 **Reference Link:** + +--- + +### 5. Groq + +**Provider** + +- `groq` + +**Supported Models:** + +- `Kimi-K2-Instruct` +- `Llama-4-Scout-17B-16E-Instruct` +- `Llama-4-Maverick-17B-128E-Instruct` +- `Llama-Guard-4-12B` +- `DeepSeek-R1-Distill-Llama-70B` +- `Qwen3-32B` +- `Llama-3.3-70B-Instruct` + +📚 **Reference Link:** + +--- + +### 6. Monica (Proxy Platform) + +**Provider** + +- `monica` + +**OpenAI Models:** + +- `gpt-4.1` +- `gpt-4.1-mini` +- `gpt-4.1-nano` +- `gpt-4o-2024-11-20` +- `gpt-4o-mini-2024-07-18` +- `o4-mini` +- `o3` + +**Anthropic Claude Models:** + +- `claude-opus-4-20250514` +- `claude-sonnet-4-20250514` +- `claude-3-7-sonnet-latest` +- `claude-3-5-sonnet-20241022` +- `claude-3-5-sonnet-20240620` +- `claude-3-5-haiku-20241022` + + +**Google Gemini Models:** + +- `gemini-2.5-pro-preview-03-25` +- `gemini-2.5-flash-lite` +- `gemini-2.5-flash-preview-05-20` +- `gemini-2.0-flash-001` +- `gemini-1.5-pro-002` +- `gemini-1.5-flash-002` + +**DeepSeek Models:** + +- `deepseek-reasoner` +- `deepseek-chat` + +**Meta Llama Models:** + +- `Llama-4-Scout-17B-16E-Instruct` Context length: 10M tokens +- `Llama-4-Maverick-17B-128E-Instruct ` Context length: 1M tokens +- `llama-3.3-70b-instruct` +- `llama-3-70b-instruct` +- `llama-3.1-405b-instruct` + +**xAI Grok Models:** + +- `grok-3-beta` +- `grok-beta` + +📚 **Reference Link:** + +--- + +### 7. OpenRouter (Proxy Platform) + +**Provider** + +- `openrouter` + +**OpenAI Models:** + +- `gpt-4.1` +- `gpt-4.1-mini` +- `o1` +- `o1-pro` +- `o1-mini` +- `o3` +- `o3-pro` +- `o3-mini` +- `o4-mini` + +**xAI Grok Models:** + +- `grok-4` Total Context: 256K Max Output: 256K +- `grok-3` +- `grok-3-mini` + +**Anthropic Claude Models:** + +- `claude-opus-4` +- `claude-sonnet-4` + +**Google Gemini Models:** + +- `gemini-2.5-flash` +- `gemini-2.5-pro` + +📚 **Reference Link:** + +--- + +### 8. Azure OpenAI + +**Provider** + +- `azure` + + +**Supported Models:** + +- `gpt-4.1` +- `gpt-4.1-mini` +- `gpt-4.1-nano` +- `o1` +- `o3` +- `o4-mini` + +📚 **Reference Link:** + +--- + +### 9. Lybic AI + +**Provider:** + +- `lybic` + +**Supported Models:** + +- `gpt-5` +- `gpt-4.1` +- `gpt-4.1-mini` +- `gpt-4.1-nano` +- `gpt-4.5-preview` +- `gpt-4o` +- `gpt-4o-realtime-preview` +- `gpt-4o-mini` +- `o1` +- `o1-pro` +- `o1-mini` +- `o3` +- `o3-pro` +- `o3-mini` +- `o4-mini` + +**Note:** Lybic AI provides OpenAI-compatible API endpoints with the same model names and pricing structure. + +📚 **Reference Link:** + +--- + +### 10. DeepSeek + +**Provider** + +- `deepseek` + +**Supported Models:** + +- `deepseek-chat` Context length: 128K, Output length: Default 4K, Max 8K +- `deepseek-reasoner` Context length: 128K, Output length: Default 32K, Max 64K + +📚 **Reference Link:** + +--- + +### 11. Alibaba Cloud Qwen + +**Supported Models:** + +- `qwen-max-latest` Context window: 32,768 Max input token length: 30,720 Max generation token length: 8,192 +- `qwen-plus-latest` Context window: 131,072 Max input token length: 98,304 (thinking) Max generation token length: 129,024 Max output: 16,384 +- `qwen-turbo-latest` Context window: 1,000,000 Max input token length: 1,000,000 Max generation token length: 16,384 +- `qwen-vl-max-latest` (Grounding) Context window: 131,072 Max input token length: 129,024 Max generation token length: 8,192 +- `qwen-vl-plus-latest` (Grounding) Context window: 131,072 Max input token length: 129,024 Max generation token length: 8,192 + +**Embedding Models:** + +- `text-embedding-v4` +- `text-embedding-v3` + +📚 **Reference Link:** + +--- + +### 12. ByteDance Doubao + +**Supported Models:** + +- `doubao-seed-1-6-flash-250615` Context window: 256k Max input token length: 224k Max generation token length: 32k Max thinking content token length: 32k +- `doubao-seed-1-6-thinking-250715` Context window: 256k Max input token length: 224k Max generation token length: 32k Max thinking content token length: 32k +- `doubao-seed-1-6-250615` Context window: 256k Max input token length: 224k Max generation token length: 32k Max thinking content token length: 32k +- `doubao-1.5-vision-pro-250328` (Grounding) Context window: 128k Max input token length: 96k Max generation token length: 16k Max thinking content token length: 32k +- `doubao-1-5-thinking-vision-pro-250428` (Grounding) Context window: 128k Max input token length: 96k Max generation token length: 16k Max thinking content token length: 32k +- `doubao-1-5-ui-tars-250428` (Grounding) Context window: 128k Max input token length: 96k Max generation token length: 16k Max thinking content token length: 32k + +**Embedding Models:** + +- `doubao-embedding-large-text-250515` +- `doubao-embedding-text-240715` + +📚 **Reference Link:** + +--- + +### 13. Zhipu GLM + +**Supported Models:** + +- `GLM-4.5` Max in: 128k Max output: 0.2K +- `GLM-4.5-X` Max in: 128k Max output: 0.2K +- `GLM-4.5-Air` Max in: 128k Max output: 0.2K +- `GLM-4-Plus` +- `GLM-4-Air-250414` +- `GLM-4-AirX` (Grounding) +- `GLM-4V-Plus-0111` (Grounding) + +**Embedding Models:** + +- `Embedding-3` +- `Embedding-2` + +📚 **Reference Link:** + +--- + +### 14. SiliconFlow + +**Supported Models:** + +- `Kimi-K2-Instruct` Context Length: 128K +- `DeepSeek-V3` +- `DeepSeek-R1` +- `Qwen3-32B` + +📚 **Reference Link:** + +--- + +## 🔤 Dedicated Embedding Providers + +### 15. Jina AI + +**Embedding Models:** + +- `jina-embeddings-v4` +- `jina-embeddings-v3` + +📚 **Reference Link:** + +--- + +## 🔍 AI Search Engines + +### 16. Bocha AI + +**Service Type:** AI Research & Search + +📚 **Reference Link:** + +--- + +### 17. Exa + +**Service Type:** AI Research & Search + +**Pricing Model:** + +- $5.00 / 1k agent searches +- $5.00 / 1k exa-research agent page reads +- $10.00 / 1k exa-research-pro agent page reads +- $5.00 / 1M reasoning tokens + +📚 **Reference Link:** diff --git a/mm_agents/maestro/core/model_pricing.json b/mm_agents/maestro/core/model_pricing.json new file mode 100644 index 0000000..d8c4722 --- /dev/null +++ b/mm_agents/maestro/core/model_pricing.json @@ -0,0 +1,194 @@ +{ + "llm_models": { + "openai": { + "gpt-4.1": {"input": "2.00$", "output": "8.00$"}, + "gpt-4.1-mini": {"input": "0.40$", "output": "1.60$"}, + "gpt-4.1-nano": {"input": "0.10$", "output": "0.40$"}, + "gpt-4.5-preview": {"input": "75$", "output": "150$"}, + "gpt-4o": {"input": "2.5$", "output": "10$"}, + "gpt-4o-realtime-preview": {"input": "5$", "output": "20$"}, + "gpt-4o-mini": {"input": "0.15$", "output": "0.6$"}, + "o1": {"input": "15$", "output": "60$"}, + "o1-pro": {"input": "150$", "output": "600$"}, + "o1-mini": {"input": "1.10$", "output": "4.40$"}, + "o3": {"input": "2.0$", "output": "8$"}, + "o3-pro": {"input": "20$", "output": "80$"}, + "o3-mini": {"input": "1.10$", "output": "4.40$"}, + "o4-mini": {"input": "1.1$", "output": "4.40$"} + }, + "anthropic": { + "claude-opus-4-20250514": {"input": "15$", "output": "75$"}, + "claude-sonnet-4-20250514": {"input": "3$", "output": "15$"}, + "claude-3-7-sonnet-20250219": {"input": "3$", "output": "15$"}, + "claude-3-5-sonnet-20241022": {"input": "3$", "output": "15$"}, + "claude-3-5-haiku-20241022": {"input": "0.8$", "output": "4$"} + }, + "qwen": { + "qwen-max-latest": {"input": "2.4￥", "output": "9.6￥"}, + "qwen-plus-latest": {"input": "0.8￥", "output": "2￥"}, + "qwen-turbo-latest": {"input": "0.3￥", "output": "0.6￥"}, + "qwen-vl-max-latest": {"input": "3￥", "output": "9￥"}, + "qwen-vl-plus-latest": {"input": "1.5￥", "output": "4.5￥"} + }, + "doubao": { + "doubao-seed-1-6-flash-250615": {"input": "0.15￥", "output": "1.50￥"}, + "doubao-seed-1-6-thinking-250715": {"input": "0.8￥", "output": "8￥"}, + "doubao-seed-1-6-250615": {"input": "0.8￥", "output": "2￥"}, + "doubao-1.5-vision-pro-250328": {"input": "3￥", "output": "9￥"}, + "doubao-1-5-thinking-vision-pro-250428": {"input": "3￥", "output": "9￥"}, + "doubao-1-5-ui-tars-250428": {"input": "3.5￥", "output": "12￥"} + }, + "deepseek": { + "deepseek-chat": {"input": "2￥", "output": "8￥"}, + "deepseek-reasoner": {"input": "4￥", "output": "16￥"} + }, + "zhipu": { + "GLM-4.5": {"input": "4￥", "output": "16￥"}, + "GLM-4.5V": {"input": "4￥", "output": "12￥"}, + "GLM-4-Plus": {"input": "5￥", "output": "5￥"}, + "GLM-4-Air-250414": {"input": "0.5￥", "output": "0.5￥"}, + "GLM-4-AirX": {"input": "10￥", "output": "10￥"}, + "GLM-4V-Plus-0111": {"input": "4￥", "output": "4￥"} + }, + "groq": { + "Kimi-K2-Instruct": {"input": "1.00$", "output": "3.00$"}, + "Llama-4-Scout-17B-16E-Instruct": {"input": "0.11$", "output": "0.34$"}, + "Llama-4-Maverick-17B-128E-Instruct": {"input": "0.20$", "output": "0.60$"}, + "Llama-Guard-4-12B": {"input": "0.20$", "output": "0.20$"}, + "DeepSeek-R1-Distill-Llama-70B": {"input": "0.75$", "output": "0.99$"}, + "Qwen3-32B": {"input": "0.29$", "output": "0.59$"}, + "Llama-3.3-70B-Instruct": {"input": "0.59$", "output": "0.79$"} + }, + "siliconflow": { + "Kimi-K2-Instruct": {"input": "4￥", "output": "16￥"}, + "DeepSeek-V3": {"input": "2￥", "output": "8￥"}, + "DeepSeek-R1": {"input": "4￥", "output": "16￥"}, + "Qwen3-32B": {"input": "1￥", "output": "4￥"} + }, + "monica": { + "gpt-4.1": {"input": "2.00$", "output": "8.00$"}, + "gpt-4.1-mini": {"input": "0.40$", "output": "1.60$"}, + "gpt-4.1-nano": {"input": "0.10$", "output": "0.40$"}, + "gpt-4o-2024-11-20": {"input": "2.50$", "output": "10.00$"}, + "gpt-4o-mini-2024-07-18": {"input": "0.15$", "output": "0.60$"}, + "o4-mini": {"input": "0.55$", "output": "2.20$"}, + "o3": {"input": "2.00$", "output": "8.00$"}, + "claude-opus-4-20250514": {"input": "15.00$", "output": "75.00$"}, + "claude-sonnet-4-20250514": {"input": "3.00$", "output": "15.00$"}, + "claude-3-7-sonnet-latest": {"input": "3.00$", "output": "15.00$"}, + "claude-3-5-sonnet-20241022": {"input": "3.00$", "output": "15.00$"}, + "claude-3-5-sonnet-20240620": {"input": "3.00$", "output": "15.00$"}, + "claude-3-5-haiku-20241022": {"input": "0.80$", "output": "4.00$"}, + "claude-3-opus-20240229": {"input": "15.00$", "output": "75.00$"}, + "claude-3-haiku-20240307": {"input": "0.25$", "output": "1.25$"}, + "gemini-2.5-pro-preview-03-25": {"input": "1.25$", "output": "10.00$"}, + "gemini-2.5-flash-lite": {"input": "0.10$", "output": "0.40$"}, + "gemini-2.5-flash-preview-05-20": {"input": "0.30$", "output": "2.50$"}, + "gemini-2.0-flash-001": {"input": "0.10$", "output": "0.40$"}, + "gemini-1.5-pro-002": {"input": "1.25$", "output": "5.00$"}, + "gemini-1.5-flash-002": {"input": "0.075$", "output": "0.30$"}, + "deepseek-reasoner": {"input": "0.55$", "output": "2.21$"}, + "deepseek-chat": {"input": "0.28$", 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"output": ""} + }, + "gemini": { + "gemini-embedding-001": {"input": "0.15$", "output": ""} + } + }, + "search_models": { + "bocha": { + "ai-search": {"cost_type": "balance", "unit": "per_query"} + }, + "exa": { + "search": {"cost_type": "direct", "unit": "per_query"}, + "research": {"cost_type": "direct", "unit": "per_task"} + } + } + } diff --git a/mm_agents/maestro/core/new_knowledge.py b/mm_agents/maestro/core/new_knowledge.py new file mode 100644 index 0000000..b48eca3 --- /dev/null +++ b/mm_agents/maestro/core/new_knowledge.py @@ -0,0 +1,481 @@ +import json +import os +from typing import Dict, Tuple, List +import numpy as np +from sklearn.metrics.pairwise import cosine_similarity +from ..utils.common_utils import ( + load_embeddings, + load_knowledge_base, + save_embeddings, +) +from ..tools.new_tools import NewTools +from .mllm import CostManager + +def get_embedding_dim(model_name): + if model_name == "doubao-embedding-large-text-250515": + return 2048 + elif model_name == "doubao-embedding-text-240715": + return 2560 + elif model_name == "text-embedding-ada-002": + return 1536 + elif model_name == "text-embedding-3-small": + return 1536 + elif model_name == "text-embedding-3-large": + return 3072 + elif model_name == "gemini-embedding-001": + return 3072 + elif model_name == "jina-embeddings-v4": + return 2048 + elif model_name == "jina-embeddings-v3": + return 1024 + elif model_name == "text-embedding-v4": + return 1024 + elif model_name == "text-embedding-v3": + return 1024 + elif model_name == "embedding-2" or model_name == "embedding-3": + return 2048 + else: + return None + +class NewKnowledgeBase: + def __init__( + self, + embedding_engine: NewTools, + local_kb_path: str, + platform: str, + Tools_dict: Dict, + save_knowledge: bool = True, + ): + """ + Initialize the KnowledgeBase module + + Args: + embedding_engine: Embedding engine instance + local_kb_path: Path to local knowledge base + platform: Target platform (Windows/Darwin/Ubuntu) + Tools_dict: Dictionary containing tool configurations + save_knowledge: Whether to save knowledge embeddings + """ + self.platform = platform + + self.local_kb_path = local_kb_path + + # initialize embedding engine + self.embedding_engine = embedding_engine + + # Initialize paths for different memory types + self.episodic_memory_path = os.path.join( + self.local_kb_path, self.platform, "episodic_memory.json" + ) + self.narrative_memory_path = os.path.join( + self.local_kb_path, self.platform, "narrative_memory.json" + ) + embedding_model_name = "" + if hasattr(self.embedding_engine, "tools") and "embedding" in self.embedding_engine.tools: + embedding_model_name = self.embedding_engine.tools["embedding"].model_name + else: + embedding_model_name = "default" + embedding_dim = get_embedding_dim(embedding_model_name) + self.embeddings_path = os.path.join( + self.local_kb_path, self.platform, f"embeddings_{embedding_model_name}_{embedding_dim}.pkl" + ) + + # Initialize trajectory tracking + self.task_trajectory = "" + self.current_subtask_trajectory = "" + self.current_search_query = "" + + # query_formulator + self.query_formulator_name = "query_formulator" + self.query_formulator = NewTools() + self.query_formulator.register_tool( + self.query_formulator_name, + Tools_dict[self.query_formulator_name]["provider"], + Tools_dict[self.query_formulator_name]["model"], + ) + + # knowledge_fusion_agent + self.knowledge_fusion_agent_name = "context_fusion" + self.knowledge_fusion_agent = NewTools() + self.knowledge_fusion_agent.register_tool( + self.knowledge_fusion_agent_name, + Tools_dict[self.knowledge_fusion_agent_name]["provider"], + Tools_dict[self.knowledge_fusion_agent_name]["model"], + ) + + # narrative_summarization_agent + self.narrative_summarization_agent_name = "narrative_summarization" + self.narrative_summarization_agent = NewTools() + self.narrative_summarization_agent.register_tool( + self.narrative_summarization_agent_name, + Tools_dict[self.narrative_summarization_agent_name]["provider"], + Tools_dict[self.narrative_summarization_agent_name]["model"], + ) + + # episode_summarization_agent + self.episode_summarization_agent_name = "episode_summarization" + self.episode_summarization_agent = NewTools() + self.episode_summarization_agent.register_tool( + self.episode_summarization_agent_name, + Tools_dict[self.episode_summarization_agent_name]["provider"], + Tools_dict[self.episode_summarization_agent_name]["model"], + ) + + self.save_knowledge = save_knowledge + + def retrieve_knowledge( + self, instruction: str, search_query: str, search_engine: NewTools + ) -> Tuple[str, List[int], str]: + """Retrieve knowledge using search engine + Args: + instruction (str): task instruction + search_query (str): search query to use + search_engine (NewTools): search engine tool to use + + Returns: + Tuple[str, List[int], float]: The search results, token usage, and cost + """ + search_results, total_tokens, cost_string = search_engine.execute_tool("websearch", {"str_input": instruction + " " + search_query}) + + return search_results, total_tokens, cost_string + + def formulate_query(self, instruction: str, observation: Dict) -> Tuple[str, List[int], str]: + """Formulate search query based on instruction and current state + + Args: + instruction (str): The task instruction + observation (Dict): Current observation including screenshot + + Returns: + Tuple[str, List[int], float]: The formulated query, token usage, and cost + """ + query_path = os.path.join( + self.local_kb_path, self.platform, "formulate_query.json" + ) + try: + with open(query_path, "r") as f: + formulate_query = json.load(f) + except: + formulate_query = {} + + if instruction in formulate_query: + return formulate_query[instruction], [0, 0, 0], "" + + self.query_formulator.tools["query_formulator"].llm_agent.reset() + + content, total_tokens, cost_string = self.query_formulator.execute_tool("query_formulator", { + "str_input": f"The task is: {instruction}\n" + + "To use google search to get some useful information, first carefully analyze " + + "the screenshot of the current desktop UI state, then given the task " + + "instruction, formulate a question that can be used to search on the Internet " + + "for information in helping with the task execution.\n" + + "The question should not be too general or too specific. Please ONLY provide " + + "the question.\nQuestion:", + "img_input": observation["screenshot"] if "screenshot" in observation else None + }) + + search_query = content.strip().replace('"', "") + + # print("search query: ", search_query) + formulate_query[instruction] = search_query + with open(query_path, "w") as f: + json.dump(formulate_query, f, indent=2) + + return search_query, total_tokens, cost_string + + def retrieve_narrative_experience(self, instruction: str) -> Tuple[str, str, List[int], str]: + """Retrieve narrative experience using embeddings + + Args: + instruction (str): The task instruction + + Returns: + Tuple[str, str]: The similar task key and its narrative experience + """ + + knowledge_base = load_knowledge_base(self.narrative_memory_path) + if not knowledge_base: + return "None", "None", [0, 0, 0], "" + + embeddings = load_embeddings(self.embeddings_path) + + # Get or create instruction embedding + instruction_embedding = embeddings.get(instruction) + total_tokens, cost_string = [0, 0, 0], "" + + if instruction_embedding is None: + instruction_embedding, tokens, cost_string_now = self.embedding_engine.execute_tool("embedding", {"str_input": instruction}) + embeddings[instruction] = instruction_embedding + # total_tokens += tokens + for i in range(len(total_tokens)): + total_tokens[i] += tokens[i] + cost_string = cost_string_now + # Get or create embeddings for knowledge base entries + candidate_embeddings = [] + for key in knowledge_base: + candidate_embedding = embeddings.get(key) + if candidate_embedding is None: + candidate_embedding, tokens, cost_string_now = self.embedding_engine.execute_tool("embedding", {"str_input": key}) + for i in range(len(tokens)): + total_tokens[i] += tokens[i] + # total_tokens += tokens + cost_string = CostManager.add_costs(cost_string, cost_string_now) + embeddings[key] = candidate_embedding + + candidate_embeddings.append(candidate_embedding) + + save_embeddings(self.embeddings_path, embeddings) + + similarities = cosine_similarity( + instruction_embedding, np.vstack(candidate_embeddings) + )[0] + sorted_indices = np.argsort(similarities)[::-1] + + keys = list(knowledge_base.keys()) + idx = 1 if keys[sorted_indices[0]] == instruction else 0 + return keys[sorted_indices[idx]], knowledge_base[keys[sorted_indices[idx]]], total_tokens, cost_string + + def retrieve_episodic_experience(self, instruction: str) -> Tuple[str, str, List[int], str]: + """Retrieve similar task experience using embeddings + + Args: + instruction (str): The task instruction + + Returns: + Tuple[str, str]: The similar task key and its episodic experience + """ + + knowledge_base = load_knowledge_base(self.episodic_memory_path) + if not knowledge_base: + return "None", "None", [0, 0, 0], "" + + embeddings = load_embeddings(self.embeddings_path) + + # Get or create instruction embedding + instruction_embedding = embeddings.get(instruction) + total_tokens, cost_string = [0, 0, 0], "" + + if instruction_embedding is None: + instruction_embedding, tokens, cost_string_now = self.embedding_engine.execute_tool("embedding", {"str_input": instruction}) + embeddings[instruction] = instruction_embedding + + # total_tokens += tokens + for i in range(len(total_tokens)): + total_tokens[i] += tokens[i] + cost_string = cost_string_now + + # Get or create embeddings for knowledge base entries + candidate_embeddings = [] + for key in knowledge_base: + candidate_embedding = embeddings.get(key) + if candidate_embedding is None: + candidate_embedding, tokens, cost_string_now = self.embedding_engine.execute_tool("embedding", {"str_input": key}) + # total_tokens += tokens + for i in range(len(total_tokens)): + total_tokens[i] += tokens[i] + cost_string = CostManager.add_costs(cost_string, cost_string_now) + embeddings[key] = candidate_embedding + + candidate_embeddings.append(candidate_embedding) + + save_embeddings(self.embeddings_path, embeddings) + + similarities = cosine_similarity( + instruction_embedding, np.vstack(candidate_embeddings) + )[0] + sorted_indices = np.argsort(similarities)[::-1] + + keys = list(knowledge_base.keys()) + idx = 1 if keys[sorted_indices[0]] == instruction else 0 + return keys[sorted_indices[idx]], knowledge_base[keys[sorted_indices[idx]]], total_tokens, cost_string + + def knowledge_fusion( + self, + observation: Dict, + instruction: str, + web_knowledge: str, + similar_task: str, + experience: str, + ) -> Tuple[str, list, str]: + """Combine web knowledge with similar task experience""" + + content, total_tokens, cost = self.knowledge_fusion_agent.execute_tool("context_fusion", { + "str_input": f"Task: {instruction}\n" + + f"**Web search result**:\n{web_knowledge}\n\n" + + f"**Retrieved similar task experience**:\n" + + f"Similar task:{similar_task}\n{experience}\n\n" + + f"Based on the web search result and the retrieved similar task experience, " + + f"if you think the similar task experience is indeed useful to the main task, " + + f"integrate it with the web search result. Provide the final knowledge in a numbered list.", + "img_input": observation["screenshot"] if "screenshot" in observation else None + }) + + return content, total_tokens, cost + + + def save_episodic_memory(self, subtask_key: str, subtask_traj: str) -> None: + """Save episodic memory (subtask level knowledge). + + Args: + subtask_key (str): Key identifying the subtask + subtask_traj (str): Trajectory/experience of the subtask + """ + if not self.save_knowledge: + return + + try: + kb = load_knowledge_base(self.episodic_memory_path) + except: + kb = {} + + if subtask_key not in kb: + subtask_summarization = self.summarize_episode(subtask_traj) + kb[subtask_key] = subtask_summarization + + if self.save_knowledge: + os.makedirs(os.path.dirname(self.episodic_memory_path), exist_ok=True) + with open(self.episodic_memory_path, "w") as fout: + json.dump(kb, fout, indent=2) + + return kb.get(subtask_key) + + def save_narrative_memory(self, task_key: str, task_traj: str) -> None: + """Save narrative memory (task level knowledge). + + Args: + task_key (str): Key identifying the task + task_traj (str): Full trajectory/experience of the task + """ + if not self.save_knowledge: + return + + try: + kb = load_knowledge_base(self.narrative_memory_path) + except: + kb = {} + + if task_key not in kb: + task_summarization = self.summarize_narrative(task_traj) + kb[task_key] = task_summarization + + if self.save_knowledge: + os.makedirs(os.path.dirname(self.narrative_memory_path), exist_ok=True) + with open(self.narrative_memory_path, "w") as fout: + json.dump(kb, fout, indent=2) + + return kb.get(task_key) + + def initialize_task_trajectory(self, instruction: str) -> None: + """Initialize a new task trajectory. + + Args: + instruction (str): The task instruction + """ + self.task_trajectory = f"Task:\n{instruction}" + self.current_search_query = "" + self.current_subtask_trajectory = "" + + def update_task_trajectory(self, meta_data: Dict) -> None: + """Update the task trajectory with new metadata. + + Args: + meta_data (Dict): Metadata from the agent's prediction + """ + if not self.current_search_query and "search_query" in meta_data: + self.current_search_query = meta_data["search_query"] + + self.task_trajectory += ( + "\n\nReflection:\n" + + str(meta_data["reflection"]) + + "\n\n----------------------\n\nPlan:\n" + + meta_data["executor_plan"] + ) + + def handle_subtask_trajectory(self, meta_data: Dict): + """Handle subtask trajectory updates based on subtask status. + + Args: + meta_data (Dict): Metadata containing subtask information + + Returns: + bool: Whether the subtask was completed + """ + subtask_status = meta_data["subtask_status"] + subtask = meta_data["subtask"] + subtask_info = meta_data["subtask_info"] + + if subtask_status in ["Start", "Done"]: + # If there's an existing subtask trajectory, finalize it + if self.current_subtask_trajectory: + self.current_subtask_trajectory += "\nSubtask Completed.\n" + subtask_key = self.current_subtask_trajectory.split( + "\n----------------------\n\nPlan:\n" + )[0] + self.save_episodic_memory(subtask_key, self.current_subtask_trajectory) + self.current_subtask_trajectory = "" + return True + + # Start new subtask trajectory + self.current_subtask_trajectory = ( + f"Task:\n{self.current_search_query}\n\n" + f"Subtask: {subtask}\n" + f"Subtask Instruction: {subtask_info}\n" + f"----------------------\n\n" + f'Plan:\n{meta_data["executor_plan"]}\n' + ) + return False + + elif subtask_status == "In": + # Continue current subtask trajectory + self.current_subtask_trajectory += ( + f'\n----------------------\n\nPlan:\n{meta_data["executor_plan"]}\n' + ) + return False + + def finalize_task(self) -> None: + """Finalize the task by saving any remaining trajectories.""" + # Save any remaining subtask trajectory + if self.current_subtask_trajectory: + self.current_subtask_trajectory += "\nSubtask Completed.\n" + subtask_key = self.current_subtask_trajectory.split( + "\n----------------------\n\nPlan:\n" + )[0] + self.save_episodic_memory(subtask_key, self.current_subtask_trajectory) + + # Save the complete task trajectory + if self.task_trajectory and self.current_search_query: + self.save_narrative_memory(self.current_search_query, self.task_trajectory) + + # Reset trajectories + self.task_trajectory = "" + self.current_subtask_trajectory = "" + self.current_search_query = "" + + def summarize_episode(self, trajectory: str) -> Tuple[str, List[int], str]: + """Summarize the episode experience for lifelong learning reflection + + Args: + trajectory (str): The episode experience to be summarized + + Returns: + str: The summarized episode experience + """ + + # Create Reflection on whole trajectories for next round trial, keep earlier messages as exemplars + content, total_tokens, cost = self.episode_summarization_agent.execute_tool("episode_summarization", {"str_input": trajectory}) + + return content, total_tokens, cost + + def summarize_narrative(self, trajectory: str) -> Tuple[str, List[int], str]: + """Summarize the narrative experience for lifelong learning reflection + + Args: + trajectory (str): The narrative experience to be summarized + + Returns: + str: The summarized narrative experience + """ + # Create Reflection on whole trajectories for next round trial + content, total_tokens, cost = self.narrative_summarization_agent.execute_tool("narrative_summarization", {"str_input": trajectory}) + + return content, total_tokens, cost diff --git a/mm_agents/maestro/maestro/Action.py b/mm_agents/maestro/maestro/Action.py new file mode 100644 index 0000000..23c7262 --- /dev/null +++ b/mm_agents/maestro/maestro/Action.py @@ -0,0 +1,241 @@ +from __future__ import annotations +"""actions.py ▸ Unified Action primitives with helper registry +------------------------------------------------------------------- +This module defines *declarative* GUI/OS operations as tiny dataclasses +("Actions"). An **Action** describes *what* should be done — **not** *how* it +is executed. Concrete back‑ends (PyAutoGUI, ADB, cloud device API …) are free +to translate the intent into platform‑specific commands. + +Key features +============ +1. **Registry‑based reflection** – Every Action subclass registers itself in + `Action._registry` at import‑time. +2. **(De)serialisation** – Each Action can be converted to / from a plain + `dict` (JSON‑safe) via `to_dict()` / `from_dict()`. +3. **Type safety & docs** – `dataclass`+`Enum` give IDE hints & runtime checks. + +Typical workflow +---------------- +```python +>>> from actions import click, drag, action +>>> a1 = click(x = 200, y = 300) +>>> payload = a1.to_dict() # ➜ {"type": "click", "x": 200, "y": 300], ...} +>>> a2 = Action.from_dict(payload) # ➜ click(x=200, y=300, ...) +>>> assert a1 == a2 +``` +The registry makes the last line work without an if‑else chain. +""" + +from abc import ABC +from dataclasses import dataclass, field, fields, asdict +from enum import Enum, auto +from typing import Any, Dict, List, Tuple, Type, TypeVar, ClassVar + +__all__ = [ + "Action", + # concrete actions ↓ + "Click", + "DoubleClick", + "Move", + "Scroll", + "Drag", + "TypeText", + "Hotkey", + "Wait", + "Done", + "Failed", + "Screenshot", + "Memorize", + "SetCellValues", + "SwitchApplications", + "Open", +] + +T_Action = TypeVar("T_Action", bound="Action") + + +# --------------------------------------------------------------------------- +# Action base‑class with helper registry +# --------------------------------------------------------------------------- +@dataclass(slots=True) +class Action(ABC): + """Abstract base for every declarative GUI operation.""" + + # Global registry {"Click": Click, ...} + _registry: ClassVar[Dict[str, Type["Action"]]] = {} + + # --------------------------------------------------------------------- + # Reflection helpers + # --------------------------------------------------------------------- + def __init_subclass__(cls, **kwargs): # noqa: D401 (docstring from base) + # super().__init_subclass__(**kwargs) + Action._registry[cls.__name__] = cls + + # ------------------------------------------------------------------ + # (de)serialisation utilities + # ------------------------------------------------------------------ + def to_dict(self) -> Dict[str, Any]: + """Return a JSON‑serialisable dict with a "type" discriminator.""" + data = {"type": self.__class__.__name__} + for f in fields(self): + val = getattr(self, f.name) + data[f.name] = _enum_to_name(val) + return data + + @classmethod + def from_dict(cls: Type[T_Action], + data: Dict[str, Any]) -> T_Action: # noqa: N802 (cls) + if "type" not in data: + raise ValueError("Missing 'type' key in action dict") + typ = data["type"] + if typ not in cls._registry: + raise ValueError( + f"Unknown action type '{typ}' (registry size={len(cls._registry)})" + ) + target_cls = cls._registry[typ] + + # Convert strings back to Enum instances where needed + kwargs = {} + for f in fields(target_cls): + raw = data.get(f.name) + kwargs[f.name] = _name_to_enum(f.type, raw) + return target_cls(**kwargs) # type: ignore[arg-type] + + +# --------------------------------------------------------------------------- +# Helper functions for Enum <-> str +# --------------------------------------------------------------------------- + + +def _enum_to_name(val: Any) -> Any: + if isinstance(val, Enum): + return val.name + if isinstance(val, tuple): + return list(val) # json‑friendly + if isinstance(val, list): + return [_enum_to_name(v) for v in val] + return val + + +def _name_to_enum(expected_type: Any, raw: Any) -> Any: + """Convert *raw* back to Enum or original type depending on *expected_type*.""" + origin = getattr(expected_type, "__origin__", None) + if origin is list: + sub_type = expected_type.__args__[0] + return [_name_to_enum(sub_type, r) for r in raw] if isinstance( + raw, list) else raw + + if isinstance(expected_type, type) and issubclass(expected_type, Enum): + return expected_type[raw] if isinstance(raw, str) else raw + # Fallback – pass through unchanged + return raw + + +# --------------------------------------------------------------------------- +# Concrete Action subclasses +# --------------------------------------------------------------------------- +@dataclass(slots=True) +class Click(Action): + x: int + y: int + element_description: str + button: int = 0 + holdKey: List[str] = field(default_factory=list) + + +@dataclass(slots=True) +class DoubleClick(Action): + x: int + y: int + element_description: str + button: int = 0 + holdKey: List[str] = field(default_factory=list) + + +@dataclass(slots=True) +class Move(Action): + x: int + y: int + element_description: str + holdKey: List[str] = field(default_factory=list) + + +@dataclass(slots=True) +class Scroll(Action): + x: int + y: int + element_description: str + stepVertical: int | None = None + stepHorizontal: int | None = None + holdKey: List[str] = field(default_factory=list) + + +# TODO Drag是否需要区分左中右键 +@dataclass(slots=True) +class Drag(Action): + startX: int + startY: int + endX: int + endY: int + holdKey: List[str] + starting_description: str + ending_description: str + + +@dataclass(slots=True) +class TypeText(Action): + text: str + x: int | None = None + y: int | None = None + element_description: str | None = None + overwrite: bool = False + enter: bool = False + + +@dataclass(slots=True) +class Hotkey(Action): + keys: List[str] + duration: int | None = None + + +@dataclass(slots=True) +class Wait(Action): + duration: int + + +@dataclass(slots=True) +class Done(Action): + return_value: Any | None = None + + +@dataclass(slots=True) +class Failed(Action): + message: str = '' + + +@dataclass(slots=True) +class Memorize(Action): + information: str + + +@dataclass(slots=True) +class Screenshot(Action): + pass + + +# New actions integrated from Agent-S s2.5 grounding +@dataclass(slots=True) +class SetCellValues(Action): + cell_values: Dict[str, Any] + app_name: str + sheet_name: str + + +@dataclass(slots=True) +class SwitchApplications(Action): + app_code: str + + +@dataclass(slots=True) +class Open(Action): + app_or_filename: str diff --git a/mm_agents/maestro/maestro/Backend/ADBBackend.py b/mm_agents/maestro/maestro/Backend/ADBBackend.py new file mode 100644 index 0000000..b46b274 --- /dev/null +++ b/mm_agents/maestro/maestro/Backend/ADBBackend.py @@ -0,0 +1,167 @@ +# --------------------------------------------------------------------------- +# 2) Android device backend (ADB) +# --------------------------------------------------------------------------- +from ..Action import ( + Action, + Click, + DoubleClick, + Move, + Scroll, + Drag, + TypeText, + Hotkey, + Wait, + Screenshot, + SetCellValues, + SwitchApplications, + Open +) + +from .Backend import Backend +import time +import subprocess + +class ADBBackend(Backend): + """Android ADB backend for mobile automation.""" + + _supported = {Click, DoubleClick, Move, Scroll, Drag, TypeText, Hotkey, Wait, Screenshot, SetCellValues, SwitchApplications, Open} + + def __init__(self, serial: str | None = None): + self.serial = serial # specify target device; None = default + + # ------------------------------------------------------------------ + def execute(self, action: Action) -> None: + if not self.supports(type(action)): + raise NotImplementedError(f"{type(action).__name__} not supported by ADBBackend") + + if isinstance(action, Click): + self._click(action) + elif isinstance(action, DoubleClick): + self._doubleClick(action) + elif isinstance(action, Move): + self._move(action) + elif isinstance(action, Scroll): + self._scroll(action) + elif isinstance(action, Drag): + self._drag(action) + elif isinstance(action, TypeText): + self._type(action) + elif isinstance(action, Hotkey): + self._hotkey(action) + elif isinstance(action, Screenshot): + return self._screenshot() + elif isinstance(action, Wait): + time.sleep(action.duration * 1e-3) + elif isinstance(action, SetCellValues): + self._set_cell_values(action) + elif isinstance(action, SwitchApplications): + self._switch_applications(action) + elif isinstance(action, Open): + self._open(action) + else: + raise NotImplementedError(f"Unhandled action: {action}") + + def _click(self, action: Click) -> None: + prefix = ["adb"] + if self.serial: + prefix += ["-s", self.serial] + prefix.append("shell") + cmd = prefix + ["input", "tap", str(action.x), str(action.y)] + subprocess.run(cmd, check=True) + + def _doubleClick(self, action: DoubleClick) -> None: + prefix = ["adb"] + if self.serial: + prefix += ["-s", self.serial] + prefix.append("shell") + cmd = prefix + ["input", "doubletap", str(action.x), str(action.y)] + subprocess.run(cmd, check=True) + + def _move(self, action: Move) -> None: + prefix = ["adb"] + if self.serial: + prefix += ["-s", self.serial] + prefix.append("shell") + cmd = prefix + ["input", "move", str(action.x), str(action.y)] + subprocess.run(cmd, check=True) + + def _scroll(self, action: Scroll) -> None: + prefix = ["adb"] + if self.serial: + prefix += ["-s", self.serial] + prefix.append("shell") + # For scroll, we'll use swipe with the x,y coordinates + # Scroll amount depends on stepVertical/stepHorizontal + scroll_amount = action.stepVertical or action.stepHorizontal or 3 + if action.stepVertical: + # Vertical scroll + end_x, end_y = action.x, action.y - scroll_amount * 100 + else: + # Horizontal scroll + end_x, end_y = action.x - scroll_amount * 100, action.y + + cmd = prefix + ["input", "swipe", str(action.x), str(action.y), str(end_x), str(end_y), "500"] + subprocess.run(cmd, check=True) + + def _drag(self, action: Drag) -> None: + prefix = ["adb"] + if self.serial: + prefix += ["-s", self.serial] + prefix.append("shell") + cmd = prefix + ["input", "swipe", str(action.startX), str(action.startY), str(action.endX), str(action.endY), "1000"] + subprocess.run(cmd, check=True) + + def _type(self, action: TypeText) -> None: + prefix = ["adb"] + if self.serial: + prefix += ["-s", self.serial] + prefix.append("shell") + text = action.text.replace(" ", "%s") # escape spaces + cmd = prefix + ["input", "text", text] + subprocess.run(cmd, check=True) + + def _hotkey(self, action: Hotkey) -> None: + prefix = ["adb"] + if self.serial: + prefix += ["-s", self.serial] + prefix.append("shell") + # Map first key for demo purposes + key = action.keys[0].upper() + cmd = prefix + ["input", "keyevent", key] + subprocess.run(cmd, check=True) + + def _screenshot(self): + # ADB screenshot implementation + pass + + def _set_cell_values(self, act: SetCellValues) -> None: + """Set cell values in mobile spreadsheet apps via ADB""" + # For mobile apps, we'll use text input to set cell values + # This is a simplified implementation - in practice, you'd need app-specific logic + for cell_ref, value in act.cell_values.items(): + # Navigate to cell and set value + # This would require app-specific implementation + pass + + def _switch_applications(self, act: SwitchApplications) -> None: + """Switch to a different Android application via ADB""" + # Use ADB to switch apps + import subprocess + try: + # Get list of running apps + result = subprocess.run(['adb', 'shell', 'dumpsys', 'activity', 'activities'], + capture_output=True, text=True) + # Parse output to find app and switch to it + # This is a simplified implementation + pass + except subprocess.SubprocessError: + pass + + def _open(self, act: Open) -> None: + """Open an Android application via ADB""" + import subprocess + try: + # Use ADB to launch app + subprocess.run(['adb', 'shell', 'am', 'start', '-n', act.app_or_filename]) + except subprocess.SubprocessError: + pass diff --git a/mm_agents/maestro/maestro/Backend/Backend.py b/mm_agents/maestro/maestro/Backend/Backend.py new file mode 100644 index 0000000..269f151 --- /dev/null +++ b/mm_agents/maestro/maestro/Backend/Backend.py @@ -0,0 +1,28 @@ +# Abstract backend base‑class +# --------------------------------------------------------------------------- +from abc import ABC, abstractmethod +from typing import Any, List, Type, Dict, Set +from ..Action import ( + Action +) + + +class Backend(ABC): + """Abstract base for platform back‑ends.""" + + #: Each backend advertises which Action subclasses it supports. + _supported: Set[Type[Action]] = set() + + # --------------------------------------------------------------------- + def supports(self, action_type: Type[Action]) -> bool: + return action_type in self._supported + + # --------------------------------------------------------------------- + @abstractmethod + def execute(self, action: Action) -> Any: + """Translate an *Action* into concrete commands. + + Should raise **NotImplementedError** if the *action* type is not in + `self._supported`, so upper layers can decide how to degrade / retry. + """ + diff --git a/mm_agents/maestro/maestro/Backend/PyAutoGUIBackend.py b/mm_agents/maestro/maestro/Backend/PyAutoGUIBackend.py new file mode 100644 index 0000000..b249598 --- /dev/null +++ b/mm_agents/maestro/maestro/Backend/PyAutoGUIBackend.py @@ -0,0 +1,467 @@ +# --------------------------------------------------------------------------- +# 1) Desktop automation backend (PyAutoGUI) +# --------------------------------------------------------------------------- +import os +import subprocess, difflib +import sys +import pyperclip +from PIL import Image +from numpy import imag +from ..Action import ( + Action, + Click, + DoubleClick, + Move, + Scroll, + Drag, + TypeText, + Hotkey, + Wait, + Screenshot, + SetCellValues, + SwitchApplications, + Open +) + +from .Backend import Backend +import time + + +class PyAutoGUIBackend(Backend): + """Pure local desktop backend powered by *pyautogui*. + + Pros : zero dependency besides Python & pyautogui. + Cons : Requires an active, visible desktop session (won't work headless). + """ + + _supported = {Click, DoubleClick, Move, Scroll, Drag, TypeText, Hotkey, Wait, Screenshot, SetCellValues, SwitchApplications, Open} + + # ¶ PyAutoGUI sometimes throws exceptions if mouse is moved to a corner. + def __init__(self, default_move_duration: float = 0.0, platform: str | None = None, **kwargs): + import pyautogui as pag # local import to avoid hard requirement + pag.FAILSAFE = False + self.pag = pag + self.default_move_duration = default_move_duration + # ↙️ Critical patch: save platform identifier + self.platform = (platform or sys.platform).lower() + + # ------------------------------------------------------------------ + def execute(self, action: Action) -> None: + if not self.supports(type(action)): + raise NotImplementedError(f"{type(action).__name__} not supported by PyAutoGUIBackend") + + if isinstance(action, Click): + self._click(action) + elif isinstance(action, DoubleClick): + self._doubleClick(action) + elif isinstance(action, Move): + self._move(action) + elif isinstance(action, Scroll): + self._scroll(action) + elif isinstance(action, Drag): + self._drag(action) + elif isinstance(action, TypeText): + self._type(action) + elif isinstance(action, Hotkey): + self._hotkey(action) + elif isinstance(action, Screenshot): + screenshot = self._screenshot() + return screenshot # type: ignore + elif isinstance(action, Wait): + time.sleep(action.duration * 1e-3) + elif isinstance(action, SetCellValues): + self._set_cell_values(action) + elif isinstance(action, SwitchApplications): + self._switch_applications(action) + elif isinstance(action, Open): + self._open(action) + else: + # This shouldn't happen due to supports() check, but be safe. + raise NotImplementedError(f"Unhandled action: {action}") + + # ----- individual helpers ------------------------------------------------ + def _click(self, act: Click) -> None: + for k in act.holdKey or []: + self.pag.keyDown(k) + time.sleep(0.05) + + button_str = 'primary' + if act.button == 1: + button_str = "left" + elif act.button == 4: + button_str = "middle" + elif act.button == 2: + button_str = "right" + + self.pag.click( + x=act.x, + y=act.y, + clicks=1, + button=button_str, # type: ignore + duration=self.default_move_duration, + interval=0.1, + ) + for k in act.holdKey or []: + self.pag.keyUp(k) + + def _doubleClick(self, act: DoubleClick) -> None: + for k in act.holdKey or []: + self.pag.keyDown(k) + time.sleep(0.05) + button_str = 'primary' + if act.button == 1: + button_str = "left" + elif act.button == 4: + button_str = "middle" + elif act.button == 2: + button_str = "right" + + self.pag.click( + x=act.x, + y=act.y, + clicks=2, + button=button_str, + duration=self.default_move_duration, + interval=0.1, + ) + for k in act.holdKey or []: + self.pag.keyUp(k) + + def _move(self, act: Move) -> None: + for k in act.holdKey or []: + self.pag.keyDown(k) + time.sleep(0.05) + self.pag.moveTo(x = act.x, y = act.y) + for k in act.holdKey or []: + self.pag.keyUp(k) + + def _scroll(self, act: Scroll) -> None: + for k in act.holdKey or []: + self.pag.keyDown(k) + time.sleep(0.05) + + self.pag.moveTo(x = act.x, y = act.y) + if act.stepVertical is None: + if act.stepHorizontal is not None: + self.pag.hscroll(act.stepHorizontal) + else: + self.pag.vscroll(act.stepVertical) + + for k in act.holdKey or []: + self.pag.keyUp(k) + + def _drag(self, act: Drag) -> None: + for k in act.holdKey or []: + self.pag.keyDown(k) + time.sleep(0.05) + + self.pag.moveTo(x=act.startX, y=act.startY) + time.sleep(0.1) + + self.pag.mouseDown(button='left') + time.sleep(0.2) + + self.pag.moveTo(x=act.endX, y=act.endY, duration=0.5) + time.sleep(0.1) + + self.pag.mouseUp(button='left') + + for k in act.holdKey or []: + self.pag.keyUp(k) + + def _type(self, act: TypeText) -> None: + # 1) Optional focus by clicking target element center + if act.x is not None and act.y is not None: + self.pag.click(x=act.x, y=act.y) + time.sleep(0.05) + + # 2) Optional overwrite: select-all + backspace + if act.overwrite: + if self.platform.startswith("darwin"): + self.pag.hotkey("command", "a", interval=0.2) + else: + self.pag.hotkey("ctrl", "a", interval=0.2) + time.sleep(0.05) + self.pag.press("backspace") + time.sleep(0.05) + + # 3) Type text (keep clipboard-paste for robust i18n like Chinese) + pyperclip.copy(act.text) + time.sleep(0.05) # let clipboard stabilize + if self.platform.startswith("darwin"): + subprocess.run([ + "osascript", "-e", + 'tell application "System Events" to keystroke "v" using command down' + ]) + else: # Windows / Linux + self.pag.hotkey("ctrl", "v", interval=0.05) + + # 4) Optional press enter + if act.enter: + key = "return" if self.platform.startswith("darwin") else "enter" + self.pag.press(key) + + def _hotkey(self, act: Hotkey) -> None: + # self.pag.hotkey(*act.keys, interval=0.1) + if act.duration is not None: + for k in act.keys or []: + self.pag.keyDown(k) + time.sleep(act.duration * 1e-3) + # time.sleep(act.duration * 1e-3) + for k in reversed(act.keys): + self.pag.keyUp(k) + else: + self.pag.hotkey(*act.keys, interval=0.1) + + def _screenshot(self): + screenshot = self.pag.screenshot() + return screenshot + + def _set_cell_values(self, act: SetCellValues) -> None: + """Set cell values in LibreOffice Calc (Linux only)""" + if self.platform.startswith("linux"): + # Use subprocess to execute LibreOffice automation + import subprocess + import tempfile + import os + + # Create a temporary Python script with the cell values + script_content = f""" +import uno +import subprocess + +def set_cell_values(new_cell_values, app_name, sheet_name): + # Clean up previous TCP connections + subprocess.run('echo "osworld-public-evaluation" | sudo -S ss --kill --tcp state TIME-WAIT sport = :2002', + shell=True, check=True, text=True, capture_output=True) + + # Start LibreOffice with socket connection + subprocess.run(['soffice', '--accept=socket,host=localhost,port=2002;urp;StarOffice.Service']) + + local_context = uno.getComponentContext() + resolver = local_context.ServiceManager.createInstanceWithContext( + "com.sun.star.bridge.UnoUrlResolver", local_context + ) + context = resolver.resolve("uno:socket,host=localhost,port=2002;urp;StarOffice.ComponentContext") + desktop = context.ServiceManager.createInstanceWithContext( + "com.sun.star.frame.Desktop", context + ) + + # Find the spreadsheet and set cell values + for component in desktop.Components: + if component.supportsService("com.sun.star.sheet.SpreadsheetDocument"): + if component.Title == app_name: + sheet = component.Sheets.getByName(sheet_name) + for cell_ref, value in new_cell_values.items(): + # Convert cell reference to column/row indices + col_letters = ''.join(filter(str.isalpha, cell_ref)) + row_number = ''.join(filter(str.isdigit, cell_ref)) + + col = sum((ord(char.upper()) - ord('A') + 1) * (26**idx) for idx, char in enumerate(reversed(col_letters))) - 1 + row = int(row_number) - 1 + + cell = sheet.getCellByPosition(col, row) + if isinstance(value, (int, float)): + cell.Value = value + elif isinstance(value, str): + if value.startswith("="): + cell.Formula = value + else: + cell.String = value + elif isinstance(value, bool): + cell.Value = 1 if value else 0 + break + +set_cell_values({act.cell_values}, "{act.app_name}", "{act.sheet_name}") +""" + + with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f: + f.write(script_content) + temp_script = f.name + + try: + subprocess.run(['python3', temp_script], check=True) + finally: + os.unlink(temp_script) + else: + raise NotImplementedError(f"SetCellValues not supported on platform: {self.platform}") + + def _switch_applications(self, act: SwitchApplications) -> None: + """Switch to a different application that is already open""" + if self.platform.startswith("darwin"): + # macOS: Command+Space to open Spotlight, then type app name + self.pag.hotkey("command", "space", interval=0.2) + time.sleep(0.5) + self.pag.typewrite(act.app_code) + time.sleep(1.0) + self.pag.press("enter") + time.sleep(1.0) + elif self.platform.startswith("linux"): + # Linux: Use wmctrl to switch windows with improved matching + import subprocess + import difflib + + def _normalize(s): + return "".join(ch.lower() for ch in s if ch.isalnum()) + + def _parse_app_code(app_code): + if ":" in app_code: + cls, ttl = app_code.split(":", 1) + return cls.strip(), ttl.strip() + return app_code.strip(), None + + # Extended app mapping with common variations + APP_CLASS_MAP = { + "chrome": ["google-chrome.Google-chrome", "chromium.Chromium"], + "chromium": ["chromium.Chromium"], + "code": ["code.Code"], + "vscode": ["code.Code"], + "visual": ["code.Code"], + "studio": ["code.Code"], + "impress": ["libreoffice.libreoffice-impress"], + "calc": ["libreoffice.libreoffice-calc"], + "libreoffice": ["libreoffice.libreoffice-calc", "libreoffice.libreoffice-impress"], + "office": ["libreoffice.libreoffice-calc", "libreoffice.libreoffice-impress"], + "terminal": ["gnome-terminal-server.Gnome-terminal"], + "gnome-terminal": ["gnome-terminal-server.Gnome-terminal"], + "nautilus": ["org.gnome.Nautilus.Org.gnome.Nautilus"], + "files": ["org.gnome.Nautilus.Org.gnome.Nautilus"], + "filemanager": ["org.gnome.Nautilus.Org.gnome.Nautilus"], + "software": ["org.gnome.Software.Org.gnome.Software"], + "firefox": ["firefox.Firefox"], + "browser": ["firefox.Firefox", "google-chrome.Google-chrome", "chromium.Chromium"], + } + + def _match_by_class(entries, cls_key): + cls_key_n = _normalize(cls_key) + # 1) Alias exact match + if cls_key_n in APP_CLASS_MAP: + targets = {_normalize(x) for x in APP_CLASS_MAP[cls_key_n]} + exact_alias = [e for e in entries if _normalize(e[1]) in targets] + if exact_alias: + return exact_alias + # 2) Exact match + exact = [e for e in entries if _normalize(e[1]) == cls_key_n] + if exact: + return exact + # 3) Prefix/contains + pref = [e for e in entries if _normalize(e[1]).startswith(cls_key_n)] + if pref: + return pref + sub = [e for e in entries if cls_key_n in _normalize(e[1])] + if sub: + return sub + # 4) Fuzzy fallback (higher threshold) + wm_classes = [e[1] for e in entries] + matches = difflib.get_close_matches(cls_key, wm_classes, n=3, cutoff=0.6) + if matches: + chosen = matches[0] + return [e for e in entries if e[1] == chosen] + return [] + + def _match_by_title(candidates, title_key): + ttl_n = _normalize(title_key) + # Exact + exact = [e for e in candidates if _normalize(e[2]) == ttl_n] + if exact: + return exact[0] + # Contains + sub = [e for e in candidates if ttl_n in _normalize(e[2])] + if sub: + return sub[0] + # Fuzzy + titles = [e[2] for e in candidates] + matches = difflib.get_close_matches(title_key, titles, n=1, cutoff=0.6) + if matches: + chosen = matches[0] + for e in candidates: + if e[2] == chosen: + return e + return None + + try: + self.pag.press("escape") + time.sleep(0.5) + + output = subprocess.check_output(['wmctrl', '-lx']).decode('utf-8').splitlines() + + # Parse entries: (window_id, wm_class, title, raw_line) + entries = [] + for raw in output: + if not raw or not raw.strip(): + continue + parts = raw.split(None, 4) + if len(parts) < 3: + continue + window_id = parts[0] + wm_class = parts[2] + title = parts[4] if len(parts) >= 5 else "" + entries.append((window_id, wm_class, title, raw)) + + if not entries: + return # No valid entries found + + # Match by class first, then by title if multiple candidates + cls_key, title_key = _parse_app_code(act.app_code) + candidates = _match_by_class(entries, cls_key) + + if not candidates: + return # No class match found + + chosen_entry = None + if len(candidates) == 1 or not title_key: + chosen_entry = candidates[0] + else: + chosen_entry = _match_by_title(candidates, title_key) + if chosen_entry is None: + # Fallback to first candidate + chosen_entry = candidates[0] + + window_id, wm_class, title, raw = chosen_entry + + # Activate and maximize + subprocess.run(['wmctrl', '-ia', window_id]) + subprocess.run(['wmctrl', '-ir', window_id, '-b', 'add,maximized_vert,maximized_horz']) + + except (subprocess.SubprocessError, IndexError, Exception): + # Fallback to Alt+Tab if wmctrl fails + self.pag.hotkey("alt", "tab") + elif self.platform.startswith("win"): + # Windows: Win+D to show desktop, then type app name + self.pag.hotkey("win", "d", interval=0.1) + time.sleep(0.5) + self.pag.typewrite(act.app_code) + time.sleep(1.0) + self.pag.press("enter") + time.sleep(1.0) + else: + raise NotImplementedError(f"SwitchApplications not supported on platform: {self.platform}") + + def _open(self, act: Open) -> None: + """Open an application or file""" + if self.platform.startswith("darwin"): + # macOS: Command+Space to open Spotlight + self.pag.hotkey("command", "space", interval=0.2) + time.sleep(0.5) + self.pag.typewrite(act.app_or_filename) + time.sleep(1.0) + self.pag.press("enter") + time.sleep(0.5) + elif self.platform.startswith("linux"): + # Linux: Win key to open application menu + self.pag.press("super") + time.sleep(0.5) + self.pag.typewrite(act.app_or_filename) + time.sleep(1.0) + self.pag.press("enter") + time.sleep(0.5) + elif self.platform.startswith("win"): + # Windows: Win+R to open Run dialog + self.pag.hotkey("win", "r", interval=0.1) + time.sleep(0.5) + self.pag.typewrite(act.app_or_filename) + time.sleep(1.0) + self.pag.press("enter") + time.sleep(1.0) + else: + raise NotImplementedError(f"Open not supported on platform: {self.platform}") \ No newline at end of file diff --git a/mm_agents/maestro/maestro/Backend/PyAutoGUIVMwareBackend.py b/mm_agents/maestro/maestro/Backend/PyAutoGUIVMwareBackend.py new file mode 100644 index 0000000..32d47e1 --- /dev/null +++ b/mm_agents/maestro/maestro/Backend/PyAutoGUIVMwareBackend.py @@ -0,0 +1,570 @@ +# --------------------------------------------------------------------------- +# 1) Desktop automation backend (PyAutoGUI) +# --------------------------------------------------------------------------- +import os +import io +from typing import Optional +from desktop_env.desktop_env import DesktopEnv +from ..Action import ( + Action, + Click, + DoubleClick, + Move, + Scroll, + Drag, + TypeText, + Hotkey, + Wait, + Done, + Failed, + Screenshot, + SetCellValues, + SwitchApplications, + Open +) +from ...utils.common_utils import screenshot_bytes_to_pil_image +from .Backend import Backend +import time + +class PyAutoGUIVMwareBackend(Backend): + """VMware desktop backend powered by *pyautogui*. + + Pros : zero dependency besides Python & pyautogui. + Cons : Requires an active, visible desktop session (won't work headless). + """ + + _supported = {Click, DoubleClick, Move, Scroll, Drag, TypeText, Hotkey, Wait, Done, Failed, Screenshot, SetCellValues, SwitchApplications, Open} + + # ¶ PyAutoGUI sometimes throws exceptions if mouse is moved to a corner. + def __init__(self, default_move_duration: float = 0.0, platform: str | None = None, **kwargs): + import pyautogui as pag # local import to avoid hard requirement + pag.FAILSAFE = False + self.pag = pag + self.default_move_duration = default_move_duration + # Extract env_controller from kwargs if provided, but don't require it + self.env_controller: Optional[DesktopEnv] = kwargs.get('env_controller', None) + self.platform = platform + + + # ------------------------------------------------------------------ + def execute(self, action: Action) -> str: # type: ignore + if not self.supports(type(action)): + raise NotImplementedError(f"{type(action).__name__} not supported by PyAutoGUIBackend") + + # For automation OSWorld evaluation + if self.env_controller is None: + if isinstance(action, Click): + return self._click(action) + elif isinstance(action, DoubleClick): + return self._doubleClick(action) + elif isinstance(action, Move): + return self._move(action) + elif isinstance(action, Scroll): + return self._scroll(action) + elif isinstance(action, Drag): + return self._drag(action) + elif isinstance(action, TypeText): + return self._type(action) + elif isinstance(action, Hotkey): + return self._hotkey(action) + elif isinstance(action, Screenshot): + screenshot = self._screenshot() + return screenshot # type: ignore + elif isinstance(action, Wait): + return f"WAIT" + elif isinstance(action, Done): + return f"DONE" + elif isinstance(action, Failed): + return f"FAIL" + elif isinstance(action, SetCellValues): + return self._set_cell_values(action) + elif isinstance(action, SwitchApplications): + return self._switch_applications(action) + elif isinstance(action, Open): + return self._open(action) + else: + # This shouldn't happen due to supports() check, but be safe. + raise NotImplementedError(f"Unhandled action: {action}") + + # For cli_app + else: + if isinstance(action, Click): + action_pyautogui_code = self._click(action) + elif isinstance(action, DoubleClick): + action_pyautogui_code = self._doubleClick(action) + elif isinstance(action, Move): + action_pyautogui_code = self._move(action) + elif isinstance(action, Scroll): + action_pyautogui_code = self._scroll(action) + elif isinstance(action, Drag): + action_pyautogui_code = self._drag(action) + elif isinstance(action, TypeText): + action_pyautogui_code = self._type(action) + elif isinstance(action, Hotkey): + action_pyautogui_code = self._hotkey(action) + elif isinstance(action, Screenshot): + screenshot = self._screenshot() + return screenshot # type: ignore + elif isinstance(action, Wait): + action_pyautogui_code = f"WAIT" + elif isinstance(action, Done): + action_pyautogui_code = f"DONE" + elif isinstance(action, Failed): + action_pyautogui_code = f"FAIL" + elif isinstance(action, SetCellValues): + action_pyautogui_code = self._set_cell_values(action) + elif isinstance(action, SwitchApplications): + action_pyautogui_code = self._switch_applications(action) + elif isinstance(action, Open): + action_pyautogui_code = self._open(action) + else: + # This shouldn't happen due to supports() check, but be safe. + raise NotImplementedError(f"Unhandled action: {action}") + + pause = 2 if action_pyautogui_code != "WAIT" else action.duration * 1e-3 + self.env_controller.step(action_pyautogui_code, pause) + + # ----- individual helpers ------------------------------------------------ + def _click(self, act: Click) -> str: + button_str = 'primary' + if act.button == 1: + button_str = "left" + elif act.button == 4: + button_str = "middle" + elif act.button == 2: + button_str = "right" + + hold_keys = act.holdKey or [] + code_parts = [] + for k in hold_keys: + code_parts.append(f"pyautogui.keyDown('{k}')") + code_parts.append(f"time.sleep(0.05)") + code_parts.append(f"pyautogui.click(x={act.x}, y={act.y}, clicks=1, button='{button_str}', duration={self.default_move_duration}, interval=0.1)") + for k in hold_keys: + code_parts.append(f"pyautogui.keyUp('{k}')") + return "; ".join(code_parts) + + def _doubleClick(self, act: DoubleClick) -> str: + + button_str = 'primary' + if act.button == 1: + button_str = "left" + elif act.button == 4: + button_str = "middle" + elif act.button == 2: + button_str = "right" + + + hold_keys = act.holdKey or [] + code_parts = [] + for k in hold_keys: + code_parts.append(f"pyautogui.keyDown('{k}')") + code_parts.append(f"time.sleep(0.05)") + code_parts.append(f"pyautogui.click(x={act.x}, y={act.y}, clicks=2, button='{button_str}', duration={self.default_move_duration}, interval=0.1)") + for k in hold_keys: + code_parts.append(f"pyautogui.keyUp('{k}')") + return "; ".join(code_parts) + + def _move(self, act: Move) -> str: + code_parts = [] + for k in act.holdKey or []: + code_parts.append(f"pyautogui.keyDown('{k}')") + code_parts.append(f"time.sleep(0.05)") + code_parts.append(f"pyautogui.moveTo(x = {act.x}, y = {act.y})") + for k in act.holdKey or []: + code_parts.append(f"pyautogui.keyUp('{k}')") + return "; ".join(code_parts) + + def _scroll(self, act: Scroll) -> str: + code_parts = [] + + for k in act.holdKey or []: + code_parts.append(f"pyautogui.keyDown('{k}')") + code_parts.append(f"time.sleep(0.05)") + + code_parts.append(f"pyautogui.moveTo(x = {act.x}, y = {act.y})") + if act.stepVertical is None: + if act.stepHorizontal is not None: + code_parts.append(f"pyautogui.hscroll({act.stepHorizontal})") + else: + code_parts.append(f"pyautogui.vscroll({act.stepVertical})") + + for k in act.holdKey or []: + code_parts.append(f"pyautogui.keyUp('{k}')") + + return "; ".join(code_parts) + + def _drag(self, act: Drag) -> str: + hold_keys = act.holdKey or [] + code_parts = [] + for k in hold_keys: + code_parts.append(f"pyautogui.keyDown('{k}')") + code_parts.append(f"time.sleep(0.05)") + + code_parts.append(f"pyautogui.moveTo(x = {act.startX}, y = {act.startY})") + code_parts.append("time.sleep(0.1)") + + code_parts.append(f"pyautogui.mouseDown(button='left')") + code_parts.append("time.sleep(0.2)") + + code_parts.append(f"pyautogui.moveTo(x = {act.endX}, y = {act.endY}, duration=0.5)") + code_parts.append("time.sleep(0.1)") + + code_parts.append(f"pyautogui.mouseUp(button='left')") + + for k in hold_keys: + code_parts.append(f"pyautogui.keyUp('{k}')") + return "; ".join(code_parts) + + def _type(self, act: TypeText) -> str: + code_parts = [] + # 1) Optional focus + if act.x is not None and act.y is not None: + code_parts.append(f"pyautogui.click({act.x}, {act.y}, clicks=2, interval=0.1)") + code_parts.append("time.sleep(0.05)") + # 2) Optional overwrite + if act.overwrite: + code_parts.append("pyautogui.hotkey('ctrl', 'a', interval=0.2)") + code_parts.append("time.sleep(0.05)") + code_parts.append("pyautogui.press('backspace')") + code_parts.append("time.sleep(0.05)") + # 3) Type text by write in VMware codegen path + code_parts.append("pyautogui.write(" + repr(act.text) + ")") + # 4) Optional enter + if act.enter: + code_parts.append("pyautogui.press('enter')") + return "; ".join(code_parts) + + def _hotkey(self, act: Hotkey) -> str: + code_parts = [] + if act.duration is not None: + for k in act.keys or []: + code_parts.append(f"pyautogui.keyDown('{k}')") + code_parts.append(f"time.sleep({act.duration} * 1e-3)") + for k in reversed(act.keys): + code_parts.append(f"pyautogui.keyUp('{k}')") + else: + keys_str = "', '".join(act.keys) + code_parts.append(f"pyautogui.hotkey('{keys_str}', interval=0.1)") + return "; ".join(code_parts) + + def _screenshot(self) -> str: + if self.env_controller is None: + return "screenshot = pyautogui.screenshot(); return screenshot" + else: + obs = self.env_controller._get_obs() + return screenshot_bytes_to_pil_image(obs["screenshot"]) #type: ignore + + def _set_cell_values(self, act: SetCellValues) -> str: + """Set cell values in LibreOffice Calc (Linux only)""" + if self.platform == "Ubuntu": + # Create Python script for LibreOffice automation + script_content = f""" +import uno +import subprocess + +def identify_document_type(component): + if component.supportsService("com.sun.star.sheet.SpreadsheetDocument"): + return "Calc" + + if component.supportsService("com.sun.star.text.TextDocument"): + return "Writer" + + if component.supportsService("com.sun.star.sheet.PresentationDocument"): + return "Impress" + + return None + +def cell_ref_to_indices(cell_ref): + column_letters = ''.join(filter(str.isalpha, cell_ref)) + row_number = ''.join(filter(str.isdigit, cell_ref)) + + col = sum((ord(char.upper()) - ord('A') + 1) * (26**idx) for idx, char in enumerate(reversed(column_letters))) - 1 + row = int(row_number) - 1 + return col, row + +def set_cell_values(new_cell_values: dict[str, str], app_name: str = "Untitled 1", sheet_name: str = "Sheet1"): + new_cell_values_idx = {{}} + for k, v in new_cell_values.items(): + try: + col, row = cell_ref_to_indices(k) + except: + col = row = None + + if col is not None and row is not None: + new_cell_values_idx[(col, row)] = v + + # Clean up previous TCP connections. + # The command may fail if no such connections exist, so we don't check for exit code. + subprocess.run( + 'echo \"osworld-public-evaluation\" | sudo -S ss --kill --tcp state TIME-WAIT sport = :2002', + shell=True, + check=False, + text=True, + capture_output=True + ) + + # Dynamically allow soffice to listen on port 2002. + # Use Popen to run soffice in the background. + soffice_process = subprocess.Popen( + [ + "soffice", + "--headless", + "--invisible", + "--accept=socket,host=localhost,port=2002;urp;StarOffice.Service" + ] + ) + + # Wait for soffice to start + import time + time.sleep(3) + + try: + local_context = uno.getComponentContext() + resolver = local_context.ServiceManager.createInstanceWithContext( + "com.sun.star.bridge.UnoUrlResolver", local_context + ) + context = resolver.resolve( + f"uno:socket,host=localhost,port=2002;urp;StarOffice.ComponentContext" + ) + desktop = context.ServiceManager.createInstanceWithContext( + "com.sun.star.frame.Desktop", context + ) + + # Collect all LibreOffice-related opened windows. + documents = [] + for i, component in enumerate(desktop.Components): + title = component.Title + doc_type = identify_document_type(component) + documents.append((i, component, title, doc_type)) + + # Find the LibreOffice Calc app and the sheet of interest. + spreadsheet = [doc for doc in documents if doc[3] == "Calc"] + selected_spreadsheet = [doc for doc in spreadsheet if doc[2] == app_name] + if spreadsheet: + try: + if selected_spreadsheet: + spreadsheet = selected_spreadsheet[0][1] + else: + spreadsheet = spreadsheet[0][1] + + sheet = spreadsheet.Sheets.getByName(sheet_name) + except: + raise ValueError(f"Could not find sheet {{sheet_name}} in {{app_name}}.") + + for (col, row), value in new_cell_values_idx.items(): + cell = sheet.getCellByPosition(col, row) + + # Set the cell value. + if isinstance(value, (int, float)): + cell.Value = value + elif isinstance(value, str): + if value.startswith("="): + cell.Formula = value + else: + cell.String = value + elif isinstance(value, bool): + cell.Value = 1 if value else 0 + elif value is None: + cell.clearContents(0) + else: + raise ValueError(f"Unsupported cell value type: {{type(value)}}") + + else: + raise ValueError(f"Could not find LibreOffice Calc app corresponding to {{app_name}}.") + finally: + # Terminate the soffice process + soffice_process.terminate() + soffice_process.wait() + +set_cell_values(new_cell_values={act.cell_values}, app_name="{act.app_name}", sheet_name="{act.sheet_name}") +""" + return script_content + else: + return f"# SetCellValues not supported on platform: {self.platform}" # type: ignore + + def _switch_applications(self, act: SwitchApplications) -> str: + """Switch to a different application that is already open""" + if self.platform == "Ubuntu": + # Linux: Use wmctrl to switch windows with improved matching + return f""" +import subprocess +import difflib +import pyautogui +import time + +def _normalize(s): + return "".join(ch.lower() for ch in s if ch.isalnum()) + +def _parse_app_code(app_code): + if ":" in app_code: + cls, ttl = app_code.split(":", 1) + return cls.strip(), ttl.strip() + return app_code.strip(), None + +# Extended app mapping with common variations +APP_CLASS_MAP = {{ + "chrome": ["google-chrome.Google-chrome", "chromium.Chromium"], + "chromium": ["chromium.Chromium"], + "code": ["code.Code"], + "vscode": ["code.Code"], + "visual": ["code.Code"], + "studio": ["code.Code"], + "impress": ["libreoffice.libreoffice-impress"], + "calc": ["libreoffice.libreoffice-calc"], + "libreoffice": ["libreoffice.libreoffice-calc", "libreoffice.libreoffice-impress"], + "office": ["libreoffice.libreoffice-calc", "libreoffice.libreoffice-impress"], + "terminal": ["gnome-terminal-server.Gnome-terminal"], + "gnome-terminal": ["gnome-terminal-server.Gnome-terminal"], + "nautilus": ["org.gnome.Nautilus.Org.gnome.Nautilus"], + "files": ["org.gnome.Nautilus.Org.gnome.Nautilus"], + "filemanager": ["org.gnome.Nautilus.Org.gnome.Nautilus"], + "software": ["org.gnome.Software.Org.gnome.Software"], + "firefox": ["firefox.Firefox"], + "browser": ["firefox.Firefox", "google-chrome.Google-chrome", "chromium.Chromium"], +}} + +def _match_by_class(entries, cls_key): + cls_key_n = _normalize(cls_key) + # 1) Alias exact match + if cls_key_n in APP_CLASS_MAP: + targets = {{_normalize(x) for x in APP_CLASS_MAP[cls_key_n]}} + exact_alias = [e for e in entries if _normalize(e[1]) in targets] + if exact_alias: + return exact_alias + # 2) Exact match + exact = [e for e in entries if _normalize(e[1]) == cls_key_n] + if exact: + return exact + # 3) Prefix/contains + pref = [e for e in entries if _normalize(e[1]).startswith(cls_key_n)] + if pref: + return pref + sub = [e for e in entries if cls_key_n in _normalize(e[1])] + if sub: + return sub + # 4) Fuzzy fallback (higher threshold) + wm_classes = [e[1] for e in entries] + matches = difflib.get_close_matches(cls_key, wm_classes, n=3, cutoff=0.6) + if matches: + chosen = matches[0] + return [e for e in entries if e[1] == chosen] + return [] + +def _match_by_title(candidates, title_key): + ttl_n = _normalize(title_key) + # Exact + exact = [e for e in candidates if _normalize(e[2]) == ttl_n] + if exact: + return exact[0] + # Contains + sub = [e for e in candidates if ttl_n in _normalize(e[2])] + if sub: + return sub[0] + # Fuzzy + titles = [e[2] for e in candidates] + matches = difflib.get_close_matches(title_key, titles, n=1, cutoff=0.6) + if matches: + chosen = matches[0] + for e in candidates: + if e[2] == chosen: + return e + return None + +try: + pyautogui.press('escape') + time.sleep(0.5) + + output = subprocess.check_output(['wmctrl', '-lx']) + output = output.decode('utf-8').splitlines() + + # Parse entries: (window_id, wm_class, title, raw_line) + entries = [] + for raw in output: + if not raw or not raw.strip(): + continue + parts = raw.split(None, 4) + if len(parts) < 3: + continue + window_id = parts[0] + wm_class = parts[2] + title = parts[4] if len(parts) >= 5 else "" + entries.append((window_id, wm_class, title, raw)) + + if not entries: + print("[SwitchApplications] no valid entries found") + exit(0) + + # Match by class first, then by title if multiple candidates + cls_key, title_key = _parse_app_code('{act.app_code}') + candidates = _match_by_class(entries, cls_key) + + if not candidates: + print(f"[SwitchApplications] no class match for '{{cls_key}}'") + exit(0) + + chosen_entry = None + if len(candidates) == 1 or not title_key: + chosen_entry = candidates[0] + else: + chosen_entry = _match_by_title(candidates, title_key) + if chosen_entry is None: + # Fallback to first candidate + chosen_entry = candidates[0] + + window_id, wm_class, title, raw = chosen_entry + print(f"[SwitchApplications] selected: {{wm_class}} - {{title}}") + + # Activate and maximize + subprocess.run(['wmctrl', '-ia', window_id]) + subprocess.run(['wmctrl', '-ir', window_id, '-b', 'add,maximized_vert,maximized_horz']) + +except Exception as e: + print(f"[SwitchApplications] exception: {{e}}", flush=True) +""" + elif self.platform == "Windows": + # Windows: Win+D to show desktop, then type app name + return f""" +import pyautogui +import time + +pyautogui.hotkey('win', 'd', interval=0.1) +time.sleep(0.5) +pyautogui.typewrite(""" + repr(act.app_code) + """) +time.sleep(1.0) +pyautogui.press('enter') +time.sleep(1.0) +""" + else: + return f"# SwitchApplications not supported on platform: {self.platform}" + + def _open(self, act: Open) -> str: + """Open an application or file""" + if self.platform == "Ubuntu": + # Linux: Win key to open application menu + return f""" +import pyautogui +import time + +pyautogui.press('win') +time.sleep(0.5) +pyautogui.write(""" + repr(act.app_or_filename) + """) +time.sleep(1.0) +pyautogui.hotkey('enter') +time.sleep(0.5) +""" + elif self.platform == "Windows": + # Windows: Win+R to open Run dialog + return f""" +import pyautogui +import time + +pyautogui.hotkey('win', 'r', interval=0.1) +time.sleep(0.5) +pyautogui.typewrite(""" + repr(act.app_or_filename) + """) +time.sleep(1.0) +pyautogui.press('enter') +time.sleep(1.0) +""" + else: + return f"# Open not supported on platform: {self.platform}" diff --git a/mm_agents/maestro/maestro/__init__.py b/mm_agents/maestro/maestro/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mm_agents/maestro/maestro/controller/__init__.py b/mm_agents/maestro/maestro/controller/__init__.py new file mode 100644 index 0000000..41df020 --- /dev/null +++ b/mm_agents/maestro/maestro/controller/__init__.py @@ -0,0 +1,20 @@ +""" +Controller package for Maestro +""" + +from .config_manager import ConfigManager +from .rule_engine import RuleEngine +from .state_handlers import StateHandlers +from .state_machine import StateMachine +from .main_controller import MainController + +NewController = MainController + +__all__ = [ + 'ConfigManager', + 'RuleEngine', + 'StateHandlers', + 'StateMachine', + 'MainController', + 'NewController' +] \ No newline at end of file diff --git a/mm_agents/maestro/maestro/controller/config_manager.py b/mm_agents/maestro/maestro/controller/config_manager.py new file mode 100644 index 0000000..4437fd6 --- /dev/null +++ b/mm_agents/maestro/maestro/controller/config_manager.py @@ -0,0 +1,120 @@ +""" +Configuration Manager for Maestro Controller +Responsible for tool configuration and knowledge base setup +""" + +import os +import json +import logging +from typing import Dict, Any + +logger = logging.getLogger(__name__) + + +class ConfigManager: + """Configuration manager responsible for tool configuration and knowledge base setup""" + + def __init__(self, + memory_root_path: str = os.getcwd(), + memory_folder_name: str = "kb_s2", + kb_release_tag: str = "v0.2.2", + ): + self.memory_root_path = memory_root_path + self.memory_folder_name = memory_folder_name + self.tools_config = {} + self.tools_dict = {} + self.flow_config: Dict[str, Any] = {} + + def load_tools_configuration(self) -> Dict[str, Any]: + """Load tool configuration from configuration file""" + try: + tools_config_path = os.path.join( + os.path.dirname(os.path.dirname(os.path.dirname(__file__))), + "tools", "new_tools_config.json" + ) + + with open(tools_config_path, "r") as f: + self.tools_config = json.load(f) + logger.info(f"Loaded tools configuration from: {tools_config_path}") + + # Build tool dictionary, preserving all configuration fields + for tool in self.tools_config["tools"]: + tool_name = tool["tool_name"] + # Copy all configuration fields, not just provider and model + self.tools_dict[tool_name] = tool.copy() + # Ensure model field exists (mapped from model_name) + if "model_name" in tool: + self.tools_dict[tool_name]["model"] = tool["model_name"] + + logger.debug(f"Tools configuration loaded: {len(self.tools_dict)} tools") + return self.tools_dict + + except Exception as e: + logger.error(f"Failed to load tools configuration: {e}") + return {} + + def setup_knowledge_base(self, platform: str) -> str: + """Initialize agent's knowledge base path and check if it exists""" + try: + # Initialize agent's knowledge base path + local_kb_path = os.path.join(self.memory_root_path, self.memory_folder_name) + + # Check if knowledge base exists + kb_platform_path = os.path.join(local_kb_path, platform) + if not os.path.exists(kb_platform_path): + logger.warning(f"Knowledge base for {platform} platform not found in {local_kb_path}") + os.makedirs(kb_platform_path, exist_ok=True) + logger.info(f"Created directory: {kb_platform_path}") + else: + logger.info(f"Found local knowledge base path: {kb_platform_path}") + + return local_kb_path + + except Exception as e: + logger.error(f"Failed to setup knowledge base: {e}") + return self.memory_root_path + + def get_tools_dict(self) -> Dict[str, Any]: + """Get tools dictionary""" + return self.tools_dict + + def get_tools_config(self) -> Dict[str, Any]: + """Get tools configuration""" + return self.tools_config + + # ===== New: Centralized flow configuration management ===== + def load_flow_configuration(self) -> Dict[str, Any]: + """Load flow configuration (currently mainly built-in defaults, can be extended to files/environment variables later).""" + try: + # Unified default threshold configuration + self.flow_config = { + # Task and state + "max_state_switches": 1000, # default: 500 + # "max_state_duration_secs": 300, # default: 300 + # Quality check related + # "quality_check_interval_secs": 300, # Time interval since last quality check default: 300 + "first_quality_check_min_commands": 3, # Number of commands to trigger first quality check default: 5 + # Consecutive same behavior and replanning + # "repeated_action_min_consecutive": 3, # default: 3 + # "replan_long_execution_threshold": 15, # default: 15 + # Planning count limit + # "plan_number_limit": 50, # default: 50 + # Snapshots and main loop + # "enable_snapshots": True, + "enable_snapshots": False, + "snapshot_interval_steps": 10, + # "create_checkpoint_snapshots": True, + "create_checkpoint_snapshots": False, + "main_loop_sleep_secs": 0.1, + } + except Exception as e: + logger.error(f"Failed to load flow configuration: {e}") + # Fallback: at least provide an empty dictionary + self.flow_config = {} + return self.flow_config + + def get_flow_config(self) -> Dict[str, Any]: + """Get flow configuration (load with defaults if not loaded).""" + if not self.flow_config: + return self.load_flow_configuration() + return self.flow_config \ No newline at end of file diff --git a/mm_agents/maestro/maestro/controller/main_controller.py b/mm_agents/maestro/maestro/controller/main_controller.py new file mode 100644 index 0000000..bb66026 --- /dev/null +++ b/mm_agents/maestro/maestro/controller/main_controller.py @@ -0,0 +1,579 @@ +""" +Main Controller for Maestro +Integrates all modules and provides a unified interface +""" + +import time +import os +import logging +from datetime import datetime +from typing import Dict, Any, Optional, List +import platform + +from ..Action import Screenshot + +from ..data_models import SubtaskData, TaskData +from desktop_env.desktop_env import DesktopEnv +from ..hardware_interface import HardwareInterface +from PIL import Image + +from ..utils.screenShot import scale_screenshot_dimensions +from ...store.registry import Registry + +from ..new_global_state import NewGlobalState +from ..new_manager import NewManager +from ..new_executor import NewExecutor +from ..enums import ControllerState, TaskStatus, SubtaskStatus, TriggerCode, TriggerRole + +from .config_manager import ConfigManager +from .rule_engine import RuleEngine +from .state_handlers import StateHandlers +from .state_machine import StateMachine + +logger = logging.getLogger(__name__) + + +class MainController: + """Main controller that integrates all modules and provides a unified interface""" + + def __init__( + self, + platform: str = platform.system().lower(), + memory_root_path: str = os.getcwd(), + memory_folder_name: str = "kb_s2", + kb_release_tag: str = "v0.2.2", + enable_takeover: bool = False, + enable_search: bool = False, + enable_rag: bool = False, + backend: str = "pyautogui", + user_query: str = "", + max_steps: int = 50, + env: Optional[DesktopEnv] = None, + env_password: str = "osworld-public-evaluation", + log_dir: str = "logs", + datetime_str: str = datetime.now().strftime("%Y%m%d_%H%M%S"), + enable_snapshots: bool = True, + snapshot_interval: int = 10, # Automatically create snapshot every N steps + create_checkpoint_snapshots: bool = True, # Whether to create checkpoint snapshots at key states + global_state: Optional[NewGlobalState] = None, # New: Allow injection of existing global state (for snapshot recovery) + initialize_controller: bool = True # New: Whether to execute initialization process (skip when recovering from snapshot) + ): + # Snapshot configuration + self.enable_snapshots = enable_snapshots + self.snapshot_interval = snapshot_interval + self.create_checkpoint_snapshots = create_checkpoint_snapshots + self.last_snapshot_step = 0 + + # Initialize global state (support external injection) + if global_state is not None: + self.global_state = global_state + else: + self.global_state = self._registry_global_state(log_dir, datetime_str) + + # Basic configuration + self.platform = platform + self.user_query = user_query + self.max_steps = max_steps + self.env = env + self.env_password = env_password + self.enable_search = enable_search + self.enable_takeover = enable_takeover + self.enable_rag = enable_rag + self.backend = backend + + # Initialize configuration manager + self.config_manager = ConfigManager(memory_root_path, memory_folder_name) + self.tools_dict = self.config_manager.load_tools_configuration() + self.local_kb_path = self.config_manager.setup_knowledge_base(platform) + # New: Load flow configuration and override default parameters + self.flow_config = self.config_manager.get_flow_config() + self.max_steps = self.flow_config.get("max_steps", self.max_steps) + self.enable_snapshots = self.flow_config.get("enable_snapshots", self.enable_snapshots) + self.snapshot_interval = self.flow_config.get("snapshot_interval_steps", self.snapshot_interval) + self.create_checkpoint_snapshots = self.flow_config.get("create_checkpoint_snapshots", self.create_checkpoint_snapshots) + self.main_loop_sleep_secs = self.flow_config.get("main_loop_sleep_secs", 0.1) + + # Initialize manager + manager_params = { + "tools_dict": self.tools_dict, + "global_state": self.global_state, + "local_kb_path": self.local_kb_path, + "platform": self.platform, + "enable_search": enable_search + } + self.manager = NewManager(**manager_params) + + # Initialize hardware interface + backend_kwargs = { + "platform": platform, + "env_controller": self.env + } + self.hwi = HardwareInterface(backend=backend, **backend_kwargs) + logger.info(f"Hardware interface initialized with backend: {backend}") + + # Initialize executor + executor_params = { + "global_state": self.global_state, + "hardware_interface": self.hwi, + "env_controller": self.env + } + self.executor = NewExecutor(**executor_params) + logger.info("Executor initialized") + + # Initialize rule engine + rule_engine_params: Dict[str, Any] = dict( + global_state=self.global_state, + max_steps=self.max_steps, + max_state_switches=self.flow_config.get("max_state_switches", 500), + max_state_duration=self.flow_config.get("max_state_duration_secs", 300), + flow_config=self.flow_config, + ) + self.rule_engine = RuleEngine(**rule_engine_params) + + # Initialize state handlers + state_handlers_params: Dict[str, Any] = dict( + global_state=self.global_state, + manager=self.manager, + executor=self.executor, + tools_dict=self.tools_dict, + platform=self.platform, + enable_search=enable_search, + env_password=self.env_password, + rule_engine=self.rule_engine + ) + self.state_handlers = StateHandlers(**state_handlers_params) + + # Initialize state machine + state_machine_params: Dict[str, Any] = dict( + global_state=self.global_state, + rule_engine=self.rule_engine, + state_handlers=self.state_handlers + ) + self.state_machine = StateMachine(**state_machine_params) + + # Initialize counters + self.reset_counters() + + # Initialize task and initial snapshot (can be skipped for snapshot recovery) + if initialize_controller: + # Initialize task and generate first screenshot + self._handle_task_init() + + # Create initial snapshot + if self.enable_snapshots: + self._create_initial_snapshot() + + def _registry_global_state(self, log_dir: str, datetime_str: str): + """Register global state""" + # Ensure necessary directory structure exists + timestamp_dir = os.path.join(log_dir, datetime_str) + cache_dir = os.path.join(timestamp_dir, "cache", "screens") + state_dir = os.path.join(timestamp_dir, "state") + + os.makedirs(cache_dir, exist_ok=True) + os.makedirs(state_dir, exist_ok=True) + + global_state = NewGlobalState( + screenshot_dir=cache_dir, + state_dir=state_dir, + display_info_path=os.path.join(timestamp_dir, "display.json") + ) + Registry.register("GlobalStateStore", global_state) + return global_state + + def _handle_task_init(self): + """Handle task initialization state""" + logger.info("Handling INIT state") + self.global_state.set_task_objective(self.user_query) + # Initialize controller state + self.global_state.reset_controller_state() + logger.info("MainController initialized") + + # wait for environment to setup + time.sleep(10) + + # Get first screenshot + screenshot: Image.Image = self.hwi.dispatch(Screenshot()) # type: ignore + self.global_state.set_screenshot(scale_screenshot_dimensions(screenshot, self.hwi)) + + def _build_env_config(self) -> Dict[str, Any]: + """Build serializable environment configuration for rebuilding DesktopEnv during snapshot recovery. + Only record key fields needed for reconstruction, avoiding storing sensitive information. + """ + env_config: Dict[str, Any] = {"present": False} + try: + if self.env is None: + return env_config + env_config["present"] = True + # Basic information + env_config["class_name"] = self.env.__class__.__name__ + # Key fields (safely get using getattr) + for key in [ + "provider_name", + "os_type", + "action_space", + "headless", + "require_a11y_tree", + "require_terminal", + "snapshot_name", + ]: + value = getattr(self.env, key, None) + if value is not None: + env_config[key] = value + # Path fields: may be relative paths, try to store as absolute paths + path_to_vm = getattr(self.env, "path_to_vm", None) + if path_to_vm: + try: + env_config["path_to_vm"] = os.path.abspath(path_to_vm) + except Exception: + env_config["path_to_vm"] = path_to_vm + # Resolution + screen_width = getattr(self.env, "screen_width", None) + screen_height = getattr(self.env, "screen_height", None) + if screen_width and screen_height: + env_config["screen_size"] = [int(screen_width), int(screen_height)] + except Exception: + # Don't block snapshot due to environment serialization failure + logger.debug("Failed to build env config for snapshot", exc_info=True) + return env_config + + def _base_snapshot_config(self) -> Dict[str, Any]: + """Uniformly build snapshot configuration parameters, including existing configuration and environment information.""" + return { + "tools_dict": self.tools_dict, + "platform": self.platform, + "enable_search": self.enable_search, + "env_password": self.env_password, + "enable_takeover": self.enable_takeover, + "enable_rag": self.enable_rag, + "backend": self.backend, + "max_steps": self.max_steps, + "env": self._build_env_config(), + } + + def _create_initial_snapshot(self): + """Create initial snapshot""" + try: + if self.enable_snapshots: + # Prepare configuration parameters + config_params = self._base_snapshot_config() + + snapshot_id = self.global_state.create_snapshot( + description=f"Initial state for task: {self.user_query}", + snapshot_type="initial", + config_params=config_params + ) + logger.info(f"Initial snapshot created: {snapshot_id}") + except Exception as e: + logger.warning(f"Failed to create initial snapshot: {e}") + + def _should_create_auto_snapshot(self) -> bool: + """Determine whether to create automatic snapshot""" + if not self.enable_snapshots: + return False + + task = self.global_state.get_task() + current_step = task.step_num if task else 0 + return (current_step - self.last_snapshot_step) >= self.snapshot_interval + + def _create_auto_snapshot(self): + """Create automatic snapshot""" + try: + if self._should_create_auto_snapshot(): + task = self.global_state.get_task() + current_step = task.step_num if task else 0 + + # Prepare configuration parameters + config_params = self._base_snapshot_config() + + snapshot_id = self.global_state.create_snapshot( + description=f"Auto snapshot at step {current_step}", + snapshot_type="auto", + config_params=config_params + ) + self.last_snapshot_step = current_step + logger.debug(f"Auto snapshot created: {snapshot_id}") + except Exception as e: + logger.warning(f"Failed to create auto snapshot: {e}") + + def _create_checkpoint_snapshot(self, checkpoint_name: str = ""): + """Create checkpoint snapshot""" + try: + if self.enable_snapshots and self.create_checkpoint_snapshots: + task = self.global_state.get_task() + current_step = task.step_num if task else 0 + + if not checkpoint_name: + checkpoint_name = f"checkpoint_step_{current_step}" + + # Prepare configuration parameters + config_params = self._base_snapshot_config() + + snapshot_id = self.global_state.create_snapshot( + description=f"Checkpoint: {checkpoint_name}", + snapshot_type="checkpoint", + config_params=config_params + ) + logger.info(f"Checkpoint snapshot created: {snapshot_id}") + return snapshot_id + except Exception as e: + logger.warning(f"Failed to create checkpoint snapshot: {e}") + return None + + def _create_error_snapshot(self, error_message: str, error_type: str = "unknown"): + """Create error snapshot""" + try: + if self.enable_snapshots: + # Prepare configuration parameters + config_params = self._base_snapshot_config() + + snapshot_id = self.global_state.create_snapshot( + description=f"Error: {error_message}", + snapshot_type=f"error_{error_type}", + config_params=config_params + ) + logger.info(f"Error snapshot created: {snapshot_id}") + return snapshot_id + except Exception as e: + logger.warning(f"Failed to create error snapshot: {e}") + return None + + def _handle_snapshot_creation(self, current_state: ControllerState): + """Handle snapshot creation logic""" + if not self.enable_snapshots: + return + + try: + # Check if should create automatic snapshot + self._create_auto_snapshot() + + # In key states create checkpoint snapshot + if self.create_checkpoint_snapshots: + if current_state in [ControllerState.PLAN, ControllerState.QUALITY_CHECK, ControllerState.FINAL_CHECK, ControllerState.GET_ACTION]: + self._create_checkpoint_snapshot(f"checkpoint_{current_state.value.lower()}") + + except Exception as e: + logger.warning(f"Error in snapshot creation: {e}") + + def execute_single_step(self, steps: int = 1) -> None: + """Single step execution logic (execute steps steps, do not enter loop)""" + if steps is None or steps <= 0: + steps = 1 + + try: + for step_index in range(steps): + # 1. Check if should terminate (single step sequence) + if self.state_machine.should_exit_loop(): + logger.info("Task fulfilled or rejected, terminating single step batch") + break + + # 2. Get current state + current_state = self.state_machine.get_current_state() + logger.info(f"Current state (single step {step_index + 1}/{steps}): {current_state}") + + # 3. Handle snapshot creation + self._handle_snapshot_creation(current_state) + + # 4. According to state execute appropriate handling (once step by step) + self._handle_state(current_state) + + # 5. Each step ends, handle rules and update states + self.state_machine.process_rules_and_update_states() + + except Exception as e: + logger.error(f"Error in single step batch: {e}") + # Create error snapshot + self._create_error_snapshot(str(e), "single_step_batch") + + self.global_state.add_event( + "controller", "error", f"Single step batch error: {str(e)}") + # Error recovery: back to INIT state (single step sequence) + self.state_machine.switch_state( + ControllerState.INIT, TriggerRole.CONTROLLER, f"Error recovery from single step batch: {str(e)}", TriggerCode.ERROR_RECOVERY) + + def execute_main_loop(self) -> None: + """Main loop execution - based on state state machine""" + logger.info("Starting main loop execution") + + # Record main loop start time + main_loop_start_time = time.time() + while True: + try: + # print("execute_main_loop") + # 1. Check if should exit loop + if self.state_machine.should_exit_loop(): + logger.info("Task fulfilled or rejected, breaking main loop") + break + + # 2. Get current state + current_state = self.state_machine.get_current_state() + + # 3. Handle snapshot creation + self._handle_snapshot_creation(current_state) + + # 4. According to state execute appropriate handling + self._handle_state(current_state) + + # 5. Each loop ends, handle rules and update states + self.state_machine.process_rules_and_update_states() + + # 6. Increase turn count + self.increment_turn_count() + + # 7. Short term wait + time.sleep(self.main_loop_sleep_secs) + + except Exception as e: + logger.error(f"Error in main loop: {e}") + # Create error snapshot + self._create_error_snapshot(str(e), "main_loop") + + self.global_state.log_operation( + "controller", "error", {"error": f"Main loop error: {str(e)}"}) + # Error recovery: back to INIT state + self.state_machine.switch_state( + ControllerState.INIT, TriggerRole.CONTROLLER, f"Error recovery from main loop: {str(e)}", TriggerCode.ERROR_RECOVERY) + time.sleep(1) + + # Record main loop end statistics + main_loop_duration = time.time() - main_loop_start_time + counters = self.get_counters() + self.global_state.log_operation( + "controller", "main_loop_completed", { + "duration": main_loop_duration, + "step_count": counters["step_count"], + "turn_count": counters["turn_count"], + "final_state": self.state_machine.get_current_state().value + }) + + # Create completed snapshot + if self.enable_snapshots: + self._create_checkpoint_snapshot("task_completed") + + logger.info( + f"Main loop completed in {main_loop_duration:.2f}s with {counters['step_count']} steps and {counters['turn_count']} turns" + ) + + def _handle_state(self, current_state: ControllerState): + """Handle state according to state""" + if current_state == ControllerState.INIT: + new_state, trigger_role, trigger_details, trigger_code = self.state_handlers.handle_init_state() + self.state_machine.switch_state(new_state, trigger_role, trigger_details, trigger_code) + + elif current_state == ControllerState.GET_ACTION: + new_state, trigger_role, trigger_details, trigger_code = self.state_handlers.handle_get_action_state() + self.state_machine.switch_state(new_state, trigger_role, trigger_details, trigger_code) + + elif current_state == ControllerState.EXECUTE_ACTION: + new_state, trigger_role, trigger_details, trigger_code = self.state_handlers.handle_execute_action_state() + self.state_machine.switch_state(new_state, trigger_role, trigger_details, trigger_code) + + elif current_state == ControllerState.QUALITY_CHECK: + new_state, trigger_role, trigger_details, trigger_code = self.state_handlers.handle_quality_check_state() + self.state_machine.switch_state(new_state, trigger_role, trigger_details, trigger_code) + + elif current_state == ControllerState.PLAN: + new_state, trigger_role, trigger_details, trigger_code = self.state_handlers.handle_plan_state() + self.state_machine.switch_state(new_state, trigger_role, trigger_details, trigger_code) + + elif current_state == ControllerState.SUPPLEMENT: + new_state, trigger_role, trigger_details, trigger_code = self.state_handlers.handle_supplement_state() + self.state_machine.switch_state(new_state, trigger_role, trigger_details, trigger_code) + + elif current_state == ControllerState.FINAL_CHECK: + new_state, trigger_role, trigger_details, trigger_code = self.state_handlers.handle_final_check_state() + self.state_machine.switch_state(new_state, trigger_role, trigger_details, trigger_code) + + elif current_state == ControllerState.DONE: + logger.info("Task completed") + else: + logger.error(f"Unknown state: {current_state}") + self.state_machine.switch_state( + ControllerState.INIT, TriggerRole.CONTROLLER, f"Unknown state encountered: {current_state}", TriggerCode.UNKNOWN_STATE) + + def get_controller_info(self) -> Dict[str, Any]: + """Get controller information""" + return { + "current_state": self.state_machine.get_current_state().value, + "state_start_time": self.global_state.get_controller_state_start_time(), + "state_switch_count": self.state_machine.get_state_switch_count(), + "plan_num": self.global_state.get_plan_num(), + "controller_state": self.global_state.get_controller_state(), + "task_id": self.global_state.task_id, + "executor_status": self.executor.get_execution_status(), + "snapshot_info": { + "enabled": self.enable_snapshots, + "interval": self.snapshot_interval, + "last_snapshot_step": self.last_snapshot_step, + "checkpoint_snapshots": self.create_checkpoint_snapshots, + "note": "Use create_manual_snapshot() to create snapshots" + } + } + + def reset_controller(self): + """Reset controller state""" + logger.info("Resetting controller") + self.state_machine.reset_state_switch_count() + self.global_state.reset_controller_state() + self.reset_counters() # Reset counters + + # Reset snapshot related state + self.last_snapshot_step = 0 + + # Reset plan_num + task = self.global_state.get_task() + if task: + task.plan_num = 0 + self.global_state.set_task(task) + logger.info("Plan number reset to 0") + + logger.info("Controller reset completed") + + def reset_counters(self) -> None: + """Reset statistics counters""" + self.step_count = 0 + self.turn_count = 0 + logger.info("Counters reset: step_count=0, turn_count=0") + + def increment_step_count(self) -> None: + """Increment step count""" + self.step_count += 1 + logger.debug(f"Step count incremented: {self.step_count}") + + def increment_turn_count(self) -> None: + """Increment turn count""" + self.turn_count += 1 + logger.debug(f"Turn count incremented: {self.turn_count}") + + def get_counters(self) -> Dict[str, int]: + """Get current counters status""" + task = self.global_state.get_task() + step_count = task.step_num if task else 0 + return {"step_count": step_count, "turn_count": self.turn_count} + + # ========= Snapshot Management Methods ========= + def create_manual_snapshot(self, description: str = "") -> Optional[str]: + """Manual snapshot creation""" + try: + if not self.enable_snapshots: + logger.warning("Snapshots are disabled") + return None + + task = self.global_state.get_task() + current_step = task.step_num if task else 0 + + if not description: + description = f"Manual snapshot at step {current_step}" + + # Prepare configuration parameters + config_params = self._base_snapshot_config() + + snapshot_id = self.global_state.create_snapshot(description, "manual", config_params) + logger.info(f"Manual snapshot created: {snapshot_id}") + return snapshot_id + + except Exception as e: + logger.error(f"Failed to create manual snapshot: {e}") + return None + + \ No newline at end of file diff --git a/mm_agents/maestro/maestro/controller/rule_engine.py b/mm_agents/maestro/maestro/controller/rule_engine.py new file mode 100644 index 0000000..b4641ad --- /dev/null +++ b/mm_agents/maestro/maestro/controller/rule_engine.py @@ -0,0 +1,319 @@ +"""Rule Engine for Maestro Controller +Responsible for handling various business rules and state checks +""" + +import time +import logging +from datetime import datetime +from typing import Optional, Dict, Any, List + +from ..new_global_state import NewGlobalState +from ..enums import ControllerState, SubtaskStatus, TaskStatus, TriggerCode +from ..data_models import CommandData +from ..Action import Action + +logger = logging.getLogger(__name__) + + +class RuleEngine: + """Rule engine responsible for handling various business rules and state checks""" + + def __init__( + self, + global_state: NewGlobalState, + max_steps: int = 50, + max_state_switches: int = 500, + max_state_duration: int = 300, + flow_config: Optional[Dict[str, Any]] = None, + ): + self.global_state = global_state + self.max_steps = max_steps + self.max_state_switches = max_state_switches + self.max_state_duration = max_state_duration + # Added: Flow configuration thresholds + self.flow_config = flow_config or {} + self.quality_check_interval_secs = self.flow_config.get("quality_check_interval_secs", 300) + self.first_quality_check_min_commands = self.flow_config.get("first_quality_check_min_commands", 5) + self.repeated_action_min_consecutive = self.flow_config.get("repeated_action_min_consecutive", 3) + self.replan_long_execution_threshold = self.flow_config.get("replan_long_execution_threshold", 15) + self.plan_number_limit = self.flow_config.get("plan_number_limit", 50) + + # Added: Repeated action counter to track consecutive similar actions + # This counter is reset after each quality check trigger to avoid continuous triggering + self.repeated_action_counter = 0 + + def _are_actions_similar(self, action1: Any, action2: Any) -> bool: + """Check if two Actions are the same (excluding descriptive fields) + + Supports both dict format (regular Actions) and list format (technician Actions). + + Args: + action1: Action representation (dict or list) + action2: Action representation (dict or list) + + Returns: + bool: Returns True if the two Actions are the same, otherwise False + """ + try: + # Handle different action format combinations + action1_is_dict = isinstance(action1, dict) + action2_is_dict = isinstance(action2, dict) + action1_is_list = isinstance(action1, list) + action2_is_list = isinstance(action2, list) + + # Case 1: Both are dict format (regular Actions) + if action1_is_dict and action2_is_dict: + return self._compare_dict_actions(action1, action2) + + # Case 2: Both are list format (technician Actions) + elif action1_is_list and action2_is_list: + return self._compare_list_actions(action1, action2) + + # Case 3: Mixed formats (dict vs list) - always different + else: + return False + + except Exception as e: + logger.error(f"Error comparing actions: {e}") + return False + + def _compare_dict_actions(self, action1: Dict[str, Any], action2: Dict[str, Any]) -> bool: + """Compare two dict format actions (regular Actions)""" + # Check if Action types are the same + if action1.get("type") != action2.get("type"): + return False + + # Define descriptive fields to exclude (these fields don't affect actual Action execution) + descriptive_fields = { + "element_description", # Click, DoubleClick, Move, Scroll + "starting_description", # Drag + "ending_description", # Drag + } + + # Compare all non-descriptive fields + for key in action1: + if key in descriptive_fields: + continue # Skip descriptive fields + + if key not in action2: + return False + + if action1[key] != action2[key]: + return False + + # Check if action2 has fields that action1 doesn't have (except descriptive fields) + for key in action2: + if key in descriptive_fields: + continue # Skip descriptive fields + + if key not in action1: + return False + + return True + + def _compare_list_actions(self, action1: List[Any], action2: List[Any]) -> bool: + """Compare two list format actions (technician Actions) + + Technician actions are in format [language, code] where: + - language: programming language (e.g., "bash", "python") + - code: the actual code content + """ + # Both should be lists with exactly 2 elements + if len(action1) != 2 or len(action2) != 2: + return False + + # Extract language and code for both actions + lang1, code1 = action1[0], action1[1] + lang2, code2 = action2[0], action2[1] + + # Both language and code should be strings + if not (isinstance(lang1, str) and isinstance(code1, str) and + isinstance(lang2, str) and isinstance(code2, str)): + return False + + # Compare language and code + return lang1 == lang2 and code1 == code2 + + def _check_consecutive_similar_actions(self, commands: List[CommandData], min_consecutive: int = 3) -> bool: + """Check if there are consecutive similar Actions using repeated_action_counter + + Args: + commands: List of commands + min_consecutive: Minimum consecutive count + + Returns: + bool: Returns True if consecutive similar Actions are found, otherwise False + """ + try: + if min_consecutive is None: + min_consecutive = self.repeated_action_min_consecutive + if len(commands) < min_consecutive: + return False + + # Check if the latest command is similar to the previous one + if len(commands) >= 2: + latest_action = commands[-1].action + previous_action = commands[-2].action + + if self._are_actions_similar(latest_action, previous_action): + # Increment counter for consecutive similar actions + self.repeated_action_counter += 1 + logger.debug(f"Incremented repeated action counter to {self.repeated_action_counter}") + + # Check if we've reached the threshold + if self.repeated_action_counter >= min_consecutive: + logger.info(f"Found {self.repeated_action_counter} consecutive similar actions") + return True + else: + # Reset counter if actions are different + self.repeated_action_counter = 1 + logger.debug("Reset repeated action counter due to different action") + return False + + except Exception as e: + logger.error(f"Error checking consecutive similar actions: {e}") + return False + + def check_task_state_rules(self, state_switch_count: int) -> Optional[tuple[ControllerState, TriggerCode]]: + """Check task_state related rules, including termination conditions + + Returns: + Optional[tuple[ControllerState, TriggerCode]]: Returns new state and corresponding TriggerCode, returns None if no rules are triggered + """ + try: + task = self.global_state.get_task() + if not task: + return None + + # Check maximum state switch count + if state_switch_count >= self.max_state_switches: + logger.warning( + f"Maximum state switches ({self.max_state_switches}) reached" + ) + self.global_state.update_task_status(TaskStatus.REJECTED) + return (ControllerState.DONE, TriggerCode.RULE_MAX_STATE_SWITCHES_REACHED) + + # Check task status + if task.status == "fulfilled": + logger.info("Task marked as completed") + return (ControllerState.DONE, TriggerCode.RULE_TASK_COMPLETED) + + # rule: If task runtime exceeds 2 hours from created_at, mark as REJECTED + try: + created_at_dt = datetime.fromisoformat(task.created_at) + if (datetime.now() - created_at_dt).total_seconds() > 2 * 3600: + logger.warning( + "Task runtime exceeded 2 hours since creation, marking task as REJECTED" + ) + self.global_state.update_task_status(TaskStatus.REJECTED) + return (ControllerState.DONE, TriggerCode.RULE_TASK_RUNTIME_EXCEEDED) + except Exception as _time_err: + logger.error(f"Error parsing task.created_at or computing runtime: {_time_err}") + + # Check planning count limit - if planning count exceeds configured limit, mark task as failed + plan_num = self.global_state.get_plan_num() + if plan_num > self.plan_number_limit: + logger.warning( + f"Plan number ({plan_num}) exceeds limit ({self.plan_number_limit}), marking task as REJECTED") + self.global_state.update_task_status(TaskStatus.REJECTED) + return (ControllerState.DONE, TriggerCode.RULE_PLAN_NUMBER_EXCEEDED) + + # current_step greater than max_steps - rejected/fulfilled + if task.step_num >= self.max_steps: + # Check if all subtasks are completed + logger.warning( + f"Step number ({task.step_num}) >= max_steps ({self.max_steps}) but subtasks not completed, marking task as REJECTED" + ) + self.global_state.update_task_status(TaskStatus.REJECTED) + return (ControllerState.DONE, TriggerCode.RULE_STATE_SWITCH_COUNT_EXCEEDED) + + + return None + + except Exception as e: + logger.error(f"Error checking task state rules: {e}") + return None + + def check_current_state_rules(self) -> Optional[tuple[ControllerState, TriggerCode]]: + """Check current_state related rules + + Returns: + Optional[tuple[ControllerState, TriggerCode]]: Returns new state and corresponding TriggerCode, returns None if no rules are triggered + """ + try: + task = self.global_state.get_task() + if not task: + return None + + # Quality check trigger logic: 5 commands have been generated since the last quality check, and the creation time of these 5 commands is all greater than the last quality check time + gate_checks = self.global_state.get_gate_checks() + if gate_checks: + # Get the time of the most recent quality check + latest_quality_check = max(gate_checks, key=lambda x: x.created_at) + latest_quality_check_time = datetime.fromisoformat(latest_quality_check.created_at) + + # Check if there are enough commands for quality check + all_commands = self.global_state.get_commands() + + # Calculate the number of new commands generated since the last quality check + new_commands_count = 0 + for command in reversed(all_commands): # Start checking from the latest command + cmd_time = datetime.fromisoformat(command.created_at) + if cmd_time > latest_quality_check_time: + new_commands_count += 1 + else: + break # Stop when encountering commands earlier than quality check time + + # If the number of newly generated commands reaches the threshold, trigger quality check + if (new_commands_count >= self.first_quality_check_min_commands and + self.global_state.get_controller_current_state() not in [ControllerState.QUALITY_CHECK, ControllerState.DONE]): + logger.info(f"Quality check triggered: {new_commands_count} new commands after last quality check at {latest_quality_check_time}, switching to QUALITY_CHECK") + return (ControllerState.QUALITY_CHECK, TriggerCode.RULE_QUALITY_CHECK_STEPS) + else: + # If there are no quality check records and the current subtask's command count reaches the threshold, perform the first quality check + if task.current_subtask_id: + subtask = self.global_state.get_subtask(task.current_subtask_id) + if (subtask and len(subtask.command_trace_ids) >= self.first_quality_check_min_commands and + self.global_state.get_controller_current_state() not in [ControllerState.QUALITY_CHECK, ControllerState.DONE]): + logger.info(f"First quality check after {self.first_quality_check_min_commands} commands for subtask {task.current_subtask_id}, switching to QUALITY_CHECK") + return (ControllerState.QUALITY_CHECK, TriggerCode.RULE_QUALITY_CHECK_STEPS) + + # Consecutive similar actions exceed configured count - QUALITY_CHECK + if task.current_subtask_id: + subtask = self.global_state.get_subtask(task.current_subtask_id) + if subtask and len(subtask.command_trace_ids) >= self.repeated_action_min_consecutive: + # Get all commands for the current subtask + commands = self.global_state.get_commands_for_subtask(task.current_subtask_id) + if commands and self._check_consecutive_similar_actions(commands, min_consecutive=self.repeated_action_min_consecutive): + logger.info( + f"Found {self.repeated_action_counter} consecutive similar actions in subtask {task.current_subtask_id}, switching to QUALITY_CHECK" + ) + # Reset immediately to avoid re-trigger loops + self.reset_repeated_action_counter() + return (ControllerState.QUALITY_CHECK, TriggerCode.RULE_QUALITY_CHECK_REPEATED_ACTIONS) + + # If a subtask's execution actions are too long, exceeding the configured threshold - REPLAN + if task.current_subtask_id: + subtask = self.global_state.get_subtask(task.current_subtask_id) + if subtask and len(subtask.command_trace_ids) > self.replan_long_execution_threshold: + logger.info( + f"Subtask {task.current_subtask_id} has > {self.replan_long_execution_threshold} commands, switching to PLAN" + ) + self.global_state.update_subtask_status(task.current_subtask_id, SubtaskStatus.REJECTED, "replan long execution, too many commands, current_subtask_id: " + task.current_subtask_id) + return (ControllerState.PLAN, TriggerCode.RULE_REPLAN_LONG_EXECUTION) + + return None + + except Exception as e: + logger.error(f"Error checking current situation rules: {e}") + return None + + def is_state_timeout(self) -> bool: + """Check if the current state has timed out""" + state_start_time = self.global_state.get_controller_state_start_time() + return (time.time() - state_start_time) > self.max_state_duration + + def reset_repeated_action_counter(self): + """Reset the repeated action counter - called when state changes or quality check is triggered""" + self.repeated_action_counter = 0 + logger.debug("Repeated action counter reset") diff --git a/mm_agents/maestro/maestro/controller/state_handlers.py b/mm_agents/maestro/maestro/controller/state_handlers.py new file mode 100644 index 0000000..1077f90 --- /dev/null +++ b/mm_agents/maestro/maestro/controller/state_handlers.py @@ -0,0 +1,497 @@ +"""State Handlers for Maestro Controller +Responsible for handling specific logic of various states +""" + +import logging +from typing import Optional + +from ..data_models import CommandData +from ..new_global_state import NewGlobalState +from ..enums import ControllerState, TaskStatus, SubtaskStatus, WorkerDecision, GateDecision, TriggerRole, TriggerCode +from ..new_manager import NewManager +from ..new_worker import NewWorker +from ..evaluator import Evaluator +from ..new_executor import NewExecutor + +logger = logging.getLogger(__name__) + + +class StateHandlers: + """State handler, responsible for handling specific logic of various states""" + + def __init__(self, + global_state: NewGlobalState, + manager: NewManager, + executor: NewExecutor, + tools_dict: dict, + platform: str, + enable_search: bool, + env_password: str = "osworld-public-evaluation", + rule_engine=None + ): + self.global_state: NewGlobalState = global_state + self.manager = manager + self.executor = executor + self.tools_dict = tools_dict + self.platform = platform + self.enable_search = enable_search + self.env_password = env_password + self.rule_engine = rule_engine + + def handle_init_state(self) -> tuple[ControllerState, TriggerRole, str, TriggerCode]: + """Initialize state handling""" + logger.info("Handling INIT state") + self.global_state.set_task_objective(self.global_state.get_task().objective) + + try: + # Check if there are pending subtasks + task = self.global_state.get_task() + pending_subtask_ids = task.pending_subtask_ids or [] + + if pending_subtask_ids: + # Have subtasks, set the first one as current subtask + first_subtask_id = pending_subtask_ids[0] + self.global_state.advance_to_next_subtask() + # Update task status to pending + self.global_state.update_task_status(TaskStatus.PENDING) + logger.info(f"Set current subtask: {first_subtask_id}") + return (ControllerState.GET_ACTION, TriggerRole.CONTROLLER, f"First subtask {first_subtask_id} ready", TriggerCode.SUBTASK_READY) + else: + # No subtasks, need to create + logger.info("No subtasks available, switching to PLAN state") + return (ControllerState.PLAN, TriggerRole.CONTROLLER, "No subtasks available, need planning", TriggerCode.NO_SUBTASKS) + + except Exception as e: + logger.error(f"Error in INIT state: {e}") + self.global_state.add_event("controller", "error", f"INIT state error: {str(e)}") + return (ControllerState.PLAN, TriggerRole.CONTROLLER, f"INIT state error: {str(e)}", TriggerCode.INIT_ERROR) + + def handle_get_action_state(self) -> tuple[ControllerState, TriggerRole, str, TriggerCode]: + """Get next action phase""" + logger.info("Handling GET_ACTION state") + current_subtask_id = self.global_state.get_task().current_subtask_id + + try: + if not current_subtask_id: + logger.warning("No current subtask ID, switching to INIT") + return (ControllerState.INIT, TriggerRole.WORKER_GET_ACTION, "No current subtask ID in GET_ACTION state", TriggerCode.NO_CURRENT_SUBTASK_ID) + + # Check subtask status + subtask = self.global_state.get_subtask(current_subtask_id) + if not subtask: + logger.warning(f"Subtask {current_subtask_id} not found, switching to INIT") + return (ControllerState.INIT, TriggerRole.WORKER_GET_ACTION, f"Subtask {current_subtask_id} not found in GET_ACTION state", TriggerCode.SUBTASK_NOT_FOUND) + + # Check if current subtask indicates task is impossible + if subtask.title == "Task Cannot Be Completed" or "cannot be completed" in subtask.title.lower(): + logger.info("Detected impossible task subtask, rejecting task") + self.global_state.update_task_status(TaskStatus.REJECTED) + return (ControllerState.DONE, TriggerRole.WORKER_GET_ACTION, "Task cannot be completed", TriggerCode.TASK_IMPOSSIBLE) + + # Handled uniformly by Worker: generate action/record decision/create command based on role + worker_params = { + "tools_dict": self.tools_dict, + "global_state": self.global_state, + "platform": self.platform, + "enable_search": self.enable_search, + "client_password": self.env_password + } + worker = NewWorker(**worker_params) + worker.process_subtask_and_create_command() + + # Get worker decision + worker_decision = self.global_state.get_subtask_worker_decision(current_subtask_id) + + if worker_decision: + logger.info(f"Subtask {current_subtask_id} has worker_decision: {worker_decision}") + + # Check if command should be executed immediately + command = self.global_state.get_current_command_for_subtask(current_subtask_id) + if command and self.should_execute_immediately(command): + logger.info(f"Command {command.command_id} should be executed immediately") + self.executor.execute_current_action() + + # Switch states based on worker_decision + if worker_decision == WorkerDecision.WORKER_DONE.value: + # Operation successful, enter quality check phase + logger.info(f"Worker decision is WORKER_DONE, switching to QUALITY_CHECK") + self.global_state.update_subtask_status( + current_subtask_id, SubtaskStatus.PENDING, + "Worker completed action, waiting for quality check") + return (ControllerState.QUALITY_CHECK, TriggerRole.WORKER_GET_ACTION, f"Worker decision success for subtask {current_subtask_id}", TriggerCode.WORKER_SUCCESS) + + elif worker_decision == WorkerDecision.CANNOT_EXECUTE.value: + # Cannot execute, need replanning + logger.info(f"Worker decision is CANNOT_EXECUTE, switching to PLAN") + self.global_state.update_subtask_status( + current_subtask_id, SubtaskStatus.REJECTED, + "Worker cannot execute this subtask") + return (ControllerState.PLAN, TriggerRole.WORKER_GET_ACTION, f"Worker cannot execute subtask {current_subtask_id}", TriggerCode.WORK_CANNOT_EXECUTE) + + elif worker_decision == WorkerDecision.STALE_PROGRESS.value: + # Progress stalled, enter quality check phase + logger.info(f"Worker decision is STALE_PROGRESS, switching to QUALITY_CHECK") + self.global_state.update_subtask_status( + current_subtask_id, SubtaskStatus.STALE, + "Worker progress stale, waiting for quality check") + return (ControllerState.QUALITY_CHECK, TriggerRole.WORKER_GET_ACTION, f"Worker progress stale for subtask {current_subtask_id}", TriggerCode.WORKER_STALE_PROGRESS) + + elif worker_decision == WorkerDecision.SUPPLEMENT.value: + # Need supplementary materials + logger.info(f"Worker decision is SUPPLEMENT, switching to SUPPLEMENT") + self.global_state.update_subtask_status( + current_subtask_id, SubtaskStatus.REJECTED, + "Worker needs supplement, waiting for supplement") + return (ControllerState.SUPPLEMENT, TriggerRole.WORKER_GET_ACTION, f"Worker needs supplement for subtask {current_subtask_id}", TriggerCode.WORKER_SUPPLEMENT) + + elif worker_decision == WorkerDecision.GENERATE_ACTION.value: + # Generated new action, execute action + logger.info(f"Worker decision is GENERATE_ACTION, switching to EXECUTE_ACTION") + self.global_state.update_subtask_status( + current_subtask_id, SubtaskStatus.PENDING, + "Worker generated action, waiting for execute") + return (ControllerState.EXECUTE_ACTION, TriggerRole.WORKER_GET_ACTION, f"Worker generated action for subtask {current_subtask_id}", TriggerCode.WORKER_GENERATE_ACTION) + else: + # Unknown worker_decision, default switch to PLAN + logger.warning(f"Unknown worker_decision: {worker_decision}, switching to PLAN") + self.global_state.update_subtask_status( + current_subtask_id, SubtaskStatus.REJECTED, + f"Unknown worker_decision: {worker_decision}") + return (ControllerState.PLAN, TriggerRole.WORKER_GET_ACTION, f"Unknown worker_decision: {worker_decision}", TriggerCode.NO_WORKER_DECISION) + else: + # Error handling + logger.info(f"Subtask {current_subtask_id} has no worker_decision, switching to PLAN") + self.global_state.update_subtask_status( + current_subtask_id, SubtaskStatus.REJECTED, + "Worker has no worker_decision, switching to PLAN") + return (ControllerState.PLAN, TriggerRole.WORKER_GET_ACTION, f"Subtask {current_subtask_id} has no worker_decision in GET_ACTION state", TriggerCode.NO_WORKER_DECISION) + + except Exception as e: + logger.error(f"Error in GET_ACTION state: {e}") + self.global_state.log_operation( + "controller", "error", {"error": f"GET_ACTION state error: {str(e)}"}) + + # Update subtask status to failed + if current_subtask_id is not None: + self.global_state.update_subtask_status( + current_subtask_id, SubtaskStatus.REJECTED, + "Worker has no worker_decision, switching to PLAN") + return (ControllerState.PLAN, TriggerRole.WORKER_GET_ACTION, f"GET_ACTION state error: {str(e)}", TriggerCode.GET_ACTION_ERROR) + + def handle_execute_action_state(self) -> tuple[ControllerState, TriggerRole, str, TriggerCode]: + """Execute action phase""" + logger.info("Handling EXECUTE_ACTION state") + + try: + current_subtask_id = self.global_state.get_task().current_subtask_id + if not current_subtask_id: + logger.warning("No current subtask ID in EXECUTE_ACTION state") + return (ControllerState.INIT, TriggerRole.EXECUTOR_EXECUTE_ACTION, "No current subtask ID in EXECUTE_ACTION state", TriggerCode.NO_CURRENT_SUBTASK_ID) + + # Get current subtask + subtask = self.global_state.get_subtask(current_subtask_id) + if not subtask: + logger.warning(f"Subtask {current_subtask_id} not found in EXECUTE_ACTION state") + return (ControllerState.INIT, TriggerRole.EXECUTOR_EXECUTE_ACTION, f"Subtask {current_subtask_id} not found in EXECUTE_ACTION state", TriggerCode.SUBTASK_NOT_FOUND) + + # Check if command should be executed immediately + command = self.global_state.get_current_command_for_subtask(current_subtask_id) + if command and self.should_execute_immediately(command): + logger.info(f"Command {command.command_id} has executed immediately, switching to GET_ACTION") + return (ControllerState.GET_ACTION, TriggerRole.EXECUTOR_EXECUTE_ACTION, f"Command {command.command_id} has executed immediately", TriggerCode.COMMAND_COMPLETED) + + # use executor to execute action + execution_result = self.executor.execute_current_action() + if execution_result["success"]: + logger.info(f"Action executed successfully for subtask {current_subtask_id} in {execution_result['execution_time']:.2f}s") + else: + # Execution failed, mark subtask as failed and switch to replanning state + error_msg = execution_result.get("error_message", "Unknown execution error") + logger.warning(f"Action execution failed for subtask {current_subtask_id}: {error_msg}") + + self.global_state.update_subtask_status( + current_subtask_id, SubtaskStatus.PENDING, + f"Action execution failed: {error_msg}") + return (ControllerState.GET_ACTION, TriggerRole.EXECUTOR_EXECUTE_ACTION, f"Action execution failed: {error_msg}", TriggerCode.EXECUTION_ERROR) + + # Get screenshot Executor processing + command = self.global_state.get_current_command_for_subtask(current_subtask_id) + if command: + # Check current subtask's assignee_role, if analyst, enter quality check directly after executing action + subtask = self.global_state.get_subtask(current_subtask_id) + if subtask and subtask.assignee_role == "analyst": + logger.info(f"Analyst subtask {current_subtask_id} action executed, switching to GET_ACTION") + # Update subtask status to continue normal flow + self.global_state.update_subtask_status( + current_subtask_id, SubtaskStatus.PENDING, + "Analyst action executed, continue to next action") + return (ControllerState.GET_ACTION, TriggerRole.EXECUTOR_EXECUTE_ACTION, f"Analyst action executed for subtask {current_subtask_id}", TriggerCode.COMMAND_COMPLETED) + else: + # Non-analyst role, continue normal GET_ACTION flow + return (ControllerState.GET_ACTION, TriggerRole.EXECUTOR_EXECUTE_ACTION, f"{command.command_id} command completed", TriggerCode.COMMAND_COMPLETED) + else: + return (ControllerState.GET_ACTION, TriggerRole.EXECUTOR_EXECUTE_ACTION, "No command found in EXECUTE_ACTION state", TriggerCode.NO_COMMAND) + + except Exception as e: + logger.error(f"Error in EXECUTE_ACTION state: {e}") + self.global_state.add_event("controller", "error", f"EXECUTE_ACTION state error: {str(e)}") + return (ControllerState.GET_ACTION, TriggerRole.EXECUTOR_EXECUTE_ACTION, f"EXECUTE_ACTION state error: {str(e)}", TriggerCode.EXECUTION_ERROR) + + def handle_quality_check_state(self) -> tuple[ControllerState, TriggerRole, str, TriggerCode]: + """Quality gate check phase""" + logger.info("Handling QUALITY_CHECK state") + current_subtask_id = self.global_state.get_task().current_subtask_id + + try: + if not current_subtask_id: + logger.warning("No current subtask ID in QUALITY_CHECK state") + return (ControllerState.INIT, TriggerRole.EVALUATOR_QUALITY_CHECK, "No current subtask ID in QUALITY_CHECK state", TriggerCode.NO_CURRENT_SUBTASK_ID) + + # Check if current subtask indicates task is impossible + subtask = self.global_state.get_subtask(current_subtask_id) + if subtask and (subtask.title == "Task Cannot Be Completed" or "cannot be completed" in subtask.title.lower()): + logger.info("Detected impossible task subtask in quality check, rejecting task") + self.global_state.update_task_status(TaskStatus.REJECTED) + return (ControllerState.DONE, TriggerRole.EVALUATOR_QUALITY_CHECK, "Task cannot be completed", TriggerCode.TASK_IMPOSSIBLE) + + # Reset repeated action counter when entering quality check state + # This ensures we don't immediately trigger another quality check for the same pattern + if self.rule_engine: + self.rule_engine.reset_repeated_action_counter() + + evaluator_params = { + "global_state": self.global_state, + "tools_dict": self.tools_dict + } + evaluator = Evaluator(**evaluator_params) + + # Wait for Evaluator to complete quality check + evaluator.quality_check() + + # Check quality check results + latest_gate = self.global_state.get_latest_gate_check_for_subtask(current_subtask_id) + + if latest_gate: + decision = latest_gate.decision + logger.info(f"Latest gate check decision for subtask {current_subtask_id}: {decision}") + + if decision == GateDecision.GATE_DONE.value: + # Quality check passed, subtask completed + self.global_state.update_subtask_status( + current_subtask_id, SubtaskStatus.FULFILLED, + "Quality check passed") + logger.info(f"Quality check passed for subtask {current_subtask_id}") + + # Check if task is completed + task = self.global_state.get_task() + if not task.pending_subtask_ids: + # All subtasks completed + if not getattr(task, 'managerComplete', False): + logger.info("All subtasks completed, but manager incomplete, entering plan") + return (ControllerState.PLAN, TriggerRole.EVALUATOR_QUALITY_CHECK, "All subtasks completed, manager incomplete; entering plan", TriggerCode.FINAL_CHECK_FAILED) + logger.info("All subtasks completed, entering final check") + return (ControllerState.FINAL_CHECK, TriggerRole.EVALUATOR_QUALITY_CHECK, "All subtasks completed, entering final check", TriggerCode.ALL_SUBTASKS_COMPLETED) + + # Still have pending subtasks, advance to next one + self.global_state.advance_to_next_subtask() + return (ControllerState.GET_ACTION, TriggerRole.EVALUATOR_QUALITY_CHECK, f"Quality check passed for subtask {current_subtask_id}", TriggerCode.QUALITY_CHECK_PASSED) + + elif decision == GateDecision.GATE_FAIL.value: + logger.info(f"Quality check failed for subtask {current_subtask_id}") + # Update subtask status to failed + self.global_state.update_subtask_status( + current_subtask_id, SubtaskStatus.REJECTED, + "Quality check failed") + return (ControllerState.PLAN, TriggerRole.EVALUATOR_QUALITY_CHECK, f"Quality check failed for subtask {current_subtask_id}", TriggerCode.QUALITY_CHECK_FAILED) + + elif decision == GateDecision.GATE_SUPPLEMENT.value: + # Need supplementary materials + logger.info(f"Quality check requires supplement for subtask {current_subtask_id}") + self.global_state.update_subtask_status( + current_subtask_id, SubtaskStatus.REJECTED, + "Quality check requires supplement") + return (ControllerState.SUPPLEMENT, TriggerRole.EVALUATOR_QUALITY_CHECK, f"Quality check requires supplement for subtask {current_subtask_id}", TriggerCode.QUALITY_CHECK_SUPPLEMENT) + + elif decision == GateDecision.GATE_CONTINUE.value: + # execute_action + logger.info(f"Quality check requires execute action for subtask {current_subtask_id}") + self.global_state.update_subtask_status( + current_subtask_id, SubtaskStatus.PENDING, + "Quality check requires execute action") + return (ControllerState.EXECUTE_ACTION, TriggerRole.EVALUATOR_QUALITY_CHECK, f"Quality check requires execute action for subtask {current_subtask_id}", TriggerCode.QUALITY_CHECK_EXECUTE_ACTION) + else: + # Unknown gate decision, default switch to PLAN + logger.warning(f"Unknown gate decision: {decision}, switching to PLAN") + self.global_state.update_subtask_status( + current_subtask_id, SubtaskStatus.REJECTED, + f"Unknown gate decision: {decision}") + return (ControllerState.PLAN, TriggerRole.EVALUATOR_QUALITY_CHECK, f"Unknown gate decision: {decision}", TriggerCode.QUALITY_CHECK_ERROR) + else: + # No quality check records, error + logger.debug(f"No gate checks found for subtask {current_subtask_id}") + return (ControllerState.PLAN, TriggerRole.EVALUATOR_QUALITY_CHECK, "Quality check error", TriggerCode.QUALITY_CHECK_ERROR) + + except Exception as e: + logger.error(f"Error in QUALITY_CHECK state: {e}") + self.global_state.add_event("controller", "error", f"QUALITY_CHECK state error: {str(e)}") + if current_subtask_id is not None: + self.global_state.update_subtask_status( + current_subtask_id, SubtaskStatus.REJECTED, + "Quality check error") + return (ControllerState.PLAN, TriggerRole.EVALUATOR_QUALITY_CHECK, f"QUALITY_CHECK state error: {str(e)}", TriggerCode.QUALITY_CHECK_ERROR) + + def handle_plan_state(self) -> tuple[ControllerState, TriggerRole, str, TriggerCode]: + """Replanning phase""" + logger.info("Handling PLAN state") + + try: + # Increment planning count + self.global_state.increment_plan_num() + logger.info(f"Plan number incremented to: {self.global_state.get_plan_num()}") + + # Call Manager for replanning + self.manager.plan_task("replan") + + # Check new subtask list + task = self.global_state.get_task() + pending_subtask_ids = task.pending_subtask_ids or [] + + if pending_subtask_ids: + # Have subtasks, set the first one as current subtask + first_subtask_id = pending_subtask_ids[0] + self.global_state.advance_to_next_subtask() + self.global_state.update_task_status(TaskStatus.PENDING) + logger.info(f"Set current subtask: {first_subtask_id}") + return (ControllerState.GET_ACTION, TriggerRole.MANAGER_REPLAN, f"First subtask {first_subtask_id} ready", TriggerCode.SUBTASK_READY_AFTER_PLAN) + else: + # No subtasks, task may not be completable + logger.warning("No subtasks available, continuing to wait for planning") + return (ControllerState.INIT, TriggerRole.MANAGER_REPLAN, "Plan error, no subtasks available", TriggerCode.PLAN_ERROR) + + except Exception as e: + logger.error(f"Error in PLAN state: {e}") + self.global_state.add_event("controller", "error", f"PLAN state error: {str(e)}") + return (ControllerState.INIT, TriggerRole.MANAGER_REPLAN, f"PLAN state error: {str(e)}", TriggerCode.PLAN_ERROR) + + def handle_supplement_state(self) -> tuple[ControllerState, TriggerRole, str, TriggerCode]: + """Material supplement phase""" + logger.info("Handling SUPPLEMENT state") + + try: + # Increment planning count (supplementing materials is also a planning behavior) + self.global_state.increment_plan_num() + logger.info(f"Plan number incremented to: {self.global_state.get_plan_num()} (supplement)") + + # Wait for Manager to supplement materials + self.manager.plan_task("supplement") + + # If material supplement is completed, return to PLAN + logger.info("Supplement state completed, returning to PLAN") + return (ControllerState.PLAN, TriggerRole.MANAGER_SUPPLEMENT, "Supplement collection completed", TriggerCode.SUPPLEMENT_COMPLETED) + + except Exception as e: + logger.error(f"Error in SUPPLEMENT state: {e}") + self.global_state.add_event("controller", "error", f"SUPPLEMENT state error: {str(e)}") + # current_subtask_id is not defined here, corrected to get current subtask_id + current_subtask_id = self.global_state.get_task().current_subtask_id + if current_subtask_id is not None: + self.global_state.update_subtask_status( + current_subtask_id, SubtaskStatus.REJECTED, + "Supplement collection failed") + return (ControllerState.PLAN, TriggerRole.MANAGER_SUPPLEMENT, f"SUPPLEMENT state error: {str(e)}", TriggerCode.SUPPLEMENT_ERROR) + + def handle_final_check_state(self) -> tuple[ControllerState, TriggerRole, str, TriggerCode]: + """Final quality check phase""" + logger.info("Handling FINAL_CHECK state") + + try: + # Perform final quality check + task = self.global_state.get_task() + if not task: + logger.error("No task found for final check") + return (ControllerState.DONE, TriggerRole.EVALUATOR_FINAL_CHECK, "No task found", TriggerCode.FINAL_CHECK_ERROR) + + # Check if task is impossible by looking for "Task Cannot Be Completed" subtask + all_subtasks = self.global_state.get_subtasks() + for subtask in all_subtasks: + if subtask.title == "Task Cannot Be Completed" or "cannot be completed" in subtask.title.lower(): + logger.info("Detected impossible task, rejecting task") + self.global_state.update_task_status(TaskStatus.REJECTED) + return (ControllerState.DONE, TriggerRole.EVALUATOR_FINAL_CHECK, "Task cannot be completed", TriggerCode.TASK_IMPOSSIBLE) + + # Check if there are still pending subtasks + if task.pending_subtask_ids and len(task.pending_subtask_ids) > 0: + logger.info("Still have pending subtasks, switching to GET_ACTION") + return (ControllerState.GET_ACTION, TriggerRole.EVALUATOR_FINAL_CHECK, "Still have pending subtasks", TriggerCode.FINAL_CHECK_PENDING) + + # All subtasks completed, perform final quality check + logger.info("All subtasks completed, performing final quality check") + + # Here can call evaluator for final quality check + evaluator_params = { + "global_state": self.global_state, + "tools_dict": self.tools_dict + } + evaluator = Evaluator(**evaluator_params) + + # Wait for Evaluator to complete quality check + evaluator.quality_check() + + # Check quality check results + gate_checks = self.global_state.get_gate_checks() + latest_gate = None + + for gate in gate_checks: + if not latest_gate or gate.created_at > latest_gate.created_at: + latest_gate = gate + + if latest_gate: + decision = latest_gate.decision + logger.info(f"Latest gate check decision for final check: {decision}") + if decision == GateDecision.GATE_DONE.value: + # If quality check passes, mark task as completed + self.global_state.update_task_status(TaskStatus.FULFILLED) + logger.info("Final quality check passed, task fulfilled") + # Switch to DONE state + return (ControllerState.DONE, TriggerRole.EVALUATOR_FINAL_CHECK, "Final quality check passed", TriggerCode.FINAL_CHECK_PASSED) + elif decision == GateDecision.GATE_FAIL.value: + # Final quality check failed + logger.info("Final quality check failed, task rejected") + # Switch to PLAN state + return (ControllerState.PLAN, TriggerRole.EVALUATOR_FINAL_CHECK, "Final quality check failed", TriggerCode.FINAL_CHECK_FAILED) + + # Other states, continue waiting + logger.info(f"Final quality check failed.") + return (ControllerState.PLAN, TriggerRole.EVALUATOR_FINAL_CHECK, "Final quality check failed", TriggerCode.FINAL_CHECK_FAILED) + + except Exception as e: + logger.error(f"Error in FINAL_CHECK state: {e}") + self.global_state.add_event("controller", "error", f"FINAL_CHECK state error: {str(e)}") + # Final quality check failed + return (ControllerState.PLAN, TriggerRole.EVALUATOR_FINAL_CHECK, f"Final check failed: {str(e)}", TriggerCode.FINAL_CHECK_FAILED) + + + def should_execute_immediately(self, command: CommandData) -> bool: + """Decide whether a command should be executed immediately. + Current heuristic: if action.type == 'memorize', do NOT execute immediately. + Args: + command: CommandData instance or plain dict representing a command + Returns: + True if should execute immediately, False otherwise + """ + try: + # Extract action dict from CommandData or dict + if hasattr(command, "action"): + action = getattr(command, "action", {}) or {} + elif isinstance(command, dict): + action = command.get("action", {}) or {} + else: + action = {} + + action_type = str(action.get("type", "")).strip().lower() + if action_type == "memorize": + return True + return False + except Exception: + # Be conservative: if unsure, default to execute + return False \ No newline at end of file diff --git a/mm_agents/maestro/maestro/controller/state_machine.py b/mm_agents/maestro/maestro/controller/state_machine.py new file mode 100644 index 0000000..438a192 --- /dev/null +++ b/mm_agents/maestro/maestro/controller/state_machine.py @@ -0,0 +1,115 @@ +"""State Machine for Maestro Controller +Responsible for state switching and process control +""" + +import time +import logging +from typing import Dict, Any + +from .rule_engine import RuleEngine +from .state_handlers import StateHandlers +from ..new_global_state import NewGlobalState +from ..enums import ControllerState, TriggerRole + +logger = logging.getLogger(__name__) + + +class StateMachine: + """State machine, responsible for state switching and process control""" + + def __init__(self, + global_state: NewGlobalState, + rule_engine: RuleEngine, + state_handlers: StateHandlers + ): + self.global_state = global_state + self.rule_engine = rule_engine + self.state_handlers = state_handlers + self.state_switch_count = 0 + + def get_current_state(self) -> ControllerState: + """Get current state""" + return self.global_state.get_controller_current_state() + + def switch_state(self, new_state: ControllerState, trigger_role: TriggerRole, trigger_details: str = "", trigger_code: str = "controller"): + """Switch to new state""" + if new_state == self.get_current_state(): + logger.debug(f"Already in state {new_state}") + return + + old_state = self.get_current_state() + self.state_switch_count += 1 + + # Reset repeated action counter when switching to certain states + # This prevents stale counters from affecting new state logic + if new_state in [ControllerState.PLAN, ControllerState.QUALITY_CHECK, ControllerState.FINAL_CHECK]: + self.rule_engine.reset_repeated_action_counter() + + # Record state switch event + self.global_state.add_event( + trigger_role, "state_switch", + f"State changed: {old_state} -> {new_state} (trigger_role: {trigger_role}, details: {trigger_details}, trigger_code: {trigger_code})" + ) + + # Update controller state, including trigger_code + try: + self.global_state.update_controller_state(new_state, trigger_role, trigger_details, trigger_code) + except Exception as e: + logger.warning(f"Failed to update controller state: {e}") + + logger.info( + f"State switched: {old_state} -> {new_state} " + f"(trigger_role: {trigger_role}, details: {trigger_details}, trigger_code: {trigger_code})" + ) + + def process_rules_and_update_states(self) -> None: + """Process rules and update states - called after each loop iteration""" + try: + # 1. Check task state rules + result = self.rule_engine.check_task_state_rules(self.state_switch_count) + if result: + new_state, trigger_code = result + self.switch_state(new_state, TriggerRole.CONTROLLER, f"Task state rule triggered: {new_state}", trigger_code.value) + + # 2. Check current state rules + result = self.rule_engine.check_current_state_rules() + if result: + new_state, trigger_code = result + self.switch_state(new_state, TriggerRole.CONTROLLER, f"Current state rule triggered: {new_state}", trigger_code.value) + + except Exception as e: + logger.error(f"Error in rule processing: {e}") + self.global_state.log_operation( + TriggerRole.CONTROLLER, "error", + {"error": f"Rule processing error: {str(e)}"}) + + def should_exit_loop(self) -> bool: + """Determine whether to exit the main loop""" + try: + task = self.global_state.get_task() + + if not task: + return False + task_status = task.status + + if task_status == "fulfilled": + logger.info("Task fulfilled, should exit loop") + return True + elif task_status == "rejected": + logger.info("Task rejected, should exit loop") + return True + + return False + + except Exception as e: + logger.error(f"Error checking exit condition: {e}") + return False + + def get_state_switch_count(self) -> int: + """Get state switch count""" + return self.state_switch_count + + def reset_state_switch_count(self): + """Reset state switch count""" + self.state_switch_count = 0 + logger.info("State switch count reset to 0") \ No newline at end of file diff --git a/mm_agents/maestro/maestro/data_models.py b/mm_agents/maestro/maestro/data_models.py new file mode 100644 index 0000000..0741199 --- /dev/null +++ b/mm_agents/maestro/maestro/data_models.py @@ -0,0 +1,336 @@ +""" +Data Models for Maestro Agent System +Defines core data structure models in the system +""" + +from typing import List, Optional, Dict, Any, Union +from datetime import datetime +from dataclasses import dataclass, field +from enum import Enum +import time + +from .enums import ( + TaskStatus, SubtaskStatus, GateDecision, GateTrigger, + ControllerState, ExecStatus, WorkerDecision +) + +# ========= Controller State Data Model ========= +@dataclass +class ControllerStateData: + """Controller state data structure""" + current_state: str = field(default_factory=lambda: ControllerState.GET_ACTION.value) + trigger: str = field(default="controller") + trigger_details: str = field(default="initialization") + trigger_code: str = field(default="controller") + history_state: List[str] = field(default_factory=list) + state_start_time: float = field(default_factory=time.time) + updated_at: str = field(default_factory=lambda: datetime.now().isoformat()) + + def to_dict(self) -> Dict[str, Any]: + """Convert to dictionary format""" + return { + "current_state": self.current_state, + "trigger": self.trigger, + "trigger_details": self.trigger_details, + "trigger_code": self.trigger_code, + "history_state": self.history_state, + "state_start_time": self.state_start_time, + "updated_at": self.updated_at + } + + @classmethod + def from_dict(cls, data: Dict[str, Any]) -> 'ControllerStateData': + """Create instance from dictionary""" + return cls( + current_state=data.get("current_state", ControllerState.GET_ACTION.value), + trigger=data.get("trigger", "controller"), + trigger_details=data.get("trigger_details", "initialization"), + trigger_code=data.get("trigger_code", "controller"), + history_state=data.get("history_state", []), + state_start_time=data.get("state_start_time", time.time()), + updated_at=data.get("updated_at", datetime.now().isoformat()) + ) + + +# ========= Task Data Model ========= +@dataclass +class TaskData: + """Task data structure""" + task_id: str + created_at: str = field(default_factory=lambda: datetime.now().isoformat()) + objective: str = "" + status: str = field(default_factory=lambda: TaskStatus.CREATED.value) + current_subtask_id: Optional[str] = None + step_num: int = 0 + plan_num: int = 0 # Record the number of planning attempts + history_subtask_ids: List[str] = field(default_factory=list) + pending_subtask_ids: List[str] = field(default_factory=list) + managerComplete: bool = False + qa_policy: Dict[str, Any] = field(default_factory=lambda: { + "per_subtask": True, + "final_gate": True, + "risky_actions": ["open", "submit", "hotkey"] + }) + + def to_dict(self) -> Dict[str, Any]: + """Convert to dictionary format""" + return { + "task_id": self.task_id, + "created_at": self.created_at, + "objective": self.objective, + "status": self.status, + "current_subtask_id": self.current_subtask_id, + "step_num": self.step_num, + "plan_num": self.plan_num, + "history_subtask_ids": self.history_subtask_ids, + "pending_subtask_ids": self.pending_subtask_ids, + "managerComplete": self.managerComplete, + "qa_policy": self.qa_policy + } + + @classmethod + def from_dict(cls, data: Dict[str, Any]) -> 'TaskData': + """Create instance from dictionary""" + return cls( + task_id=data["task_id"], + created_at=data.get("created_at", datetime.now().isoformat()), + objective=data.get("objective", ""), + status=data.get("status", TaskStatus.CREATED.value), + current_subtask_id=data.get("current_subtask_id"), + step_num=data.get("step_num", 0), + plan_num=data.get("plan_num", 0), + history_subtask_ids=data.get("history_subtask_ids", []), + pending_subtask_ids=data.get("pending_subtask_ids", []), + managerComplete=data.get("managerComplete", False), + qa_policy=data.get("qa_policy", { + "per_subtask": True, + "final_gate": True, + "risky_actions": ["open", "submit", "hotkey"] + }) + ) + + +# ========= Subtask Data Model ========= +@dataclass +class SubtaskData: + """Subtask data structure""" + subtask_id: str + task_id: str + title: str = "" + description: str = "" + assignee_role: str = "operator" + attempt_no: int = 1 + status: str = field(default_factory=lambda: SubtaskStatus.READY.value) + reasons_history: List[Dict[str, str]] = field(default_factory=list) + command_trace_ids: List[str] = field(default_factory=list) + gate_check_ids: List[str] = field(default_factory=list) + last_reason_text: Optional[str] = None + last_gate_decision: Optional[str] = None + created_at: str = field(default_factory=lambda: datetime.now().isoformat()) + updated_at: str = field(default_factory=lambda: datetime.now().isoformat()) + + def to_dict(self) -> Dict[str, Any]: + """Convert to dictionary format""" + return { + "subtask_id": self.subtask_id, + "task_id": self.task_id, + "title": self.title, + "description": self.description, + "assignee_role": self.assignee_role, + "attempt_no": self.attempt_no, + "status": self.status, + "reasons_history": self.reasons_history, + "command_trace_ids": self.command_trace_ids, + "gate_check_ids": self.gate_check_ids, + "last_reason_text": self.last_reason_text, + "last_gate_decision": self.last_gate_decision, + "created_at": self.created_at, + "updated_at": self.updated_at + } + + @classmethod + def from_dict(cls, data: Dict[str, Any]) -> 'SubtaskData': + """Create instance from dictionary""" + return cls( + subtask_id=data["subtask_id"], + task_id=data["task_id"], + title=data.get("title", ""), + description=data.get("description", ""), + assignee_role=data.get("assignee_role", "operator"), + attempt_no=data.get("attempt_no", 1), + status=data.get("status", SubtaskStatus.READY.value), + reasons_history=data.get("reasons_history", []), + command_trace_ids=data.get("command_trace_ids", []), + gate_check_ids=data.get("gate_check_ids", []), + last_reason_text=data.get("last_reason_text"), + last_gate_decision=data.get("last_gate_decision"), + created_at=data.get("created_at", datetime.now().isoformat()), + updated_at=data.get("updated_at", datetime.now().isoformat()) + ) + + +# ========= Command Data Model ========= +@dataclass +class CommandData: + """Command data structure""" + command_id: str + task_id: str + subtask_id: Optional[str] = None + assignee_role: str = "operator" + action: Dict[str, Any] = field(default_factory=dict) + pre_screenshot_id: Optional[str] = None + pre_screenshot_analysis: str = "" + post_screenshot_id: Optional[str] = None + worker_decision: str = field(default_factory=lambda: WorkerDecision.GENERATE_ACTION.value) + message: str = "" # Worker decision message + reason_text: str = "" # Unified reason text across decisions + exec_status: str = field(default_factory=lambda: ExecStatus.PENDING.value) + exec_message: str = "OK" + exec_latency_ms: int = 0 + created_at: str = field(default_factory=lambda: datetime.now().isoformat()) + updated_at: str = field(default_factory=lambda: datetime.now().isoformat()) + executed_at: str = field(default_factory=lambda: datetime.now().isoformat()) + + def to_dict(self) -> Dict[str, Any]: + """Convert to dictionary format""" + return { + "command_id": self.command_id, + "task_id": self.task_id, + "subtask_id": self.subtask_id, + "assignee_role": self.assignee_role, + "action": self.action, + "pre_screenshot_id": self.pre_screenshot_id, + "pre_screenshot_analysis": self.pre_screenshot_analysis, + "post_screenshot_id": self.post_screenshot_id, + "worker_decision": self.worker_decision, + "message": self.message, + "reason_text": self.reason_text, + "exec_status": self.exec_status, + "exec_message": self.exec_message, + "exec_latency_ms": self.exec_latency_ms, + "created_at": self.created_at, + "updated_at": self.updated_at, + "executed_at": self.executed_at + } + + @classmethod + def from_dict(cls, data: Dict[str, Any]) -> 'CommandData': + """Create instance from dictionary""" + return cls( + command_id=data["command_id"], + task_id=data["task_id"], + subtask_id=data.get("subtask_id"), + assignee_role=data.get("assignee_role", "operator"), + action=data.get("action", {}), + pre_screenshot_id=data.get("pre_screenshot_id"), + pre_screenshot_analysis=data.get("pre_screenshot_analysis", ""), + post_screenshot_id=data.get("post_screenshot_id"), + worker_decision=data.get("worker_decision", WorkerDecision.GENERATE_ACTION.value), + message=data.get("message", ""), + reason_text=data.get("reason_text", ""), + exec_status=data.get("exec_status", ExecStatus.PENDING.value), + exec_message=data.get("exec_message", "OK"), + exec_latency_ms=data.get("exec_latency_ms", 0), + created_at=data.get("created_at", datetime.now().isoformat()), + updated_at=data.get("updated_at", datetime.now().isoformat()), + executed_at=data.get("executed_at", datetime.now().isoformat()) + ) + + +# ========= Gate Check Data Model ========= +@dataclass +class GateCheckData: + """Gate check data structure""" + gate_check_id: str + task_id: str + subtask_id: Optional[str] = None + trigger: str = field(default_factory=lambda: GateTrigger.PERIODIC_CHECK.value) + decision: Optional[str] = None + notes: str = "" + created_at: str = field(default_factory=lambda: datetime.now().isoformat()) + + def to_dict(self) -> Dict[str, Any]: + """Convert to dictionary format""" + return { + "gate_check_id": self.gate_check_id, + "task_id": self.task_id, + "subtask_id": self.subtask_id, + "trigger": self.trigger, + "decision": self.decision, + "notes": self.notes, + "created_at": self.created_at + } + + @classmethod + def from_dict(cls, data: Dict[str, Any]) -> 'GateCheckData': + """Create instance from dictionary""" + return cls( + gate_check_id=data["gate_check_id"], + task_id=data["task_id"], + subtask_id=data.get("subtask_id"), + trigger=data.get("trigger", GateTrigger.PERIODIC_CHECK.value), + decision=data.get("decision"), + notes=data.get("notes", ""), + created_at=data.get("created_at", datetime.now().isoformat()) + ) + + +# ========= Factory Functions ========= +def create_task_data(task_id: str, objective: str = "") -> TaskData: + """Create new task data""" + return TaskData( + task_id=task_id, + objective=objective, + status=TaskStatus.CREATED.value + ) + + +def create_subtask_data(subtask_id: str, task_id: str, title: str, description: str, + assignee_role: str = "operator") -> SubtaskData: + """Create new subtask data""" + return SubtaskData( + subtask_id=subtask_id, + task_id=task_id, + title=title, + description=description, + assignee_role=assignee_role, + status=SubtaskStatus.READY.value + ) + + +def create_command_data(command_id: str, task_id: str, action: Dict[str, Any], + subtask_id: Optional[str] = None, assignee_role: str = "") -> CommandData: + """Create new command data""" + return CommandData( + command_id=command_id, + task_id=task_id, + subtask_id=subtask_id, + assignee_role=assignee_role, + action=action, + exec_status=ExecStatus.PENDING.value + ) + + +def create_gate_check_data(gate_check_id: str, task_id: str, decision: str, + subtask_id: Optional[str] = None, notes: str = "", + trigger: str = GateTrigger.PERIODIC_CHECK.value) -> GateCheckData: + """Create new gate check data""" + return GateCheckData( + gate_check_id=gate_check_id, + task_id=task_id, + subtask_id=subtask_id, + decision=decision, + notes=notes, + trigger=trigger + ) + + +def create_controller_state_data(state: ControllerState = ControllerState.GET_ACTION, + trigger: str = "controller", + trigger_details: str = "initialization") -> ControllerStateData: + """Create new controller state data""" + return ControllerStateData( + current_state=state.value, + trigger=trigger, + trigger_details=trigger_details + ) \ No newline at end of file diff --git a/mm_agents/maestro/maestro/debug_system/__init__.py b/mm_agents/maestro/maestro/debug_system/__init__.py new file mode 100644 index 0000000..4bc0896 --- /dev/null +++ b/mm_agents/maestro/maestro/debug_system/__init__.py @@ -0,0 +1,10 @@ +""" +Debug System - Support restoring state from snapshots and step-by-step debugging of all core components +""" + +from .component_debugger import ComponentDebugger +from .main_debugger import MainDebugger, create_debugger + +__all__ = ['SnapshotDebugger', 'ComponentDebugger', 'MainDebugger', 'create_debugger'] + +__version__ = "1.0.0" \ No newline at end of file diff --git a/mm_agents/maestro/maestro/debug_system/component_debugger.py b/mm_agents/maestro/maestro/debug_system/component_debugger.py new file mode 100644 index 0000000..4b7479b --- /dev/null +++ b/mm_agents/maestro/maestro/debug_system/component_debugger.py @@ -0,0 +1,123 @@ +""" +Component Debugger - Support debugging core components like Worker, Evaluator, Manager, etc. +""" + +from typing import Dict, Any, Optional + +from pyautogui import screenshot + +from ..controller.state_handlers import NewWorker, Evaluator +from ..controller.main_controller import NewManager + + +class ComponentDebugger: + """Component Debugger - Support debugging various core components""" + + def __init__(self, snapshot_debugger): + self.snapshot_debugger: SnapshotDebugger = snapshot_debugger + self.current_worker = None + self.current_evaluator = None + self.current_manager = None + + def debug_worker(self, snapshot_id: str) -> Optional[NewWorker]: + """Debug Worker component""" + if not self.snapshot_debugger.load_snapshot(snapshot_id): + return None + + try: + worker_params = self.snapshot_debugger.get_worker_params() + self.current_worker = NewWorker(**worker_params) + self.current_worker.process_subtask_and_create_command() + print(f"Worker component created, ready to debug snapshot: {snapshot_id}") + return self.current_worker + + except Exception as e: + print(f"Failed to create Worker component: {e}") + return None + + def debug_evaluator(self, snapshot_id: str) -> Optional[Evaluator]: + """Debug Evaluator component""" + if not self.snapshot_debugger.load_snapshot(snapshot_id): + return None + + try: + evaluator_params = self.snapshot_debugger.get_evaluator_params() + self.current_evaluator = Evaluator(**evaluator_params) + print(f"Evaluator component created, ready to debug snapshot: {snapshot_id}") + return self.current_evaluator + + except Exception as e: + print(f"Failed to create Evaluator component: {e}") + return None + + def debug_manager(self, snapshot_id: str) -> Optional[NewManager]: + """Debug Manager component""" + if not self.snapshot_debugger.load_snapshot(snapshot_id): + return None + + try: + screenshot = self.snapshot_debugger.global_state.get_screenshot() + print(f"screenshot: {screenshot.__sizeof__()}") + manager_params = self.snapshot_debugger.get_manager_params() + self.current_manager = NewManager(**manager_params) + self.current_manager.plan_task("replan") + print(f"Manager component created, ready to debug snapshot: {snapshot_id}") + return self.current_manager + + except Exception as e: + print(f"Failed to create Manager component: {e}") + return None + + def step_worker(self) -> bool: + """Step execute Worker""" + if not self.current_worker: + print("Please create Worker component first") + return False + + try: + print("Executing Worker step debugging...") + self.current_worker.process_subtask_and_create_command() + print("Worker step execution completed") + return True + + except Exception as e: + print(f"Worker execution failed: {e}") + return False + + def get_worker_state(self) -> Dict[str, Any]: + """Get current Worker state""" + if not self.current_worker: + return {} + + # Here you can get state information based on the actual structure of Worker + return { + "worker_created": True, + "global_state": self.snapshot_debugger.global_state.get_current_state_summary() + } + + def get_evaluator_state(self) -> Dict[str, Any]: + """Get current Evaluator state""" + if not self.current_evaluator: + return {} + + return { + "evaluator_created": True, + "global_state": self.snapshot_debugger.global_state.get_current_state_summary() + } + + def get_manager_state(self) -> Dict[str, Any]: + """Get current Manager state""" + if not self.current_manager: + return {} + + return { + "manager_created": True, + "global_state": self.snapshot_debugger.global_state.get_current_state_summary() + } + + def reset_debug_session(self): + """Reset debug session""" + self.current_worker = None + self.current_evaluator = None + self.current_manager = None + print("Debug session has been reset") \ No newline at end of file diff --git a/mm_agents/maestro/maestro/debug_system/debug_runtime.py b/mm_agents/maestro/maestro/debug_system/debug_runtime.py new file mode 100644 index 0000000..4e06aaf --- /dev/null +++ b/mm_agents/maestro/maestro/debug_system/debug_runtime.py @@ -0,0 +1,63 @@ +#!/usr/bin/env python3 +"""Minimal debug function: avoid nested layers and redundant error handling""" +from typing import Optional + +from .logging_setup import setup_file_logging +from .main_debugger import MainDebugger, create_debugger + + +def run_debug(runtime_path: str, snapshot_id: str, component: str = "manager") -> None: + debugger = create_debugger( + runtime_path=runtime_path, + snapshot_id=snapshot_id, + ) + + target_path = debugger.target_path + setup_file_logging(str(debugger.target_path)) + + if component == "manager": + debugger.debug_manager() + elif component == "worker": + debugger.debug_worker() + elif component == "evaluator": + debugger.debug_evaluator() + else: + raise ValueError(f"Unknown component: {component}") + + +if __name__ == "__main__": + import argparse + import logging + + from .logging_setup import setup_debug_logging + + setup_debug_logging(logging.INFO) + + parser = argparse.ArgumentParser(description="Run snapshot/component debug easily from CLI") + parser.add_argument( + "--runtime", + default="runtime/20250826_141730", + help="Runtime directory path (default: runtime/20250826_141730)", + ) + parser.add_argument( + "--snapshot", + default="snapshot_20250826_141736", + help="Snapshot id/name (default: snapshot_20250826_141736)", + ) + parser.add_argument( + "--component", + choices=["manager", "worker", "evaluator"], + default="manager", + help="Which component to debug (default: manager)", + ) + + # Hardcoded runtime path and snapshot name + runtime_path = "runtime/20250826_141730" + snapshot_id = "snapshot_20250826_141736" + component = "manager" + # args = parser.parse_args() + run_debug( + runtime_path=runtime_path, + snapshot_id=snapshot_id, + component=component + ) \ No newline at end of file diff --git a/mm_agents/maestro/maestro/debug_system/example_usage.py b/mm_agents/maestro/maestro/debug_system/example_usage.py new file mode 100644 index 0000000..b765968 --- /dev/null +++ b/mm_agents/maestro/maestro/debug_system/example_usage.py @@ -0,0 +1,39 @@ +""" +Debug System Usage Example +""" + +from .main_debugger import create_debugger + + +def main(): + """Main function - Demonstrate the usage of debug system""" + + # Create debugger + debugger = create_debugger() + + print("=== Agent Debug System ===") + print("This system allows you to restore state from snapshots and debug various components") + + # List available snapshots + snapshots = debugger.list_snapshots() # type: ignore + if snapshots: + print(f"\nFound {len(snapshots)} snapshots:") + for snapshot in snapshots: + print(f" - {snapshot}") + + # Show information of the first snapshot + first_snapshot = snapshots[0] + debugger.show_snapshot_info(first_snapshot) # type: ignore + debugger.debug_worker_from_snapshot(first_snapshot) # type: ignore + + else: + print("No available snapshots") + print("Please run the system first to create some snapshots") + + # Start interactive debug mode + print("\nStarting interactive debug mode...") + debugger.interactive_debug() # type: ignore + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/mm_agents/maestro/maestro/debug_system/logging_setup.py b/mm_agents/maestro/maestro/debug_system/logging_setup.py new file mode 100644 index 0000000..1764769 --- /dev/null +++ b/mm_agents/maestro/maestro/debug_system/logging_setup.py @@ -0,0 +1,74 @@ +import logging +import os +import sys +from pathlib import Path + + +def setup_debug_logging(level: int = logging.INFO) -> None: + """Configure root logger to output to stdout with a readable format. + + This mirrors the CLI app's behavior in a lightweight way so debug scripts + emit logs without duplicating full file handler setup. + """ + root_logger = logging.getLogger() + if root_logger.handlers: + # Already configured; just ensure level + root_logger.setLevel(level) + return + + root_logger.setLevel(level) + + formatter = logging.Formatter( + fmt="\x1b[1;33m[%(asctime)s \x1b[31m%(levelname)s \x1b[32m%(module)s/%(lineno)d-%(processName)s\x1b[1;33m] \x1b[0m%(message)s" + ) + + stdout_handler = logging.StreamHandler(sys.stdout) + stdout_handler.setLevel(level) + stdout_handler.setFormatter(formatter) + + root_logger.addHandler(stdout_handler) + + +def setup_file_logging(log_dir: str, level_info: int = logging.INFO, level_debug: int = logging.DEBUG) -> None: + """Attach file handlers writing logs under log_dir. + + Creates two files similar to CLI app: + - agents3.log (INFO) + - agents3_debug.log (DEBUG) + """ + Path(log_dir).mkdir(parents=True, exist_ok=True) + + formatter = logging.Formatter( + fmt="\x1b[1;33m[%(asctime)s \x1b[31m%(levelname)s \x1b[32m%(module)s/%(lineno)d-%(processName)s\x1b[1;33m] \x1b[0m%(message)s" + ) + + file_info = logging.FileHandler(os.path.join(log_dir, "agents3.log"), encoding="utf-8") + file_info.setLevel(level_info) + file_info.setFormatter(formatter) + + file_debug = logging.FileHandler(os.path.join(log_dir, "agents3_debug.log"), encoding="utf-8") + file_debug.setLevel(level_debug) + file_debug.setFormatter(formatter) + + root_logger = logging.getLogger() + root_logger.addHandler(file_info) + root_logger.addHandler(file_debug) + setup_doubao_api_logging(log_dir, level_debug) + + +def setup_doubao_api_logging(log_dir: str, level: int = logging.DEBUG) -> logging.Logger: + """Create a dedicated doubao_api logger writing to doubao_api.log under log_dir.""" + Path(log_dir).mkdir(parents=True, exist_ok=True) + + formatter = logging.Formatter( + fmt="\x1b[1;33m[%(asctime)s \x1b[31m%(levelname)s \x1b[32m%(module)s/%(lineno)d-%(processName)s\x1b[1;33m] \x1b[0m%(message)s" + ) + + handler = logging.FileHandler(os.path.join(log_dir, "doubao_api.log"), encoding="utf-8") + handler.setLevel(level) + handler.setFormatter(formatter) + + doubao_logger = logging.getLogger("doubao_api") + doubao_logger.setLevel(level) + doubao_logger.addHandler(handler) + return doubao_logger \ No newline at end of file diff --git a/mm_agents/maestro/maestro/debug_system/main_debugger.py b/mm_agents/maestro/maestro/debug_system/main_debugger.py new file mode 100644 index 0000000..9fe9cd2 --- /dev/null +++ b/mm_agents/maestro/maestro/debug_system/main_debugger.py @@ -0,0 +1,99 @@ +""" +Main Debugger - Integrate snapshot debugging and component debugging functionality +""" +import os +import shutil +import json +from datetime import datetime +from pathlib import Path +from typing import Optional, Dict, Any + +from ..snapshot_restorer import restore_snapshot_and_create_globalstate +from .component_debugger import ComponentDebugger +from ..controller.state_handlers import NewWorker, Evaluator +from ..controller.main_controller import NewManager + + +class MainDebugger: + """Main Debugger - Provide complete debugging functionality""" + + def __init__( + self, + runtime_path: str = "runtime/20250826_141730", + snapshot_id: str = "snapshot_20250826_141736", + target_dir: Optional[str] = None, + ): + target_runtime = '' + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + runtime_dir = Path(runtime_path) + target_runtime = runtime_dir.parent / f"{runtime_dir.name}_restored_from_{snapshot_id}_{timestamp}" + self.snapshot_id=snapshot_id + self.global_state, self.target_path, self.config_params = restore_snapshot_and_create_globalstate( + runtime_dir=runtime_path, + snapshot_id=snapshot_id, + target_dir=str(target_runtime), + ) + + def get_worker_params(self) -> Dict[str, Any]: + """Get Worker parameters""" + + config_params = self.config_params + + # Extract Worker required parameters from snapshot + worker_params = { + "tools_dict": config_params.get('tools_dict', {}), + "global_state": self.global_state, + "platform": config_params.get('platform', 'default'), + "enable_search": config_params.get('enable_search', True), + "client_password": config_params.get('client_password', 'osworld-public-evaluation') + } + + return worker_params + + def get_evaluator_params(self) -> Dict[str, Any]: + """Get Evaluator parameters""" + config_params = self.config_params + return { + "global_state": self.global_state, + "tools_dict": config_params.get('tools_dict', {}), + } + + def get_manager_params(self) -> Dict[str, Any]: + """Get Manager parameters""" + config_params = self.config_params + return { + "tools_dict": config_params.get('tools_dict', {}), + "global_state": self.global_state, + "local_kb_path": config_params.get('local_kb_path', ''), + "platform": config_params.get('platform', 'default'), + "enable_search": config_params.get('enable_search', True), + } + + # === Minimal interface: create and execute directly === + def debug_worker(self): + worker_params = self.get_worker_params() + current_worker = NewWorker(**worker_params) + current_worker.process_subtask_and_create_command() + print(f"Worker component created, ready to debug snapshot: {self.snapshot_id}") + + def debug_evaluator(self): + evaluator_params = self.get_evaluator_params() + current_evaluator = Evaluator(**evaluator_params) + print(f"Evaluator component created, ready to debug snapshot: {self.snapshot_id}") + return current_evaluator + + def debug_manager(self): + manager_params = self.get_manager_params() + current_manager = NewManager(**manager_params) + current_manager.plan_task("replan") + print(f"Manager component created, ready to debug snapshot: {self.snapshot_id}") + return current_manager + + +def create_debugger(snapshot_id: str = "snapshot_20250826_141736", runtime_path: str = "runtime/20250826_141730") -> MainDebugger: + """Compatible with old interface: create MainDebugger instance with old parameter names + - snapshot_dir: compatible old name, actually snapshot_id + - state_dir: compatible old name, actually runtime_path + """ + return MainDebugger(runtime_path=runtime_path, snapshot_id=snapshot_id) + \ No newline at end of file diff --git a/mm_agents/maestro/maestro/debug_system/run_from_snapshot.py b/mm_agents/maestro/maestro/debug_system/run_from_snapshot.py new file mode 100644 index 0000000..690920a --- /dev/null +++ b/mm_agents/maestro/maestro/debug_system/run_from_snapshot.py @@ -0,0 +1,132 @@ +#!/usr/bin/env python3 +# python gui_agents/maestro/debug_system/run_from_snapshot.py +# python -m gui_agents.maestro.debug_system.run_from_snapshot +""" +Debug helper: restore MainController from a snapshot and optionally run it. +""" + + +import argparse +import logging +import time +from typing import Optional, List, Any + +from ..data_models import CommandData +from ..new_global_state import NewGlobalState +from ..snapshot_restorer import ( + restore_maincontroller_from_globalstate, +) +from .logging_setup import setup_file_logging + +logger = logging.getLogger(__name__) + + +def _extract_commands_sorted(controller) -> List[CommandData]: + """Get all commands (CommandData) and sort them in ascending order by created_at.""" + gs: NewGlobalState = getattr(controller, "global_state") + if gs is None or not hasattr(gs, "get_commands"): + return [] + try: + cmds: List[CommandData] = gs.get_commands() or [] + except Exception: + return [] + try: + return sorted(cmds, key=lambda c: getattr(c, "created_at", 0)) + except Exception: + return cmds + + +def run_main_controller_from_snapshot( + runtime_dir: str, + snapshot_id: Optional[str] = None, + target_dir: Optional[str] = None, + os_word_task_id: Optional[str] = None, +): + """ + Restore a MainController from an existing snapshot and optionally run it. + + Args: + runtime_dir: Path to the runtime directory (e.g., "runtime/20250826_141730") + snapshot_id: Snapshot ID (e.g., "snapshot_20250826_141736"); if None, interactive selection + target_dir: Restore target directory; if None, it will be generated automatically + auto_run: Whether to immediately execute the main loop + + Returns: + The restored MainController if successful; otherwise None. + """ + result = restore_maincontroller_from_globalstate( + runtime_dir=runtime_dir, + snapshot_id=snapshot_id, + target_dir=target_dir, + os_word_task_id=os_word_task_id + ) + if result is None: + logger.error("Failed to restore MainController from snapshot") + return None + + controller, target_path, config_params = result + + # Write file logs to target_path + setup_file_logging(target_path) + + logger.info(f"MainController restored from snapshot successfully. Logs at: {target_path}") + + # Execute commands in chronological order before running the main loop, with 5-second intervals + try: + commands = _extract_commands_sorted(controller) + if commands: + logger.info(f"Pre-executing {len(commands)} commands before main loop...") + for idx, cmd in enumerate(commands, 1): + try: + logger.info(f"[PreExec {idx}/{len(commands)}] Executing command_id={getattr(cmd, 'command_id', 'N/A')}") + executor = getattr(controller, "executor", None) + if executor is not None and hasattr(executor, "execute_command"): + executor.execute_command(cmd) # Execute only, do not touch global_state + else: + logger.warning("Controller has no executor.execute_command; skipping pre-exec") + break + except Exception as e: + logger.warning(f"Pre-exec command failed: {e}") + time.sleep(5) + except Exception as e: + logger.warning(f"Failed to pre-execute commands: {e}") + + controller.execute_main_loop() + + return controller + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Run MainController from snapshot") + parser.add_argument("runtime_dir", type=str, help="Path to runtime directory") + parser.add_argument("--snapshot", "-s", type=str, default=None, help="Snapshot ID") + parser.add_argument("--target", "-t", type=str, default=None, help="Restore target directory") + parser.add_argument("--no-run", action="store_true", help="Do not auto-run main loop") + return parser.parse_args() + + +if __name__ == "__main__": + import logging + from .logging_setup import setup_debug_logging, setup_file_logging + + # Simple console logging + setup_debug_logging(logging.INFO) + + # Write to target_path log file (example with default test target directory) + # If using CLI arguments, call setup_file_logging(args.target or target_path) after parsing + + # args = _parse_args() + + target_dir=None + runtime_dir = "runtime/vmrun_20250831_003040/20250831_003117" + snapshot_id = "snapshot_20250831_004442" + # runtime_dir = "runtime/20250828_141244" + # snapshot_id = "snapshot_20250828_141320" + os_word_task_id = "535364ea-05bd-46ea-9937-9f55c68507e8" + + controller = run_main_controller_from_snapshot( + runtime_dir=runtime_dir, + snapshot_id=snapshot_id, + target_dir=target_dir, + os_word_task_id=os_word_task_id + ) diff --git a/mm_agents/maestro/maestro/debug_system/test_dataimpulse_proxy.py b/mm_agents/maestro/maestro/debug_system/test_dataimpulse_proxy.py new file mode 100644 index 0000000..f682aa5 --- /dev/null +++ b/mm_agents/maestro/maestro/debug_system/test_dataimpulse_proxy.py @@ -0,0 +1,224 @@ +#!/usr/bin/env python3 +# Test script to verify Dataimpulse proxy connectivity + +import requests +import json +import time +from urllib.parse import urlparse + +def load_proxy_config(): + """Load proxy configuration from the JSON file""" + try: + with open('/home/lxguo/osworld/evaluation_examples/settings/proxy/dataimpulse.json', 'r') as f: + proxy_configs = json.load(f) + # Return the first proxy configuration if it's a list + if isinstance(proxy_configs, list) and len(proxy_configs) > 0: + return proxy_configs[0] + elif isinstance(proxy_configs, dict): + return proxy_configs + else: + print("Invalid proxy configuration format") + return None + except Exception as e: + print(f"Error loading proxy config: {e}") + return None + +def format_proxy_url(proxy_config): + """Format proxy URL with authentication""" + if proxy_config['username'] and proxy_config['password']: + return f"http://{proxy_config['username']}:{proxy_config['password']}@{proxy_config['host']}:{proxy_config['port']}" + else: + return f"http://{proxy_config['host']}:{proxy_config['port']}" + +def test_proxy_connectivity(proxy_url, test_urls=None): + """Test proxy connectivity with multiple URLs""" + if test_urls is None: + test_urls = [ + 'http://httpbin.org/ip', + 'https://httpbin.org/ip', + 'https://www.google.com', + 'https://www.flightaware.com', + 'https://www.discussions.flightaware.com' + ] + + proxies = { + 'http': proxy_url, + 'https': proxy_url + } + + results = [] + + for url in test_urls: + print(f"\nTesting {url}...") + try: + start_time = time.time() + response = requests.get(url, proxies=proxies, timeout=30) + end_time = time.time() + + result = { + 'url': url, + 'status_code': response.status_code, + 'response_time': round(end_time - start_time, 2), + 'success': True, + 'error': None + } + + if 'httpbin.org/ip' in url: + try: + ip_info = response.json() + result['origin_ip'] = ip_info.get('origin', 'Unknown') + print(f" ✓ Success! Status: {response.status_code}, Time: {result['response_time']}s, IP: {result['origin_ip']}") + except: + print(f" ✓ Success! Status: {response.status_code}, Time: {result['response_time']}s") + else: + print(f" ✓ Success! Status: {response.status_code}, Time: {result['response_time']}s") + + except requests.exceptions.ProxyError as e: + result = { + 'url': url, + 'status_code': None, + 'response_time': None, + 'success': False, + 'error': f"Proxy Error: {str(e)}" + } + print(f" ✗ Proxy Error: {e}") + + except requests.exceptions.Timeout as e: + result = { + 'url': url, + 'status_code': None, + 'response_time': None, + 'success': False, + 'error': f"Timeout: {str(e)}" + } + print(f" ✗ Timeout: {e}") + + except Exception as e: + result = { + 'url': url, + 'status_code': None, + 'response_time': None, + 'success': False, + 'error': f"Error: {str(e)}" + } + print(f" ✗ Error: {e}") + + results.append(result) + + return results + +def test_direct_connection(test_urls=None): + """Test direct connection without proxy for comparison""" + if test_urls is None: + test_urls = [ + 'http://httpbin.org/ip', + 'https://httpbin.org/ip' + ] + + print("\n=== Testing Direct Connection (No Proxy) ===") + results = [] + + for url in test_urls: + print(f"\nTesting {url} (direct)...") + try: + start_time = time.time() + response = requests.get(url, timeout=10) + end_time = time.time() + + result = { + 'url': url, + 'status_code': response.status_code, + 'response_time': round(end_time - start_time, 2), + 'success': True, + 'error': None + } + + if 'httpbin.org/ip' in url: + try: + ip_info = response.json() + result['origin_ip'] = ip_info.get('origin', 'Unknown') + print(f" ✓ Success! Status: {response.status_code}, Time: {result['response_time']}s, IP: {result['origin_ip']}") + except: + print(f" ✓ Success! Status: {response.status_code}, Time: {result['response_time']}s") + else: + print(f" ✓ Success! Status: {response.status_code}, Time: {result['response_time']}s") + + except Exception as e: + result = { + 'url': url, + 'status_code': None, + 'response_time': None, + 'success': False, + 'error': f"Error: {str(e)}" + } + print(f" ✗ Error: {e}") + + results.append(result) + + return results + +def main(): + print("=== Dataimpulse Proxy Connectivity Test ===") + + # Load proxy configuration + proxy_config = load_proxy_config() + if not proxy_config: + print("Failed to load proxy configuration") + return + + print(f"\nProxy Configuration:") + print(f" Host: {proxy_config['host']}") + print(f" Port: {proxy_config['port']}") + print(f" Username: {proxy_config['username']}") + print(f" Provider: {proxy_config.get('provider', 'Unknown')}") + print(f" Type: {proxy_config.get('type', 'Unknown')}") + print(f" Country: {proxy_config.get('country', 'Unknown')}") + + # Format proxy URL + proxy_url = format_proxy_url(proxy_config) + print(f"\nFormatted Proxy URL: {proxy_url}") + + # Test direct connection first + direct_results = test_direct_connection() + + # Test proxy connectivity + print("\n=== Testing Proxy Connection ===") + proxy_results = test_proxy_connectivity(proxy_url) + + # Summary + print("\n=== SUMMARY ===") + + print("\nDirect Connection Results:") + for result in direct_results: + status = "✓" if result['success'] else "✗" + print(f" {status} {result['url']}: {result.get('error', 'Success')}") + + print("\nProxy Connection Results:") + successful = 0 + total = len(proxy_results) + + for result in proxy_results: + status = "✓" if result['success'] else "✗" + if result['success']: + successful += 1 + print(f" {status} {result['url']}: Success ({result['response_time']}s)") + else: + print(f" {status} {result['url']}: {result['error']}") + + print(f"\nProxy Success Rate: {successful}/{total} ({successful/total*100:.1f}%)") + + if successful == 0: + print("\n⚠️ WARNING: Proxy is not working at all!") + print(" Possible issues:") + print(" 1. Incorrect credentials") + print(" 2. Proxy server is down") + print(" 3. IP whitelist restrictions") + print(" 4. Network connectivity issues") + elif successful < total: + print(f"\n⚠️ WARNING: Proxy partially working ({successful}/{total} sites accessible)") + print(" Some sites may be blocked or have connectivity issues") + else: + print("\n✅ Proxy is working correctly for all tested sites!") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/mm_agents/maestro/maestro/debug_system/test_hosting_difference.py b/mm_agents/maestro/maestro/debug_system/test_hosting_difference.py new file mode 100644 index 0000000..a0237fd --- /dev/null +++ b/mm_agents/maestro/maestro/debug_system/test_hosting_difference.py @@ -0,0 +1,174 @@ +#!/usr/bin/env python3 +""" +Test script to verify the hosting difference between FlightAware main site and discussions +""" + +import requests +import json +from urllib.parse import urlparse + +def load_proxy_config(): + """Load proxy configuration from dataimpulse.json""" + try: + with open('/home/lxguo/osworld/evaluation_examples/settings/proxy/dataimpulse.json', 'r') as f: + proxy_configs = json.load(f) + # dataimpulse.json contains an array, get the first proxy + if isinstance(proxy_configs, list) and len(proxy_configs) > 0: + return proxy_configs[0] + else: + return proxy_configs + except Exception as e: + print(f"Error loading proxy config: {e}") + return None + +def get_proxy_dict(proxy_config): + """Convert proxy config to requests proxy format""" + if not proxy_config: + return None + + proxy_url = f"http://{proxy_config['username']}:{proxy_config['password']}@{proxy_config['host']}:{proxy_config['port']}" + return { + 'http': proxy_url, + 'https': proxy_url + } + +def test_site_hosting(url, use_proxy=False, proxy_dict=None): + """Test a site and analyze its hosting infrastructure""" + print(f"\n=== Testing {url} ===\n") + + try: + # Test with HEAD request to get headers without downloading content + response = requests.head( + url, + proxies=proxy_dict if use_proxy else None, + timeout=30, + allow_redirects=True + ) + + print(f"Status Code: {response.status_code}") + print(f"Final URL: {response.url}") + + # Analyze hosting infrastructure + headers = response.headers + + # Check for Cloudflare + cloudflare_indicators = [ + 'server' in headers and 'cloudflare' in headers['server'].lower(), + any('cf-' in key.lower() for key in headers.keys()), + any('__cf' in cookie for cookie in headers.get('set-cookie', '').split(';')) + ] + + if any(cloudflare_indicators): + print("🔵 Hosting: Cloudflare detected") + if 'server' in headers: + print(f" Server: {headers['server']}") + else: + print("🟡 Hosting: Non-Cloudflare") + if 'server' in headers: + print(f" Server: {headers['server']}") + + # Check for Discourse + discourse_indicators = [ + 'cdck-proxy-id' in headers, + 'discourse' in headers.get('server', '').lower(), + 'hosted-by-discourse' in response.url + ] + + if any(discourse_indicators): + print("💬 Platform: Discourse forum detected") + if 'cdck-proxy-id' in headers: + print(f" Discourse Proxy: {headers['cdck-proxy-id']}") + + # Check for bot protection indicators + protection_indicators = [ + 'cf-ray' in headers, + 'cf-cache-status' in headers, + any('challenge' in key.lower() for key in headers.keys()) + ] + + if any(protection_indicators): + print("🛡️ Bot Protection: Likely present") + + return True, response.status_code + + except requests.exceptions.ProxyError as e: + print(f"❌ Proxy Error: {e}") + return False, None + except requests.exceptions.RequestException as e: + print(f"❌ Request Error: {e}") + return False, None + +def main(): + print("FlightAware Hosting Infrastructure Analysis") + print("=" * 50) + + # Load proxy configuration + proxy_config = load_proxy_config() + if proxy_config: + print(f"✅ Loaded proxy: {proxy_config['host']}:{proxy_config['port']}") + proxy_dict = get_proxy_dict(proxy_config) + else: + print("❌ Failed to load proxy configuration") + return + + # Test URLs + test_urls = [ + "https://www.flightaware.com", + "https://discussions.flightaware.com", + "https://flightaware.hosted-by-discourse.com" + ] + + results = {} + + for url in test_urls: + print(f"\n{'='*60}") + print(f"Testing: {url}") + print(f"{'='*60}") + + # Test direct connection + print("\n--- Direct Connection ---") + direct_success, direct_status = test_site_hosting(url, use_proxy=False) + + # Test proxy connection + print("\n--- Proxy Connection ---") + proxy_success, proxy_status = test_site_hosting(url, use_proxy=True, proxy_dict=proxy_dict) + + results[url] = { + 'direct': {'success': direct_success, 'status': direct_status}, + 'proxy': {'success': proxy_success, 'status': proxy_status} + } + + # Summary + print(f"\n\n{'='*60}") + print("SUMMARY") + print(f"{'='*60}") + + for url, result in results.items(): + print(f"\n{url}:") + print(f" Direct: {'✅' if result['direct']['success'] else '❌'} (Status: {result['direct']['status']})") + print(f" Proxy: {'✅' if result['proxy']['success'] else '❌'} (Status: {result['proxy']['status']})") + + # Analysis + print(f"\n\n{'='*60}") + print("ANALYSIS") + print(f"{'='*60}") + + main_site_proxy_works = results["https://www.flightaware.com"]['proxy']['success'] + discussions_proxy_works = results["https://discussions.flightaware.com"]['proxy']['success'] + + if main_site_proxy_works and not discussions_proxy_works: + print("\n🔍 Key Finding:") + print(" - Main site (Cloudflare-protected) works with proxy") + print(" - Discussions (Discourse-hosted) fails with proxy") + print(" - This suggests Discourse hosting has stricter proxy detection") + print(" - Different hosting infrastructure = different proxy policies") + elif not main_site_proxy_works and not discussions_proxy_works: + print("\n🔍 Key Finding:") + print(" - Both sites fail with proxy") + print(" - This suggests general proxy blocking") + else: + print("\n🔍 Key Finding:") + print(" - Proxy behavior is consistent across both sites") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/mm_agents/maestro/maestro/debug_system/test_html_parsing.py b/mm_agents/maestro/maestro/debug_system/test_html_parsing.py new file mode 100644 index 0000000..937f347 --- /dev/null +++ b/mm_agents/maestro/maestro/debug_system/test_html_parsing.py @@ -0,0 +1,169 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Test script to verify HTML parsing logic for Steam cart evaluation +""" + +import json +import sys +import os + +# Add the osworld directory to Python path +sys.path.insert(0, '/home/lxguo/osworld') + +from desktop_env.evaluators.metrics.chrome import is_added_to_steam_cart +from desktop_env.evaluators.getters.chrome import get_page_info + +def test_html_parsing(): + """ + Test the HTML parsing logic for Steam cart evaluation using real HTML file + """ + print("=== Testing HTML Parsing Logic with Real HTML ===") + + # Load the evaluation configuration + config_path = '/home/lxguo/osworld/evaluation_examples/examples/chrome/121ba48f-9e17-48ce-9bc6-a4fb17a7ebba.json' + + try: + with open(config_path, 'r') as f: + config = json.load(f) + print(f"✓ Loaded configuration from {config_path}") + except Exception as e: + print(f"✗ Failed to load configuration: {e}") + return + + # Extract evaluation parameters + evaluator = config['evaluator'] + expected_items = evaluator['expected']['rules']['items'] + target_url = evaluator['result']['url'] + + print(f"Target URL: {target_url}") + print(f"Expected items: {expected_items}") + + # Load real HTML content from file + real_html_path = '/home/lxguo/osworld/real_cart.html' + try: + with open(real_html_path, 'r', encoding='utf-8') as f: + real_html_content = f.read() + print(f"✓ Loaded real HTML from {real_html_path}") + print(f"HTML content length: {len(real_html_content)} characters") + except Exception as e: + print(f"✗ Failed to load real HTML: {e}") + return + + # Simulate active_tab_info structure with real HTML + real_active_tab_info = { + 'title': 'Shopping Cart', + 'url': target_url, + 'content': real_html_content + } + + # Test the evaluation function with real HTML + rule = {'items': expected_items} + + print("\n=== Testing is_added_to_steam_cart function with Real HTML ===") + print(f"Rule: {rule}") + + # Check if the expected item is in the real HTML content + print("\n--- Searching for expected items in real HTML ---") + for item in expected_items: + if item in real_html_content: + print(f"✓ Found '{item}' in real HTML content") + else: + print(f"✗ '{item}' NOT found in real HTML content") + + # Try case-insensitive search + if item.lower() in real_html_content.lower(): + print(f" ℹ Found '{item}' with case-insensitive search") + else: + print(f" ℹ '{item}' not found even with case-insensitive search") + + # Try searching for individual words + words = item.split() + found_words = [] + for word in words: + if word.lower() in real_html_content.lower(): + found_words.append(word) + if found_words: + print(f" ℹ Found individual words: {found_words}") + else: + print(f" ℹ No individual words from '{item}' found") + + # Call the actual evaluation function + try: + result = is_added_to_steam_cart(real_active_tab_info, rule) + print(f"\nEvaluation result with real HTML: {result}") + + if result == 1.0: + print("✓ Evaluation PASSED - All items found in real HTML") + else: + print("✗ Evaluation FAILED - Some items missing in real HTML") + + except Exception as e: + print(f"✗ Error during evaluation: {e}") + import traceback + traceback.print_exc() + + # Additional analysis of the real HTML content + print("\n=== Additional Analysis of Real HTML Content ===") + + # Check for common Steam cart indicators + cart_indicators = [ + 'cart', 'Cart', 'CART', + 'shopping', 'Shopping', 'SHOPPING', + 'dota', 'Dota', 'DOTA', + 'soundtrack', 'Soundtrack', 'SOUNDTRACK', + 'official', 'Official', 'OFFICIAL' + ] + + print("\n--- Checking for cart-related keywords ---") + for indicator in cart_indicators: + count = real_html_content.count(indicator) + if count > 0: + print(f"✓ Found '{indicator}' {count} times") + else: + print(f"✗ '{indicator}' not found") + + # Look for any mention of "Dota" in various forms + print("\n--- Searching for Dota-related content ---") + dota_patterns = ['dota', 'Dota', 'DOTA', 'Dota 2', 'dota 2', 'DOTA 2'] + for pattern in dota_patterns: + if pattern in real_html_content: + # Find the context around the match + index = real_html_content.find(pattern) + start = max(0, index - 50) + end = min(len(real_html_content), index + len(pattern) + 50) + context = real_html_content[start:end].replace('\n', ' ').strip() + print(f"✓ Found '{pattern}' at position {index}") + print(f" Context: ...{context}...") + break + else: + print("✗ No Dota-related content found") + + # Check if this is actually an empty cart page + empty_indicators = ['empty', 'Empty', 'EMPTY', 'no items', 'No items', 'NO ITEMS'] + print("\n--- Checking for empty cart indicators ---") + for indicator in empty_indicators: + if indicator in real_html_content: + print(f"⚠ Found empty cart indicator: '{indicator}'") + + # Show some basic HTML structure info + print(f"\n--- HTML Structure Info ---") + print(f"Total HTML length: {len(real_html_content)} characters") + print(f"Number of
tags: {real_html_content.count(' tags: {real_html_content.count(' 0: + return proxy_configs[0] + else: + return proxy_configs + except Exception as e: + print(f"Error loading proxy config: {e}") + return None + +def get_proxy_dict(proxy_config): + """Convert proxy config to requests proxy format""" + if not proxy_config: + return None + + proxy_url = f"http://{proxy_config['username']}:{proxy_config['password']}@{proxy_config['host']}:{proxy_config['port']}" + return { + 'http': proxy_url, + 'https': proxy_url + } + +def test_specific_url(url, use_proxy=False, proxy_dict=None): + """Test the specific URL that failed in evaluation""" + print(f"\n=== Testing {url} ===\n") + + try: + # Test with GET request to simulate browser behavior + headers = { + 'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36', + 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8', + 'Accept-Language': 'en-US,en;q=0.5', + 'Accept-Encoding': 'gzip, deflate, br', + 'Connection': 'keep-alive', + 'Upgrade-Insecure-Requests': '1' + } + + response = requests.get( + url, + headers=headers, + proxies=proxy_dict if use_proxy else None, + timeout=60, + allow_redirects=True + ) + + print(f"Status Code: {response.status_code}") + print(f"Final URL: {response.url}") + print(f"Response Size: {len(response.content)} bytes") + + # Check response headers + print("\nKey Response Headers:") + for header in ['server', 'cf-ray', 'cf-cache-status', 'content-type']: + if header in response.headers: + print(f" {header}: {response.headers[header]}") + + # Check if we got the actual page content + if response.status_code == 200: + content = response.text.lower() + if 'banter' in content or 'thread' in content or 'discourse' in content: + print("✅ Successfully loaded page content") + elif 'cloudflare' in content or 'challenge' in content: + print("⚠️ Got Cloudflare challenge page") + elif len(content) < 1000: + print(f"⚠️ Suspiciously small content: {len(content)} chars") + print(f"Content preview: {content[:200]}...") + else: + print("✅ Got substantial content") + + return True, response.status_code, len(response.content) + + except requests.exceptions.ProxyError as e: + print(f"❌ Proxy Error: {e}") + return False, None, 0 + except requests.exceptions.Timeout as e: + print(f"❌ Timeout Error: {e}") + return False, None, 0 + except requests.exceptions.RequestException as e: + print(f"❌ Request Error: {e}") + return False, None, 0 + +def main(): + print("Testing Specific FlightAware Discussions Page") + print("=" * 60) + + # Load proxy configuration + proxy_config = load_proxy_config() + if proxy_config: + print(f"✅ Loaded proxy: {proxy_config['host']}:{proxy_config['port']}") + proxy_dict = get_proxy_dict(proxy_config) + else: + print("❌ Failed to load proxy configuration") + return + + # The specific URL that failed in the evaluation + target_url = "https://discussions.flightaware.com/t/the-banter-thread/4412" + + print(f"\nTarget URL: {target_url}") + print("This is the exact URL that failed in the evaluation log\n") + + # Test direct connection + print("=" * 40) + print("DIRECT CONNECTION TEST") + print("=" * 40) + direct_success, direct_status, direct_size = test_specific_url(target_url, use_proxy=False) + + # Test proxy connection + print("\n" + "=" * 40) + print("PROXY CONNECTION TEST") + print("=" * 40) + proxy_success, proxy_status, proxy_size = test_specific_url(target_url, use_proxy=True, proxy_dict=proxy_dict) + + # Summary + print("\n" + "=" * 60) + print("SUMMARY") + print("=" * 60) + + print(f"\nDirect Connection:") + print(f" Success: {'✅' if direct_success else '❌'}") + print(f" Status: {direct_status}") + print(f" Size: {direct_size} bytes") + + print(f"\nProxy Connection:") + print(f" Success: {'✅' if proxy_success else '❌'}") + print(f" Status: {proxy_status}") + print(f" Size: {proxy_size} bytes") + + # Analysis + print("\n" + "=" * 60) + print("ANALYSIS") + print("=" * 60) + + if direct_success and not proxy_success: + print("\n🔍 Finding: Proxy connection fails while direct works") + print(" This matches the evaluation log error pattern") + elif not direct_success and not proxy_success: + print("\n🔍 Finding: Both connections fail") + print(" The page might be restricted or have issues") + elif direct_success and proxy_success: + print("\n🔍 Finding: Both connections work") + print(" The issue might be intermittent or evaluation-specific") + else: + print("\n🔍 Finding: Unexpected pattern - proxy works but direct fails") + + # Additional tests with different approaches + print("\n" + "=" * 60) + print("ADDITIONAL TESTS") + print("=" * 60) + + # Test the base discussions URL + base_url = "https://discussions.flightaware.com" + print(f"\nTesting base URL: {base_url}") + + print("\n--- Direct ---") + base_direct_success, _, _ = test_specific_url(base_url, use_proxy=False) + + print("\n--- Proxy ---") + base_proxy_success, _, _ = test_specific_url(base_url, use_proxy=True, proxy_dict=proxy_dict) + + if base_proxy_success and not proxy_success: + print("\n💡 Insight: Base discussions URL works with proxy, but specific thread fails") + print(" This suggests the issue is with specific deep-linked content") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/mm_agents/maestro/maestro/debug_system/test_url.py b/mm_agents/maestro/maestro/debug_system/test_url.py new file mode 100644 index 0000000..6c0a401 --- /dev/null +++ b/mm_agents/maestro/maestro/debug_system/test_url.py @@ -0,0 +1,188 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Test script: Verify the evaluation result of a given URL in the check_direct_json_object function + +This script directly calls actual functions from the OSWorld evaluation system to evaluate Qatar Airways URL: +1. Use get_active_tab_url_parse to parse query parameters from URL +2. Use get_rule_relativeTime to apply relative time rules ("next Monday") +3. Use check_direct_json_object function for comparison +""" + +import sys +import os +import json +import logging +from datetime import datetime +import pytz + +# Add the project root to Python path +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +# Import actual functions from OSWorld +from desktop_env.evaluators.getters.chrome import get_active_tab_url_parse +from desktop_env.evaluators.getters.misc import get_rule_relativeTime +from desktop_env.evaluators.metrics.general import check_direct_json_object + +# Setup logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +def mock_get_active_tab_url_parse(url, config): + """Mock function to simulate get_active_tab_url_parse with a URL string""" + from urllib.parse import urlparse, parse_qs + + logger.info(f"Parsing URL: {url}") + + # Parse URL + parsed_url = urlparse(url) + query_params = parse_qs(parsed_url.query) + + # Extract specified parameters + parse_keys = config.get('parse_keys', []) + extracted_params = {key: query_params.get(key, [''])[0] for key in parse_keys} + + # Apply key name replacement + replace_mapping = config.get('replace', {}) + if replace_mapping: + for old_key, new_key in replace_mapping.items(): + if old_key in extracted_params: + value = extracted_params.pop(old_key) + extracted_params[new_key] = value + + logger.info(f"Extracted parameters: {extracted_params}") + return extracted_params + +def main(): + """Main function: Execute complete URL evaluation process""" + print("=" * 80) + print("Qatar Airways URL Evaluation Test - Using OSWorld Actual Functions and Config File") + print("=" * 80) + + # Load evaluator configuration from config file + config_file = os.path.join("evaluation_examples", "examples", "chrome", "82bc8d6a-36eb-4d2d-8801-ef714fb1e55a.json") + print(f"Loading from config file: {config_file}") + + try: + with open(config_file, 'r', encoding='utf-8') as f: + config = json.load(f) + print(f"Config file loaded successfully") + except Exception as e: + logger.error(f"Error loading config file: {e}") + print(f"Error loading config file: {e}") + return + + # Extract evaluator configuration + evaluator_config = config.get("evaluator", {}) + result_config = evaluator_config.get("result", {}) + expected_config = evaluator_config.get("expected", {}) + + print(f"\nEvaluator configuration:") + print(f" Function: {evaluator_config.get('func')}") + print(f" Result config: {result_config}") + print(f" Expected config: {expected_config}") + + # Test URL + test_url = "https://www.qatarairways.com/app/booking/flight-selection?widget=QR&searchType=F&addTaxToFare=Y&minPurTime=0&selLang=en&tripType=O&fromStation=BOM&toStation=ARN&departing=2025-09-08&bookingClass=E&adults=1&children=0&infants=0&ofw=0&teenager=0&flexibleDate=off&allowRedemption=N" + + # Current time + timezone = pytz.UTC + current_time = timezone.localize(datetime.now()) + print(f"\nCurrent time: {current_time.strftime('%Y-%m-%d %H:%M:%S %Z (%A)')}") + + # Step 1: Parse URL parameters using config file parameters + print("\nStep 1: Parse URL parameters (using config file parameters)") + url_parse_config = { + "parse_keys": result_config.get("parse_keys", []), + "replace": result_config.get("replace", {}) + } + + parsed_result = mock_get_active_tab_url_parse(test_url, url_parse_config) + print(f"Parsing result: {parsed_result}") + + # Step 2: Use relative time rules from config file + print("\nStep 2: Process relative time rules (using config file rules)") + relative_time_rules = expected_config.get("rules", {}) + relative_time_config = relative_time_rules.get("relativeTime", {}) + + print(f"Relative time configuration: {relative_time_config}") + + try: + # get_rule_relativeTime requires two parameters: env and config + # Build complete config parameter + full_config = { + "rules": { + "relativeTime": relative_time_config, + "expected": expected_config.get("rules", {}).get("expected", {}) + } + } + updated_rules = get_rule_relativeTime(None, full_config) # Pass None for env parameter + # Extract calculated time from returned rules + expected_time = updated_rules.get("expected", {}).get("time", "") + print(f"Expected time (next Monday): {expected_time}") + except Exception as e: + logger.error(f"Error calling get_rule_relativeTime: {e}") + print(f"Error calling get_rule_relativeTime: {e}") + return + + # Step 3: Build expected JSON object (using expected template from config file) + print("\nStep 3: Build expected result (using config file template)") + expected_template = relative_time_rules.get("expected", {}) + + # Build expected JSON object + expected_json = {} + for key, value in expected_template.items(): + if key == "time" and isinstance(value, str) and "{" in value: + # This is a time format template, use calculated time + expected_json[key] = expected_time + else: + expected_json[key] = value + + print(f"Expected JSON: {expected_json}") + + # Step 4: Use actual check_direct_json_object function for comparison + print("\nStep 4: Execute JSON object comparison (using OSWorld actual function)") + rules = {"expected": expected_json} + + try: + result = check_direct_json_object(parsed_result, rules) + print(f"check_direct_json_object return result: {result}") + except Exception as e: + logger.error(f"Error calling check_direct_json_object: {e}") + print(f"Error calling check_direct_json_object: {e}") + return + + # Output final result + print("\n" + "=" * 80) + print("Evaluation Result") + print("=" * 80) + print(f"Actual parsing result: {parsed_result}") + print(f"Expected result: {expected_json}") + print(f"\nFinal evaluation score: {result}") + + if result == 1.0: + print("✅ Evaluation passed! Date in URL matches calculated 'next Monday'") + else: + print("❌ Evaluation failed! Date in URL does not match calculated 'next Monday'") + + # Detailed difference analysis + print("\nDifference analysis:") + actual_time = parsed_result.get("time") + expected_time_value = expected_json.get("time") + print(f" Date in URL: {actual_time}") + print(f" Expected date: {expected_time_value}") + + if actual_time != expected_time_value: + print(f" Time mismatch: '{actual_time}' != '{expected_time_value}'") + + print("\n" + "=" * 80) + + # Additional information: Display current time and date in URL + print(f"\nAdditional information:") + print(f" Current time: {current_time.strftime('%Y-%m-%d (%A)')}") + print(f" Departing date in URL: 2025-09-01") + print(f" Calculated next Monday: {expected_time}") + print(f" Do they match: {'Yes' if expected_time == '2025-09-01' else 'No'}") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/mm_agents/maestro/maestro/enums.py b/mm_agents/maestro/maestro/enums.py new file mode 100644 index 0000000..f375f36 --- /dev/null +++ b/mm_agents/maestro/maestro/enums.py @@ -0,0 +1,260 @@ +"""Controller Enums +Centralized management of all controller-related enumeration values +""" + +from enum import Enum + +class ControllerState(str, Enum): + """Controller state definitions""" + INIT = "INIT" # Project initiation phase + GET_ACTION = "GET_ACTION" # Get next action + EXECUTE_ACTION = "EXECUTE_ACTION" # Action execution phase + QUALITY_CHECK = "QUALITY_CHECK" # Quality gate check + PLAN = "PLAN" # Re-planning phase + SUPPLEMENT = "SUPPLEMENT" # Data supplementation phase + FINAL_CHECK = "FINAL_CHECK" # Final quality check phase + DONE = "DONE" # Task completion, haven't figured out how to handle done state yet + + +class SubtaskStatus(str, Enum): + """Subtask status""" + PENDING = "pending" # In progress + READY = "ready" # Ready to execute + FULFILLED = "fulfilled" # Completed + REJECTED = "rejected" # Rejected + STALE = "stale" # Stale + + +class ExecStatus(str, Enum): + """Execution status""" + PENDING = "pending" # Pending + EXECUTED = "executed" # Executed + TIMEOUT = "timeout" # Timeout + BLOCKED = "blocked" # Blocked + ERROR = "error" # Error + SKIPPED = "skipped" # Skipped (no hardware/code execution) + + +class GateDecision(str, Enum): + """Quality gate decision""" + GATE_DONE = "gate_done" # Quality check passed + GATE_FAIL = "gate_fail" # Quality check failed + GATE_SUPPLEMENT = "gate_supplement" # Need to supplement data + GATE_CONTINUE = "gate_continue" # Continue execution + + +class GateTrigger(str, Enum): + """Quality gate trigger conditions""" + MANUAL = "manual" # Manual trigger + TIMEOUT = "timeout" # Timeout trigger + ERROR = "error" # Error trigger + PERIODIC_CHECK = "periodic_check" # Periodic check + WORKER_STALE = "worker_stale" # Worker stale + WORKER_SUCCESS = "worker_success" # Worker success + FINAL_CHECK = "final_check" # Final check + +class WorkerDecision(str, Enum): + """Worker decision""" + WORKER_DONE = "worker_done" # Subtask completed + CANNOT_EXECUTE = "worker_fail" # Subtask failed + SUPPLEMENT = "worker_supplement" # Need to supplement data + STALE_PROGRESS = "worker_stale_progress" # Need quality check + GENERATE_ACTION = "worker_generate_action" # Generate action + +class WorkerTrigger(str, Enum): + """Worker trigger conditions""" + MANUAL = "manual" # Manual trigger + + + +class TaskStatus(str, Enum): + """Task status""" + CREATED = "created" # Created + PENDING = "pending" # Pending + ON_HOLD = "on_hold" # On hold + FULFILLED = "fulfilled" # Completed + REJECTED = "rejected" # Rejected + CANCELLED = "cancelled" # Cancelled + + +class WorkerStatus(str, Enum): + """Worker status""" + IDLE = "idle" # Idle + BUSY = "busy" # Busy + ERROR = "error" # Error + OFFLINE = "offline" # Offline + + +class EvaluatorStatus(str, Enum): + """Evaluator status""" + IDLE = "idle" # Idle + EVALUATING = "evaluating" # Evaluating + ERROR = "error" # Error + OFFLINE = "offline" # Offline + + +class ManagerStatus(str, Enum): + """Manager status""" + IDLE = "idle" # Idle + PLANNING = "planning" # Planning + SUPPLEMENTING = "supplementing" # Supplementing data + ERROR = "error" # Error + OFFLINE = "offline" # Offline + + +class EventType(str, Enum): + """Event type""" + INFO = "info" # Information + WARNING = "warning" # Warning + ERROR = "error" # Error + SUCCESS = "success" # Success + STATE_SWITCH = "state_switch" # State switch + STATUS_CHANGE = "status_change" # Status change + +class TriggerRole(str, Enum): + """Trigger role""" + CONTROLLER = "controller" # controller + WORKER_GET_ACTION = "worker_get_action" # Worker + EVALUATOR_QUALITY_CHECK = "evaluator_quality_check" # Evaluator + EVALUATOR_FINAL_CHECK = "evaluator_final_check" # Evaluator + MANAGER_REPLAN = "manager_replan" # Manager + MANAGER_SUPPLEMENT = "manager_supplement" # Manager + EXECUTOR_EXECUTE_ACTION = "executor_execute_action" # Executor + HARDWARE_EXECUTE_ACTION = "hardware_execute_action" # Hardware + +class TriggerCode(str, Enum): + """Trigger code enumeration""" + + # Rule validation related + RULE_QUALITY_CHECK_STEPS = "rule_quality_check_steps" # controller -> evaluator: quality_check + RULE_QUALITY_CHECK_REPEATED_ACTIONS = "rule_quality_check_repeated_actions" # controller -> evaluator: quality_check + RULE_REPLAN_LONG_EXECUTION = "rule_replan_long_execution" # controller -> manager: replan + + # Task status rule related + RULE_MAX_STATE_SWITCHES_REACHED = "rule_max_state_switches_reached" # controller -> controller: done + RULE_PLAN_NUMBER_EXCEEDED = "rule_plan_number_exceeded" # controller -> controller: done + RULE_STATE_SWITCH_COUNT_EXCEEDED = "rule_state_switch_count_exceeded" # controller -> controller: done + RULE_TASK_COMPLETED = "rule_task_completed" # controller -> controller: done + RULE_TASK_RUNTIME_EXCEEDED = "rule_task_runtime_exceeded" # controller -> controller: done + + # State handling related - INIT state + SUBTASK_READY = "subtask_ready" # INIT -> worker: get_action + NO_SUBTASKS = "no_subtasks" # INIT -> manager: replan + INIT_ERROR = "init_error" # INIT -> manager: replan + + # State handling related - GET_ACTION state + NO_CURRENT_SUBTASK_ID = "no_current_subtask_id" # worker: get_action | executor: execute_action | evaluator: quality_check -> INIT + SUBTASK_NOT_FOUND = "subtask_not_found" # worker: get_action | executor: execute_action -> INIT + WORKER_SUCCESS = "worker_success" # worker: get_action -> evaluator: quality_check + WORK_CANNOT_EXECUTE = "work_cannot_execute" # worker: get_action -> manager: replan + WORKER_STALE_PROGRESS = "worker_stale_progress" # worker: get_action -> evaluator: quality_check + WORKER_SUPPLEMENT = "worker_supplement" # worker: get_action -> manager: supplement + WORKER_GENERATE_ACTION = "worker_generate_action" # worker: get_action -> executor + NO_WORKER_DECISION = "no_worker_decision" # worker: get_action -> manager: replan + GET_ACTION_ERROR = "get_action_error" # worker: get_action -> manager: replan + + # State handling related - EXECUTE_ACTION state + EXECUTION_ERROR = "execution_error" # executor: execute_action -> worker: get_action + COMMAND_COMPLETED = "command_completed" # executor: execute_action -> worker: get_action + NO_COMMAND = "no_command" # executor: execute_action -> worker: get_action + + # State handling related - QUALITY_CHECK state + ALL_SUBTASKS_COMPLETED = "all_subtasks_completed" # evaluator: quality_check -> evaluator: final_check + QUALITY_CHECK_PASSED = "quality_check_passed" # # evaluator: quality_check -> worker: get_action + QUALITY_CHECK_FAILED = "quality_check_failed" # evaluator: quality_check -> manager: replan + QUALITY_CHECK_SUPPLEMENT = "quality_check_supplement" # evaluator: quality_check -> manager: supplement + QUALITY_CHECK_EXECUTE_ACTION = "quality_check_execute_action" # evaluator: quality_check -> executor: execute_action + QUALITY_CHECK_ERROR = "quality_check_error" # evaluator: quality_check -> manager: replan + + # State handling related - PLAN state + SUBTASK_READY_AFTER_PLAN = "subtask_ready_after_plan" # manager: replan -> worker: get_action + PLAN_ERROR = "plan_error" # manager: replan -> INIT + + # State handling related - SUPPLEMENT state + SUPPLEMENT_COMPLETED = "supplement_completed" # manager: supplement -> manager: replan + SUPPLEMENT_ERROR = "supplement_error" # manager: supplement -> manager: replan + + # State handling related - FINAL_CHECK state + FINAL_CHECK_ERROR = "final_check_error" # evaluator: final_check -> controller: done + FINAL_CHECK_PENDING = "final_check_pending" # evaluator: final_check -> worker: get_action + FINAL_CHECK_PASSED = "final_check_passed" # evaluator: final_check -> controller: done + FINAL_CHECK_FAILED = "final_check_failed" # evaluator: final_check -> manager: replan + TASK_IMPOSSIBLE = "task_impossible" # evaluator: final_check -> controller: done (task rejected) + + # Error recovery related + UNKNOWN_STATE = "unknown_state" # unknown -> INIT + ERROR_RECOVERY = "error_recovery" # unknown -> INIT + +# Trigger code dictionary classified by module +class TRIGGER_CODE_BY_MODULE: + # Manager replan related trigger codes + MANAGER_REPLAN_CODES = { + "work_cannot_execute": TriggerCode.WORK_CANNOT_EXECUTE.value, + "quality_check_failed": TriggerCode.QUALITY_CHECK_FAILED.value, + "no_worker_decision": TriggerCode.NO_WORKER_DECISION.value, + "get_action_error": TriggerCode.GET_ACTION_ERROR.value, + "quality_check_error": TriggerCode.QUALITY_CHECK_ERROR.value, + "final_check_failed": TriggerCode.FINAL_CHECK_FAILED.value, + "rule_replan_long_execution": TriggerCode.RULE_REPLAN_LONG_EXECUTION.value, + "no_subtasks": TriggerCode.NO_SUBTASKS.value, + "init_error": TriggerCode.INIT_ERROR.value, + "supplement_completed": TriggerCode.SUPPLEMENT_COMPLETED.value, + "supplement_error": TriggerCode.SUPPLEMENT_ERROR.value, + } + + # Manager supplement related trigger codes + MANAGER_SUPPLEMENT_CODES = { + "worker_supplement": TriggerCode.WORKER_SUPPLEMENT.value, + "quality_check_supplement": TriggerCode.QUALITY_CHECK_SUPPLEMENT.value, + } + + # Worker get_action related trigger codes + WORKER_GET_ACTION_CODES = { + "subtask_ready": TriggerCode.SUBTASK_READY.value, + "execution_error": TriggerCode.EXECUTION_ERROR.value, + "command_completed": TriggerCode.COMMAND_COMPLETED.value, + "no_command": TriggerCode.NO_COMMAND.value, + "quality_check_passed": TriggerCode.QUALITY_CHECK_PASSED.value, + "subtask_ready_after_plan": TriggerCode.SUBTASK_READY_AFTER_PLAN.value, + "final_check_pending": TriggerCode.FINAL_CHECK_PENDING.value, + } + + # Evaluator quality_check related trigger codes + EVALUATOR_QUALITY_CHECK_CODES = { + "rule_quality_check_steps": TriggerCode.RULE_QUALITY_CHECK_STEPS.value, + "rule_quality_check_repeated_actions": TriggerCode.RULE_QUALITY_CHECK_REPEATED_ACTIONS.value, + "worker_success": TriggerCode.WORKER_SUCCESS.value, + "worker_stale_progress": TriggerCode.WORKER_STALE_PROGRESS.value, + } + + # Evaluator final_check related trigger codes + EVALUATOR_FINAL_CHECK_CODES = { + "all_subtasks_completed": TriggerCode.ALL_SUBTASKS_COMPLETED.value, + } + + # Executor execute_action related trigger codes + EXECUTOR_EXECUTE_ACTION_CODES = { + "worker_generate_action": TriggerCode.WORKER_GENERATE_ACTION.value, + "quality_check_execute_action": TriggerCode.QUALITY_CHECK_EXECUTE_ACTION.value, + } + + # INIT state related trigger codes + INIT_STATE_CODES = { + "no_current_subtask_id": TriggerCode.NO_CURRENT_SUBTASK_ID.value, + "subtask_not_found": TriggerCode.SUBTASK_NOT_FOUND.value, + "plan_error": TriggerCode.PLAN_ERROR.value, + "unknown_state": TriggerCode.UNKNOWN_STATE.value, + "error_recovery": TriggerCode.ERROR_RECOVERY.value, + } + + # DONE state related trigger codes + DONE_STATE_CODES = { + "rule_max_state_switches_reached": TriggerCode.RULE_MAX_STATE_SWITCHES_REACHED.value, + "rule_plan_number_exceeded": TriggerCode.RULE_PLAN_NUMBER_EXCEEDED.value, + "rule_state_switch_count_exceeded": TriggerCode.RULE_STATE_SWITCH_COUNT_EXCEEDED.value, + "rule_task_completed": TriggerCode.RULE_TASK_COMPLETED.value, + "rule_task_runtime_exceeded": TriggerCode.RULE_TASK_RUNTIME_EXCEEDED.value, + "final_check_error": TriggerCode.FINAL_CHECK_ERROR.value, + "final_check_passed": TriggerCode.FINAL_CHECK_PASSED.value, + "task_impossible": TriggerCode.TASK_IMPOSSIBLE.value, + } \ No newline at end of file diff --git a/mm_agents/maestro/maestro/evaluator.py b/mm_agents/maestro/maestro/evaluator.py new file mode 100644 index 0000000..831a782 --- /dev/null +++ b/mm_agents/maestro/maestro/evaluator.py @@ -0,0 +1,799 @@ +""" +Evaluator module + +This module implements the Evaluator as the quality assurance component. +It follows the design document provided by the user. The Evaluator is +responsible for validating execution quality at key checkpoints and +providing gate decisions to drive the controller flow. + +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Optional, Dict, Any, List +from datetime import datetime +import os +import json + +from .new_global_state import NewGlobalState +from .enums import GateDecision, GateTrigger, WorkerDecision, WorkerDecision, TriggerCode, TRIGGER_CODE_BY_MODULE +from ..tools.new_tools import NewTools +from ..prompts import get_prompt +from .manager.utils import get_history_subtasks_info, get_pending_subtasks_info, get_failed_subtasks_info + + +# ========= Data Structures ========= +@dataclass +class GateCheck: + """Quality gate record structure. + + This mirrors the system GateCheck format described in the design. + """ + + gate_check_id: str + task_id: str + subtask_id: str + trigger: str + decision: str + notes: str + created_at: str + +class Evaluator: + """Quality Evaluator implementation. + + The Evaluator consumes the complete NewGlobalState as input and makes a + gate decision for the specified trigger type. The actual decision logic + that leverages LLM prompts is left as placeholders for future work. + """ + + def __init__(self, + global_state: NewGlobalState, + tools_dict: Optional[Dict[str, Any]] = None): + """Create Evaluator. + + Args: + global_state: Shared global state store + tools_dict: Tool configuration dict, expects entries for + worker_success_role | worker_stale_role | periodic_role | final_check_role + """ + self.global_state = global_state + # Initialize evaluator tools using the new registration style + self.tools_dict = tools_dict or {} + + # Use the new tool system: register four evaluator role tools by scene + self.evaluator_agent = NewTools() + for tool_name in ("worker_success_role", "worker_stale_role", + "periodic_role", "final_check_role"): + cfg = self.tools_dict.get(tool_name) + if not cfg or not cfg.get("provider") or not cfg.get("model"): + raise ValueError( + f"Missing evaluator tool configuration for '{tool_name}' (provider/model)") + self.evaluator_agent.register_tool(tool_name, cfg["provider"], cfg["model"]) + + # ========= Public API ========= + def quality_check(self) -> GateCheck: + """Run quality check with no external parameters. + + The trigger type is inferred from the current global state. + + Returns: + GateCheck: The created gate check record + """ + + globalstate = self.global_state + + # Determine subtask context + task = globalstate.get_task() + subtask_id: Optional[str] = task.current_subtask_id + + # Get trigger code and determine tool name and prompt + trigger_code = self._get_current_trigger_code() + tool_name = self._trigger_code_to_tool_name(trigger_code) + # Build prompts + prompt = self.build_prompt_by_trigger_code(trigger_code) + screenshot = globalstate.get_screenshot() + + # Start timing + import time + evaluator_start_time = time.time() + + content, _tokens, _cost = self.evaluator_agent.execute_tool( + tool_name, { + "str_input": prompt, + "img_input": screenshot + }) + + # Log evaluator operation to display.json + evaluator_duration = time.time() - evaluator_start_time + self.global_state.log_llm_operation( + "evaluator", f"quality_check_{trigger_code.lower()}", { + "tokens": _tokens, + "cost": _cost, + "evaluator_result": content, + "trigger_code": trigger_code, + "subtask_id": subtask_id, + "duration": evaluator_duration + }, + str_input=prompt, + # img_input=screenshot + ) + + parsed = self.parse_llm_output(content or "") + normalized = self._normalize_decision_by_trigger_code(parsed.get("decision", ""), trigger_code) + if normalized is None: + raise ValueError( + f"Invalid decision from LLM: {parsed.get('decision', '')}") + decision = normalized + notes = self._compose_notes(parsed) + trigger_enum = self._trigger_code_to_gate_trigger(trigger_code) + + # Persist to global state in system format + from .data_models import create_gate_check_data + gate_check_id = globalstate.add_gate_check( + create_gate_check_data( + gate_check_id="", + task_id=globalstate.task_id, + decision=decision.value, + subtask_id=subtask_id, + notes=notes, + trigger=trigger_enum.value, + )) + + # Build dataclass instance to return + record = GateCheck( + gate_check_id=gate_check_id, + task_id=globalstate.task_id, + subtask_id=subtask_id or "", + trigger=trigger_enum.value, + decision=decision.value, + notes=notes, + created_at=datetime.now().isoformat(), + ) + + return record + + # ---- Trigger inference ---- + def _get_current_trigger_code(self) -> str: + """Get current trigger_code from controller state.""" + controller_state = self.global_state.get_controller_state() + return controller_state.get("trigger_code", "") + + + + def _trigger_code_to_tool_name(self, trigger_code: str) -> str: + """Map trigger code to the concrete tool name in the new tool system.""" + mapping = { + TriggerCode.WORKER_SUCCESS.value: "worker_success_role", + TriggerCode.WORKER_STALE_PROGRESS.value: "worker_stale_role", + TriggerCode.RULE_QUALITY_CHECK_STEPS.value: "periodic_role", + TriggerCode.RULE_QUALITY_CHECK_REPEATED_ACTIONS.value: "periodic_role", # Use periodic system prompt + TriggerCode.ALL_SUBTASKS_COMPLETED.value: "final_check_role", + } + return mapping.get(trigger_code, "periodic_role") + + def _scene_to_tool_name(self, scene: str) -> str: + """Map scene name to the concrete tool name in the new tool system.""" + mapping = { + "WORKER_SUCCESS": "worker_success_role", + "WORKER_STALE": "worker_stale_role", + "PERIODIC_CHECK": "periodic_role", + "FINAL_CHECK": "final_check_role", + } + return mapping.get(scene, "periodic_role") + + def _format_subtask_brief(self, subtask) -> str: + if not subtask: + return "(no subtask)" + if isinstance(subtask, dict): + title = subtask.get("title", "") + desc = subtask.get("description", "") + status = subtask.get("status", "") + else: + title = getattr(subtask, "title", "") + desc = getattr(subtask, "description", "") + status = getattr(subtask, "status", "") + return f"title:\n{title}\nstatus:\n{status}\ndescription:\n{desc}" + + def _format_task_brief(self) -> str: + task = self.global_state.get_task() + return f"{task.objective}" + + def _get_artifacts_text(self) -> str: + return self.global_state.get_artifacts() + + def _get_supplement_text(self) -> str: + return self.global_state.get_supplement() + + def _get_command_history_for_subtask(self, subtask_id: Optional[str]) -> str: + """Reference operator/technician, get historical operation records for specified subtask.""" + try: + if not subtask_id: + return "No historical operation records" + commands = list(reversed(self.global_state.get_commands_for_subtask(subtask_id))) + if not commands: + return "No historical operation records" + history_lines: List[str] = [] + history_lines.append("=== Historical Operation Records ===") + for i, cmd in enumerate(commands, 1): + action_type = "Unknown Operation" + action_desc = "" + action = getattr(cmd, "action", None) + if isinstance(action, dict): + if "type" in action: + action_type = str(action.get("type", "")) + if "message" in action: + action_desc = str(action.get("message", "")) + elif "element_description" in action: + action_desc = f"Operate element: {action['element_description']}" + elif "text" in action: + action_desc = f"Input text: {action['text']}" + elif "keys" in action: + action_desc = f"Key press: {action['keys']}" + elif isinstance(action, list): + action_type = "Code Generation" + if action: + # Simplified display to avoid excessive length + first_lang, first_code = action[0] + action_desc = f"[1] Language: {first_lang}, Code length: {len(str(first_code))}" + status = getattr(cmd, "worker_decision", "") + message = getattr(cmd, "message", "") or "" + reason_text = getattr(cmd, "reason_text", "") or "" + exec_status = getattr(cmd, "exec_status", "") + exec_message = getattr(cmd, "exec_message", "") + pre_screenshot_analysis = getattr(cmd, "pre_screenshot_analysis", "") + created_at = getattr(cmd, "created_at", "") + history_lines.append(f"{i}. [{action_type}] - Status: {status}") + if action_desc: + history_lines.append(f" Description: {action_desc}") + if message: + history_lines.append(f" Message: {message}") + if reason_text: + history_lines.append(f" Reason: {reason_text}") + if exec_status: + history_lines.append(f" Execution Status: {exec_status}") + if exec_message: + history_lines.append(f" Execution Message: {exec_message}") + if pre_screenshot_analysis: + history_lines.append(f" Pre-Screenshot Analysis: {pre_screenshot_analysis}") + if created_at: + history_lines.append(f" Created at: {created_at}") + history_lines.append("") + return "\n".join(history_lines) + except Exception as e: + return f"Failed to get historical records: {e}" + + def _get_last_operation_brief(self, subtask_id: Optional[str]) -> str: + """Get brief information of the most recent operation.""" + try: + if not subtask_id: + return "(no last operation)" + cmd = self.global_state.get_current_command_for_subtask(subtask_id) + if not cmd: + return "(no last operation)" + action_type = "Unknown Operation" + action_desc = "" + action = getattr(cmd, "action", None) + if isinstance(action, dict): + if "type" in action: + action_type = str(action.get("type", "")) + if "analysis" in action: + analysis_text = str(action.get("analysis", "")) + recs = action.get("recommendations", []) + recs_len = len(recs) if isinstance(recs, list) else 0 + action_desc = f"Analysis: {len(analysis_text)} chars; Recommendations: {recs_len} items" + elif "message" in action: + action_desc = str(action.get("message", "")) + elif "element_description" in action: + action_desc = f"Operate element: {action['element_description']}" + elif "text" in action: + action_desc = f"Input text: {action['text']}" + elif "keys" in action: + action_desc = f"Key press: {action['keys']}" + elif isinstance(action, list): + action_type = "Code Generation" + if action: + first_lang, first_code = action[0] + action_desc = f"[1] Language: {first_lang}, Code length: {len(str(first_code))}" + status = getattr(cmd, "worker_decision", "") + message = getattr(cmd, "message", "") or "" + reason_text = getattr(cmd, "reason_text", "") or "" + exec_status = getattr(cmd, "exec_status", "") + exec_message = getattr(cmd, "exec_message", "") + pre_screenshot_analysis = getattr(cmd, "pre_screenshot_analysis", "") + created_at = getattr(cmd, "created_at", "") + lines = [ + f"Type: {action_type}", + f"Status: {status}", + ] + if action_desc: + lines.append(f"Description: {action_desc}") + if message: + lines.append(f"Message: {message}") + if reason_text: + lines.append(f"Reason: {reason_text}") + if exec_status: + lines.append(f"Execution Status: {exec_status}") + if exec_message: + lines.append(f"Execution Message: {exec_message}") + if pre_screenshot_analysis: + lines.append(f"Pre-Screenshot Analysis: {pre_screenshot_analysis}") + if created_at: + lines.append(f"Created at: {created_at}") + return "\n".join(lines) + except Exception as e: + return f"(last operation unavailable: {e})" + + def _get_command_history_for_entire_task(self) -> str: + """Aggregate operation histories for all subtasks in the task. + + Order of aggregation: + 1) Completed/history subtasks (in recorded order) + 2) Current subtask (if any) + 3) Pending subtasks (in recorded order) + """ + try: + task = self.global_state.get_task() + completed_ids = list(task.history_subtask_ids or []) + current_id = task.current_subtask_id + pending_ids = list(task.pending_subtask_ids or []) + + ordered_ids: List[str] = [] + ordered_ids.extend(completed_ids) + if current_id: + ordered_ids.append(current_id) + ordered_ids.extend(pending_ids) + + if not ordered_ids: + return "No historical operation records" + + sections: List[str] = ["=== All Subtasks Operation Records ==="] + for idx, sid in enumerate(ordered_ids, 1): + subtask = self.global_state.get_subtask(sid) + # Get a readable brief for the subtask + if isinstance(subtask, dict): + title = subtask.get("title", "") + status = subtask.get("status", "") + else: + title = getattr(subtask, "title", "") + status = getattr(subtask, "status", "") + + sections.append(f"\n--- Subtask {idx} [{sid}] ---") + if title: + sections.append(f"Title: {title}") + if status: + sections.append(f"Status: {status}") + # Reuse per-subtask history formatter + history_text = self._get_command_history_for_subtask(sid) + sections.append(history_text) + + return "\n".join(sections) + except Exception as e: + return f"Failed to aggregate operation records: {e}" + + def _collect_scene_inputs(self, scene: str) -> dict: + """Collect and slice inputs for a specific scene. + + Command selection rules: + - WORKER_SUCCESS: all commands of current subtask + - WORKER_STALE: all commands of current subtask + - PERIODIC_CHECK: last 5 commands of current subtask + - FINAL_CHECK: all commands of entire task + """ + task = self.global_state.get_task() + subtask_id = task.current_subtask_id + subtask = self.global_state.get_subtask( + subtask_id) if subtask_id else None + + history_text = ( + self._get_command_history_for_entire_task() + if scene == "FINAL_CHECK" + else self._get_command_history_for_subtask(subtask_id) + ) + last_operation_text = self._get_last_operation_brief(subtask_id) + + global_task_status = self._get_global_task_status() + history_subtasks_info = get_history_subtasks_info(self.global_state) + pending_subtasks_info = get_pending_subtasks_info(self.global_state) + failed_subtasks_info = get_failed_subtasks_info(self.global_state) + + return { + "task_brief": self._format_task_brief(), + "subtask_brief": self._format_subtask_brief(subtask), + "artifacts": self._get_artifacts_text(), + "supplement": self._get_supplement_text(), + "worker_report": self._get_worker_report(subtask), + "history_text": history_text, + "last_operation_text": last_operation_text, + "global_task_status": global_task_status, + "history_subtasks_info": history_subtasks_info, + "pending_subtasks_info": pending_subtasks_info, + "failed_subtasks_info": failed_subtasks_info, + } + + def _get_global_task_status(self) -> str: + """Get global task status summary""" + task = self.global_state.get_task() + all_subtasks = self.global_state.get_subtasks() + + total_subtasks = len(all_subtasks) + completed_count = len(task.history_subtask_ids or []) + pending_count = len(task.pending_subtask_ids or []) + current_count = 1 if task.current_subtask_id else 0 + + # Count subtasks by status + status_counts = {} + for subtask in all_subtasks: + status = subtask.status + status_counts[status] = status_counts.get(status, 0) + 1 + + status_summary = { + "total_subtasks": total_subtasks, + "completed_subtasks": completed_count, + "pending_subtasks": pending_count, + "current_subtask": task.current_subtask_id, + "status_distribution": status_counts, + "progress_percentage (%)": round((completed_count / total_subtasks * 100), 1) if total_subtasks > 0 else 0 + } + + return json.dumps(status_summary, indent=2) + + def build_prompt_by_trigger_code(self, trigger_code: str) -> str: + """Build user prompt string based on trigger code.""" + # Map trigger code to scene for input collection + scene = self._trigger_code_to_scene(trigger_code) + inputs = self._collect_scene_inputs(scene) + + trigger_guidance = self._get_context_info_by_trigger_code(trigger_code) + + parts = [ + "# GlobalState Information\n", + f"Task objective:\n{inputs['task_brief']}\n", + f"\nGlobal Task Status:\n{inputs['global_task_status']}\n", + f"\nCompleted Subtasks:\n{inputs['history_subtasks_info']}\n", + f"\nPending Subtasks:\n{inputs['pending_subtasks_info']}\n", + f"\nFailed Subtasks:\n{inputs['failed_subtasks_info']}\n", + f"Artifacts (Memory written by previous operators and analysts):\n{inputs['artifacts']}\n", + f"Supplement (Supplement materials provided by the manager):\n{inputs['supplement']}\n", + f"Subtask:\n{inputs['subtask_brief']}\n", + (f"Worker Report:\n{inputs['worker_report']}\n" if trigger_code == TriggerCode.WORKER_STALE_PROGRESS.value else ""), + f"\nOperation History (Current Subtask):\n{inputs['history_text']}\n", + f"\nLatest Operation:\n{inputs['last_operation_text']}\n", + f"\nGuidance:\n{trigger_guidance}\n", + ] + return "\n".join(parts) + + def build_prompt(self, scene: str) -> str: + """Build user prompt string containing only runtime inputs.""" + inputs = self._collect_scene_inputs(scene) + + trigger_guidance = self._get_context_info_by_trigger(scene) + + parts = [ + "# GlobalState Information\n", + f"Task objective:\n{inputs['task_brief']}\n", + f"\nGlobal Task Status:\n{inputs['global_task_status']}\n", + f"\nCompleted Subtasks:\n{inputs['history_subtasks_info']}\n", + f"\nPending Subtasks:\n{inputs['pending_subtasks_info']}\n", + f"\nFailed Subtasks:\n{inputs['failed_subtasks_info']}\n", + f"Artifacts (Memory written by previous operators and analysts):\n{inputs['artifacts']}\n", + f"Supplement (Supplement materials provided by the manager):\n{inputs['supplement']}\n", + f"Subtask:\n{inputs['subtask_brief']}\n", + (f"Worker Report:\n{inputs['worker_report']}\n" if scene == "WORKER_STALE" else ""), + f"\nOperation History (Current Subtask):\n{inputs['history_text']}\n", + f"\nLatest Operation:\n{inputs['last_operation_text']}\n", + f"\nGuidance:\n{trigger_guidance}\n", + ] + return "\n".join(parts) + + def _compose_notes(self, parsed: Dict[str, str]) -> str: + parts = [] + if parsed.get("reason"): + parts.append(f"Reason: {parsed['reason']}") + if parsed.get("global_impact"): + parts.append(f"Global Impact: {parsed['global_impact']}") + if parsed.get("strategic_recommendations"): + parts.append(f"Strategic Recommendations: {parsed['strategic_recommendations']}") + if parsed.get("suggestion"): + parts.append(f"Suggestion: {parsed['suggestion']}") + if parsed.get("risk_alert"): + parts.append(f"Risk: {parsed['risk_alert']}") + if parsed.get("incomplete_items"): + parts.append(f"Incomplete: {parsed['incomplete_items']}") + return " \n".join(parts) if parts else "" + + def _normalize_decision(self, decision_text: str, + scene: str) -> Optional[GateDecision]: + if not decision_text: + return None + d = decision_text.strip().lower() + # Accept raw or bracketed + d = d.replace("[", "").replace("]", "") + # Allow synonyms + synonyms = { + "gate_done": GateDecision.GATE_DONE, + "done": GateDecision.GATE_DONE, + "gate_fail": GateDecision.GATE_FAIL, + "fail": GateDecision.GATE_FAIL, + "gate_supplement": GateDecision.GATE_SUPPLEMENT, + "supplement": GateDecision.GATE_SUPPLEMENT, + "gate_continue": GateDecision.GATE_CONTINUE, + "continue": GateDecision.GATE_CONTINUE, + } + candidate = synonyms.get(d) + if candidate is None: + return None + + # Enforce allowed set per scene + allowed = { + "WORKER_SUCCESS": {GateDecision.GATE_DONE, GateDecision.GATE_FAIL}, + "WORKER_STALE": { + GateDecision.GATE_CONTINUE, GateDecision.GATE_FAIL, + GateDecision.GATE_SUPPLEMENT + }, + "PERIODIC_CHECK": { + GateDecision.GATE_CONTINUE, GateDecision.GATE_DONE, + GateDecision.GATE_FAIL, GateDecision.GATE_SUPPLEMENT + }, + "FINAL_CHECK": {GateDecision.GATE_DONE, GateDecision.GATE_FAIL}, + }[scene] + return candidate if candidate in allowed else None + + def _get_worker_report(self, subtask) -> str: + """Extract the latest worker-reported reason from subtask. + + Priority: + 1) subtask.last_reason_text + 2) the latest entry in subtask.reasons_history[].text + 3) empty string + """ + if not subtask: + return "" + # 1) direct field + if isinstance(subtask, dict): + text = subtask.get("last_reason_text") + if isinstance(text, str) and text.strip(): + return text.strip() + else: + text_val = getattr(subtask, "last_reason_text", None) + if isinstance(text_val, str) and text_val.strip(): + return text_val.strip() + + # 2) history fallback + hist = subtask.get("reasons_history") if isinstance( + subtask, dict) else getattr(subtask, "reasons_history", []) + if isinstance(hist, list) and hist: + try: + + def get_at(entry): + if isinstance(entry, dict): + return entry.get("at", "") + return getattr(entry, "at", "") + + latest = max(hist, key=lambda x: get_at(x)) + if isinstance(latest, dict): + t = latest.get("text", "") + else: + t = getattr(latest, "text", "") + return t.strip() if isinstance(t, str) else "" + except Exception: + pass + return "" + + # ========= Output parsing helpers ========= + def parse_llm_output(self, text: str) -> dict: + """Parse the model output into fields expected by controller and storage. + + Expected keys per scene (subset used per decision): + - Decision: required + - Reason: short text + - Global Impact: analysis of overall task impact + - Strategic Recommendations: suggestions for task optimization + - Suggestion: optional in STALE + - Risk Alert: optional in PERIODIC_CHECK + - Incomplete Items: optional in FINAL_CHECK + """ + result: dict[str, str] = {} + if not text: + return result + lines = [ln.strip() for ln in text.splitlines() if ln.strip()] + for ln in lines: + if ln.lower().startswith("decision:"): + result["decision"] = ln.split(":", 1)[1].strip() + elif ln.lower().startswith("reason:"): + result["reason"] = ln.split(":", 1)[1].strip() + elif ln.lower().startswith("global impact:"): + result["global_impact"] = ln.split(":", 1)[1].strip() + elif ln.lower().startswith("strategic recommendations:"): + result["strategic_recommendations"] = ln.split(":", 1)[1].strip() + elif ln.lower().startswith("suggestion:"): + result["suggestion"] = ln.split(":", 1)[1].strip() + elif ln.lower().startswith("risk alert:"): + result["risk_alert"] = ln.split(":", 1)[1].strip() + elif ln.lower().startswith("incomplete items:"): + result["incomplete_items"] = ln.split(":", 1)[1].strip() + return result + + def _trigger_code_to_scene(self, trigger_code: str) -> str: + """Map trigger code to scene for input collection.""" + mapping = { + TriggerCode.WORKER_SUCCESS.value: "WORKER_SUCCESS", + TriggerCode.WORKER_STALE_PROGRESS.value: "WORKER_STALE", + TriggerCode.RULE_QUALITY_CHECK_STEPS.value: "PERIODIC_CHECK", + TriggerCode.RULE_QUALITY_CHECK_REPEATED_ACTIONS.value: "PERIODIC_CHECK", + TriggerCode.ALL_SUBTASKS_COMPLETED.value: "FINAL_CHECK", + } + return mapping.get(trigger_code, "PERIODIC_CHECK") + + def _get_context_info_by_trigger_code(self, trigger_code: str) -> str: + """Return detailed guidance text per trigger code. + Mirrors the system architecture trigger guidance philosophy. + """ + if trigger_code == TriggerCode.WORKER_SUCCESS.value: + return ( + "# Worker Success - Verification Guidance\n" + "- Worker claims the current subtask is completed; rigorously verify completeness\n" + "- Cross-check each subtask requirement with clear evidence of completion\n" + "- Verify there is explicit success feedback for key steps\n" + "- If evidence is insufficient or inconsistent, choose gate_fail and explain why\n" + "- Consider how this subtask completion affects overall task progress and other subtasks\n" + "- Provide strategic insights for optimizing the overall task execution\n" + ) + if trigger_code == TriggerCode.WORKER_STALE_PROGRESS.value: + return ( + "# Worker Stale - Diagnosis Guidance\n" + "- Diagnose causes of stagnation: element not found, error dialogs, loops, missing credentials, etc.\n" + "- Assess completed progress versus remaining path and decide feasibility of continuation\n" + "- If information is missing, specify the required supplement materials and their purpose\n" + "- If continuation is feasible, provide breakthrough suggestions; otherwise recommend replanning\n" + "- Analyze how this stagnation affects overall task timeline and success probability\n" + "- Identify lessons learned that could prevent similar issues in other subtasks\n" + "- Recommend strategic changes to overall task execution plan if needed\n" + ) + if trigger_code == TriggerCode.RULE_QUALITY_CHECK_STEPS.value: + return ( + "# Periodic Check - Health Monitoring Guidance\n" + "- Identify the current execution stage and whether it matches expectations\n" + "- Detect repetitive ineffective operations or obvious deviation from the target\n" + "- Prefer early intervention when early risks are detected\n" + "- Allowed decisions: gate_continue / gate_done / gate_fail / gate_supplement\n" + "- Evaluate overall task progress and timeline health from a strategic perspective\n" + "- Identify recurring issues across multiple subtasks and recommend optimizations\n" + "- Assess whether the overall task strategy needs adjustment\n" + ) + if trigger_code == TriggerCode.RULE_QUALITY_CHECK_REPEATED_ACTIONS.value: + return ( + "# Repeated Actions Check - Repetitive Behavior Detection Guidance\n" + "- This check was triggered because the last several commands are identical or highly similar\n" + "- Analyze the repetitive pattern to determine if it indicates stuck behavior or systematic approach\n" + "- Look for signs of: element not found, permission denied, network errors, or UI state issues\n" + "- If the worker appears stuck, recommend specific breakthrough strategies or replanning\n" + "- If missing information is causing repetition, specify required supplement materials\n" + "- Allowed decisions: gate_continue (if repetition is justified) / gate_fail (if stuck) / gate_supplement (if missing info) / gate_done (if actually completed)\n" + "- Provide specific recommendations for breaking out of the repetitive cycle\n" + "- Consider how this repetition affects overall task timeline and success probability\n" + ) + if trigger_code == TriggerCode.ALL_SUBTASKS_COMPLETED.value: + return ( + "# Final Check - Completion Verification Guidance\n" + "- Verify DoD/acceptance criteria item by item and cross-subtask consistency\n" + "- Check whether the final UI/result aligns with the user objective\n" + "- If core functionality is missing or evidence is insufficient, choose gate_fail and list the major missing items\n" + "- Evaluate the efficiency and effectiveness of the entire task execution\n" + "- Provide strategic insights and lessons learned for future task improvements\n" + "- Recommend optimizations for similar task planning and execution\n" + ) + return ( + "# General Check - Guidance\n" + "- Analyze the current context and history to make a robust judgment\n" + "- Stay conservative when uncertain and provide clear reasons\n" + "- Always consider the broader task context and long-term strategy\n" + "- Provide strategic insights for overall task optimization\n" + ) + + def _normalize_decision_by_trigger_code(self, decision_text: str, trigger_code: str) -> Optional[GateDecision]: + """Normalize decision text to GateDecision enum based on trigger code.""" + if not decision_text: + return None + d = decision_text.strip().lower() + # Accept raw or bracketed + d = d.replace("[", "").replace("]", "") + # Allow synonyms + synonyms = { + "gate_done": GateDecision.GATE_DONE, + "done": GateDecision.GATE_DONE, + "gate_fail": GateDecision.GATE_FAIL, + "fail": GateDecision.GATE_FAIL, + "gate_supplement": GateDecision.GATE_SUPPLEMENT, + "supplement": GateDecision.GATE_SUPPLEMENT, + "gate_continue": GateDecision.GATE_CONTINUE, + "continue": GateDecision.GATE_CONTINUE, + } + candidate = synonyms.get(d) + if candidate is None: + return None + + # Enforce allowed set per trigger code + allowed = { + TriggerCode.WORKER_SUCCESS.value: {GateDecision.GATE_DONE, GateDecision.GATE_FAIL}, + TriggerCode.WORKER_STALE_PROGRESS.value: { + GateDecision.GATE_CONTINUE, GateDecision.GATE_FAIL, + GateDecision.GATE_SUPPLEMENT + }, + TriggerCode.RULE_QUALITY_CHECK_STEPS.value: { + GateDecision.GATE_CONTINUE, GateDecision.GATE_DONE, + GateDecision.GATE_FAIL, GateDecision.GATE_SUPPLEMENT + }, + TriggerCode.RULE_QUALITY_CHECK_REPEATED_ACTIONS.value: { + GateDecision.GATE_CONTINUE, GateDecision.GATE_DONE, + GateDecision.GATE_FAIL, GateDecision.GATE_SUPPLEMENT + }, + TriggerCode.ALL_SUBTASKS_COMPLETED.value: {GateDecision.GATE_DONE, GateDecision.GATE_FAIL}, + }.get(trigger_code, {GateDecision.GATE_CONTINUE, GateDecision.GATE_DONE, GateDecision.GATE_FAIL, GateDecision.GATE_SUPPLEMENT}) + + return candidate if candidate in allowed else None + + def _trigger_code_to_gate_trigger(self, trigger_code: str) -> GateTrigger: + """Map trigger code to GateTrigger enum.""" + mapping = { + TriggerCode.WORKER_SUCCESS.value: GateTrigger.WORKER_SUCCESS, + TriggerCode.WORKER_STALE_PROGRESS.value: GateTrigger.WORKER_STALE, + TriggerCode.RULE_QUALITY_CHECK_STEPS.value: GateTrigger.PERIODIC_CHECK, + TriggerCode.RULE_QUALITY_CHECK_REPEATED_ACTIONS.value: GateTrigger.PERIODIC_CHECK, + TriggerCode.ALL_SUBTASKS_COMPLETED.value: GateTrigger.FINAL_CHECK, + } + return mapping.get(trigger_code, GateTrigger.PERIODIC_CHECK) + + def _get_context_info_by_trigger(self, scene: str) -> str: + """Return detailed guidance text per evaluator trigger scene. + Mirrors the system architecture trigger guidance philosophy. + """ + if scene == "WORKER_SUCCESS": + return ( + "# Worker Success - Verification Guidance\n" + "- Worker claims the current subtask is completed; rigorously verify completeness\n" + "- Cross-check each subtask requirement with clear evidence of completion\n" + "- Verify there is explicit success feedback for key steps\n" + "- If evidence is insufficient or inconsistent, choose gate_fail and explain why\n" + "- Consider how this subtask completion affects overall task progress and other subtasks\n" + "- Provide strategic insights for optimizing the overall task execution\n" + ) + if scene == "WORKER_STALE": + return ( + "# Worker Stale - Diagnosis Guidance\n" + "- Diagnose causes of stagnation: element not found, error dialogs, loops, missing credentials, etc.\n" + "- Assess completed progress versus remaining path and decide feasibility of continuation\n" + "- If information is missing, specify the required supplement materials and their purpose\n" + "- If continuation is feasible, provide breakthrough suggestions; otherwise recommend replanning\n" + "- Analyze how this stagnation affects overall task timeline and success probability\n" + "- Identify lessons learned that could prevent similar issues in other subtasks\n" + "- Recommend strategic changes to overall task execution plan if needed\n" + ) + if scene == "PERIODIC_CHECK": + return ( + "# Periodic Check - Health Monitoring Guidance\n" + "- Identify the current execution stage and whether it matches expectations\n" + "- Detect repetitive ineffective operations or obvious deviation from the target\n" + "- Prefer early intervention when early risks are detected\n" + "- Allowed decisions: gate_continue / gate_done / gate_fail / gate_supplement\n" + "- Evaluate overall task progress and timeline health from a strategic perspective\n" + "- Identify recurring issues across multiple subtasks and recommend optimizations\n" + "- Assess whether the overall task strategy needs adjustment\n" + ) + if scene == "FINAL_CHECK": + return ( + "# Final Check - Completion Verification Guidance\n" + "- Verify DoD/acceptance criteria item by item and cross-subtask consistency\n" + "- Check whether the final UI/result aligns with the user objective\n" + "- If core functionality is missing or evidence is insufficient, choose gate_fail and list the major missing items\n" + "- Evaluate the efficiency and effectiveness of the entire task execution\n" + "- Provide strategic insights and lessons learned for future task improvements\n" + "- Recommend optimizations for similar task planning and execution\n" + ) + return ( + "# General Check - Guidance\n" + "- Analyze the current context and history to make a robust judgment\n" + "- Stay conservative when uncertain and provide clear reasons\n" + "- Always consider the broader task context and long-term strategy\n" + "- Provide strategic insights for overall task optimization\n" + ) diff --git a/mm_agents/maestro/maestro/grounding.py b/mm_agents/maestro/maestro/grounding.py new file mode 100644 index 0000000..1a0a2cc --- /dev/null +++ b/mm_agents/maestro/maestro/grounding.py @@ -0,0 +1,494 @@ +import ast +import re +import logging +from collections import defaultdict +from io import BytesIO +from typing import Any, Dict, List, Optional, Tuple, Union +import time +import pytesseract +from PIL import Image +from pytesseract import Output + +from ..tools.new_tools import NewTools +from ..utils.common_utils import parse_single_code_from_string + +logger = logging.getLogger("desktopenv.agent") + + +class ACI: + + def __init__(self): + self.notes: List[str] = [] + + +def agent_action(func): + func.is_agent_action = True + return func + + +class Grounding(ACI): + + def __init__( + self, + Tools_dict: Dict, + platform: str, + global_state=None, + width: int = 1920, + height: int = 1080, + ): + self.platform = platform + self.Tools_dict = Tools_dict + self.global_state = global_state + self.width = width + self.height = height + self.coords1 = None + self.coords2 = None + + self.grounding_model = NewTools() + self.grounding_model.register_tool( + "grounding", self.Tools_dict["grounding"]["provider"], + self.Tools_dict["grounding"]["model"]) + + self.grounding_width, self.grounding_height = self.grounding_model.tools[ + "grounding"].get_grounding_wh() + if self.grounding_width is None or self.grounding_height is None: + self.grounding_width = self.width + self.grounding_height = self.height + + self.text_span_agent = NewTools() + self.text_span_agent.register_tool( + "text_span", self.Tools_dict["text_span"]["provider"], + self.Tools_dict["text_span"]["model"]) + + def generate_coords(self, ref_expr: str, obs: Dict) -> List[int]: + grounding_start_time = time.time() + self.grounding_model.tools["grounding"].llm_agent.reset() + prompt = ( + f"Task: Visual Grounding - Locate and return coordinates\n" + f"Query: {ref_expr}\n" + "Instructions: " + "1. Carefully analyze the provided screenshot image. " + "2. Locate the EXACT element/area described in the query. " + "3. Return ONLY the pixel coordinates [x, y] of one representative point strictly inside the target area. " + "4. Choose a point that is clearly inside the described element/region " + "5. Coordinates must be integers representing pixel positions on the image. " + "6. If the described element has multiple instances, select the most prominent or central one " + "7. If this appears to be for dragging (selecting text, moving items, etc.): For START points: Position slightly to the LEFT of text/content in empty space For END points: Position slightly to the RIGHT of text/content in empty space Avoid placing coordinates directly ON text characters to prevent text selection issues Keep offset minimal (3-5 pixels) - don't go too far from the target area Still return only ONE coordinate as requested \nStill return only ONE coordinate as requested \n" + "Output Format: Return only two integers separated by comma, like: (900, 400)\n" + "Important Notes: " + "- Focus on the main descriptive elements in the query (colors, positions, objects) " + "- Ignore any additional context " + "- The returned point should be clickable/actionable within the target area \n" + "CRITICAL REQUIREMENTS: " + "- MUST return exactly ONE coordinate pair under ALL circumstances " + "- NO explanations, NO multiple coordinates, NO additional text \n") + response, total_tokens, cost_string = self.grounding_model.execute_tool( + "grounding", { + "str_input": prompt, + "img_input": obs["screenshot"] + }) + logger.info( + f"Grounding model tokens: {total_tokens}, cost: {cost_string}") + grounding_end_time = time.time() + grounding_duration = grounding_end_time - grounding_start_time + logger.info( + f"Grounding model execution time: {grounding_duration:.2f} seconds") + logger.info(f"RAW GROUNDING MODEL RESPONSE: {response}") + if self.global_state: + self.global_state.log_llm_operation( + module="grounding", + operation="grounding_model_response", + data={ + "tokens": total_tokens, + "cost": cost_string, + "content": response, + "duration": grounding_duration + }, + str_input=prompt, + img_input=obs["screenshot"] + ) + numericals = re.findall(r"\d+", response) + assert len(numericals) >= 2 + return [int(numericals[0]), int(numericals[1])] + + def assign_coordinates(self, plan: str, obs: Dict): + self.coords1, self.coords2 = None, None + try: + action = parse_single_code_from_string( + plan.split("Grounded Action")[-1]) + function_name = re.match(r"(\w+\.\w+)\(", + action).group(1) # type: ignore + args = self.parse_function_args(action) + except Exception as e: + raise RuntimeError(f"Error in parsing grounded action: {e}") from e + + if (function_name in [ + "agent.click", "agent.doubleclick", "agent.move", "agent.scroll", "agent.type" + ] and len(args) >= 1 and args[0] is not None and str(args[0]).strip() != ""): + self.coords1 = self.generate_coords(args[0], obs) + elif function_name == "agent.drag" and len(args) >= 2: + self.coords1 = self.generate_coords(args[0], obs) + self.coords2 = self.generate_coords(args[1], obs) + + def reset_screen_size(self, width: int, height: int): + self.width = width + self.height = height + + def resize_coordinates(self, coordinates: List[int]) -> List[int]: + return [ + round(coordinates[0] * self.width / self.grounding_width), + round(coordinates[1] * self.height / self.grounding_height), + ] + + def resize_coordinates_with_padding(self, + coordinates: List[int]) -> List[int]: + grounding_size = max(self.grounding_width, self.grounding_height) + original_size = max(self.width, self.height) + coordinates = [ + round(coordinates[0] * original_size / grounding_size), + round(coordinates[1] * original_size / grounding_size), + ] + padding_left = round((original_size - self.width) / 2) + padding_top = round((original_size - self.height) / 2) + return [ + coordinates[0] - padding_left, + coordinates[1] - padding_top, + ] + + def parse_function_args(self, function: str) -> List[str]: + if not function or not isinstance(function, str): + return [] + pattern = r'(\w+\.\w+)\((?:"([^"]*)")?(?:,\s*(\d+))?\)' + match = re.match(pattern, function) + if match: + args = [] + if match.group(2) is not None: + args.append(match.group(2)) + if match.group(3) is not None: + args.append(int(match.group(3))) + if args: + return args + try: + tree = ast.parse(function) + except Exception: + return [] + if not tree.body or not hasattr(tree.body[0], 'value'): + return [] + call_node = tree.body[0].value # type: ignore + if not isinstance(call_node, ast.Call): + return [] + + def safe_eval(node): + if isinstance(node, ast.Constant): + return node.value + elif hasattr(ast, 'Str') and isinstance(node, ast.Str): + return node.s + else: + try: + return ast.unparse(node) + except Exception: + return str(node) + + positional_args = [] + try: + positional_args = [safe_eval(arg) for arg in call_node.args] + except Exception: + positional_args = [] + keyword_args = {} + try: + keyword_args = { + kw.arg: safe_eval(kw.value) for kw in call_node.keywords + } + except Exception: + keyword_args = {} + res = [] + for key, val in keyword_args.items(): + if key and "description" in key: + res.append(val) + for arg in positional_args: + res.append(arg) + return res + + def _record_passive_memory(self, action_type: str, action_details: str): + memory_content = f"Hardware action `{action_type}` has been executed. Details: {action_details}" + + @agent_action + def click( + self, + element_description: str, + button: int = 1, + holdKey: List[str] = [], + ): + x, y = self.resize_coordinates(self.coords1) # type: ignore + actionDict = { + "type": "Click", + "x": x, + "y": y, + "element_description": element_description, + "button": button, + "holdKey": holdKey + } + action_details = f"Clicked at coordinates ({x}, {y}) with button {button}, element: {element_description}" + self._record_passive_memory("Click", action_details) + return actionDict + + @agent_action + def doubleclick( + self, + element_description: str, + button: int = 1, + holdKey: List[str] = [], + ): + x, y = self.resize_coordinates(self.coords1) # type: ignore + actionDict = { + "type": "DoubleClick", + "x": x, + "y": y, + "element_description": element_description, + "button": button, + "holdKey": holdKey + } + action_details = f"Double clicked at coordinates ({x}, {y}) with button {button}, element: {element_description}" + self._record_passive_memory("DoubleClick", action_details) + return actionDict + + @agent_action + def move( + self, + element_description: str, + holdKey: List[str] = [], + ): + x, y = self.resize_coordinates(self.coords1) # type: ignore + actionDict = { + "type": "Move", + "x": x, + "y": y, + "element_description": element_description, + "holdKey": holdKey + } + action_details = f"Moved to coordinates ({x}, {y}), element: {element_description}" + self._record_passive_memory("Move", action_details) + return actionDict + + @agent_action + def scroll( + self, + element_description: str, + clicks: int, + vertical: bool = True, + holdKey: List[str] = [], + ): + x, y = self.resize_coordinates(self.coords1) # type: ignore + if vertical: + actionDict = { + "type": "Scroll", + "x": x, + "y": y, + "element_description": element_description, + "stepVertical": clicks, + "holdKey": holdKey + } + action_details = f"Scrolled vertically at coordinates ({x}, {y}) with {clicks} clicks, element: {element_description}" + else: + actionDict = { + "type": "Scroll", + "x": x, + "y": y, + "element_description": element_description, + "stepHorizontal": -clicks, + "holdKey": holdKey + } + action_details = f"Scrolled horizontally at coordinates ({x}, {y}) with {clicks} clicks (mapped to {-clicks}), element: {element_description}" + self._record_passive_memory("Scroll", action_details) + return actionDict + + @agent_action + def drag( + self, + starting_description: str, + ending_description: str, + holdKey: List[str] = [], + ): + x1, y1 = self.resize_coordinates(self.coords1) # type: ignore + x2, y2 = self.resize_coordinates(self.coords2) # type: ignore + actionDict = { + "type": "Drag", + "startX": x1, + "startY": y1, + "endX": x2, + "endY": y2, + "holdKey": holdKey, + "starting_description": starting_description, + "ending_description": ending_description + } + action_details = f"Dragged from ({x1}, {y1}) to ({x2}, {y2}), starting: {starting_description}, ending: {ending_description}" + self._record_passive_memory("Drag", action_details) + return actionDict + + @agent_action + def type( + self, + element_description: Optional[str] = None, + text: str = "", + overwrite: bool = False, + enter: bool = False, + ): + # 若提供 element_description 并已在 assign_coordinates 中得到 coords1，则下发坐标 + payload: Dict[str, Any] = { + "type": "TypeText", + "text": text, + "overwrite": overwrite, + "enter": enter, + } + if element_description and self.coords1 is not None: + x, y = self.resize_coordinates(self.coords1) # type: ignore + payload.update({ + "x": x, + "y": y, + "element_description": element_description, + }) + + action_details = f"Type text with params: element={element_description}, overwrite={overwrite}, enter={enter}, text={text}" + self._record_passive_memory("TypeText", action_details) + return payload + + @agent_action + def hotkey( + self, + keys: List[str] = [], + duration: int = 0, + ): + keys = [f"{key}" for key in keys] + if 1 <= duration <= 5000: + actionDict = { + "type": "Hotkey", + "keys": keys, + "duration": duration, + } + action_details = f"Pressed hotkey combination: {', '.join(keys)} with duration {duration}ms" + else: + actionDict = { + "type": "Hotkey", + "keys": keys, + } + action_details = f"Pressed hotkey combination: {', '.join(keys)}" + self._record_passive_memory("Hotkey", action_details) + return actionDict + + @agent_action + def wait(self, duration: int): + actionDict = {"type": "Wait", "duration": duration} + action_details = f"Waited for {duration} milliseconds" + self._record_passive_memory("Wait", action_details) + return actionDict + + @agent_action + def done( + self, + message: str = '', + ): + self.returned_info = message + actionDict = {"type": "Done", "message": message} + return actionDict + + @agent_action + def fail( + self, + message: str = '', + ): + actionDict = {"type": "Failed", "message": message} + return actionDict + + @agent_action + def supplement( + self, + message: str = '', + ): + actionDict = {"type": "Supplement", "message": message} + return actionDict + + @agent_action + def need_quality_check( + self, + message: str = '', + ): + actionDict = {"type": "NeedQualityCheck", "message": message} + return actionDict + + @agent_action + def memorize( + self, + information: str, + memory_type: str = "active", + ): + actionDict = { + "type": "Memorize", + "information": information, + } + return actionDict + + @agent_action + def passive_memorize( + self, + information: str, + ): + return self.memorize(information, memory_type="passive") + + @agent_action + def user_takeover( + self, + message: str = '', + ): + # self.global_state.set_running_state("stopped") + actionDict = {"type": "UserTakeover", "message": message} + return actionDict + + @agent_action + def set_cell_values( + self, + cell_values: Dict[str, Any], + app_name: str, + sheet_name: str, + ): + if str(self.platform).lower() == "windows": + raise RuntimeError( + "set_cell_values is not supported on Windows in agents3") + actionDict = { + "type": "SetCellValues", + "cell_values": cell_values, + "app_name": app_name, + "sheet_name": sheet_name, + } + self._record_passive_memory( + "SetCellValues", + f"Set values in app '{app_name}', sheet '{sheet_name}', cells: {list(cell_values.keys())}", + ) + return actionDict + + @agent_action + def switch_applications(self, app_code: str): + actionDict = { + "type": "SwitchApplications", + "app_code": app_code, + } + self._record_passive_memory( + "SwitchApplications", + f"Switch to application '{app_code}' on platform '{self.platform}'", + ) + return actionDict + + @agent_action + def switch_app(self, app_code: str): + return self.switch_applications(app_code) + + @agent_action + def open(self, app_or_filename: str): + actionDict = { + "type": "Open", + "app_or_filename": app_or_filename, + } + self._record_passive_memory( + "Open", + f"Open app or file '{app_or_filename}' on platform '{self.platform}'", + ) + return actionDict diff --git a/mm_agents/maestro/maestro/hardware_interface.py b/mm_agents/maestro/maestro/hardware_interface.py new file mode 100644 index 0000000..adc92f1 --- /dev/null +++ b/mm_agents/maestro/maestro/hardware_interface.py @@ -0,0 +1,124 @@ +from __future__ import annotations + +import pyautogui +from .Backend.Backend import Backend +from .Backend.ADBBackend import ADBBackend +from .Backend.PyAutoGUIBackend import PyAutoGUIBackend +from .Backend.PyAutoGUIVMwareBackend import PyAutoGUIVMwareBackend +"""hardware_interface.py ▸ Execute Action objects on real devices / emulators +============================================================================== +This module is the *single entry point* that upper‑layer planners / executors +use to perform UI operations. It is deliberately thin: + +* Accepts one `Action` **or** a `List[Action]` (defined in *actions.py*). +* Delegates to a concrete *Backend* which knows how to translate the `Action` + into platform‑specific calls (PyAutoGUI, ADB, Lybic cloud device, …). +* Performs minimal capability checks + error propagation. + +The default backend implemented here is **PyAutoGUIBackend**. Stubs for +**ADBBackend** and **LybicBackend** show how to extend the system. + +-------------------------------------------------------------------------- +Quick usage +-------------------------------------------------------------------------- +```python +from actions import Click +from hardware_interface import HardwareInterface + +hwi = HardwareInterface(backend="pyautogui") + +# Single action +hwi.dispatch(Click(xy=(960, 540))) + +# Batch +plan = [Click(xy=(100,200)), Click(xy=(300,400))] +hwi.dispatch(plan) + +# actionDict +hwi.dispatchDict({"type": "Click", "xy": [200, 300]}) + +``` +""" + +from typing import List, Type, Dict, Set, Union, Any + +# Import your Action primitives +from .Action import ( + Action, + Screenshot, +) + +__all__ = [ + "HardwareInterface", + "Backend", + "PyAutoGUIBackend", + "ADBBackend", + "PyAutoGUIVMwareBackend", +] + + + +# --------------------------------------------------------------------------- +# Facade – single entry point +# --------------------------------------------------------------------------- +class HardwareInterface: + """High‑level facade that routes Action objects to a chosen backend.""" + + BACKEND_MAP: Dict[str, Type[Backend]] = { + "pyautogui": PyAutoGUIBackend, + "adb": ADBBackend, + "pyautogui_vmware": PyAutoGUIVMwareBackend, + } + + # ------------------------------------------------------------------ + def __init__(self, backend: str | Backend = "pyautogui", **backend_kwargs): + if isinstance(backend, Backend): + self.backend: Backend = backend + else: + key = backend.lower() + if key not in self.BACKEND_MAP: + raise ValueError(f"Unsupported backend '{backend}'. Available: {list(self.BACKEND_MAP)}") + self.backend = self.BACKEND_MAP[key](**backend_kwargs) + + # ------------------------------------------------------------------ + def dispatch(self, actions: Action | List[Action]): + """Execute one or multiple actions *in order*. + + Args: + actions: `Action` instance or list thereof. + """ + if isinstance(actions, Action): + actions = [actions] + + for act in actions: + # Special handling for Memorize action, do not pass to backend execution + if type(act).__name__ == "Memorize": + continue + if not self.backend.supports(type(act)): + raise NotImplementedError( + f"{type(act).__name__} is not supported by backend {self.backend.__class__.__name__}" + ) + if (not isinstance(actions, list)) or (len(actions)==1): + result = self.backend.execute(act) + # If a single action returns a value (e.g., Screenshot), propagate it + return result + else: + self.backend.execute(act) + # For batch execution with no explicit return + return None + + def dispatchDict(self, actionDict: Union[Dict[str, Any], List[Dict[str, Any]]]): + """Execute one or multiple actions provided as JSON‑style dict(s). + + Parameters + ---------- + actionDict : Dict[str, Any] | List[Dict[str, Any]] + - Dict: single action, e.g. {"type": "Click", "xy": [100,200], ...} + - List: sequence of actions in the above format + """ + if isinstance(actionDict, list): + actions = [Action.from_dict(item) for item in actionDict] + else: + actions = Action.from_dict(actionDict) + + return self.dispatch(actions) diff --git a/mm_agents/maestro/maestro/manager/__init__.py b/mm_agents/maestro/maestro/manager/__init__.py new file mode 100644 index 0000000..8b7c640 --- /dev/null +++ b/mm_agents/maestro/maestro/manager/__init__.py @@ -0,0 +1,49 @@ +""" +Manager module for GUI-Agent architecture +Provides task planning, decomposition, and resource allocation functionality +""" + +from .plan import PlanningHandler, PlanningScenario, PlanningResult +from .supplement import SupplementHandler +from .planning_helpers import ( + get_planning_context, get_trigger_code_specific_context, + generate_planning_prompt, generate_trigger_specific_guidance +) +from .utils import ( + enhance_subtasks, generate_dag, topological_sort, + get_failed_subtasks_info, get_failure_reasons, get_history_subtasks_info, + get_pending_subtasks_info, count_subtasks_from_info, get_current_failed_subtask, + get_quality_check_failure_info, get_final_check_failure_info, + get_execution_time_info, get_supplement_info +) + +__all__ = [ + # Planning + 'PlanningHandler', + 'PlanningScenario', + 'PlanningResult', + + # Planning Helpers + 'get_planning_context', + 'get_trigger_code_specific_context', + 'generate_planning_prompt', + 'generate_trigger_specific_guidance', + + # Supplement + 'SupplementHandler', + + # Utilities + 'enhance_subtasks', + 'generate_dag', + 'topological_sort', + 'get_failed_subtasks_info', + 'get_failure_reasons', + 'get_history_subtasks_info', + 'get_pending_subtasks_info', + 'count_subtasks_from_info', + 'get_current_failed_subtask', + 'get_quality_check_failure_info', + 'get_final_check_failure_info', + 'get_execution_time_info', + 'get_supplement_info' +] diff --git a/mm_agents/maestro/maestro/manager/plan.py b/mm_agents/maestro/maestro/manager/plan.py new file mode 100644 index 0000000..57b9f81 --- /dev/null +++ b/mm_agents/maestro/maestro/manager/plan.py @@ -0,0 +1,432 @@ +""" +Planning module for Manager +Handles task planning, DAG generation, and context building +""" + +import json +import logging +import time +from datetime import datetime +from typing import Dict, List, Optional, Any +from enum import Enum +from dataclasses import dataclass +import re + +from ...utils.common_utils import Node +from ..data_models import SubtaskData +from ..manager.utils import ( + enhance_subtasks, generate_dag, topological_sort +) +from .planning_helpers import ( + get_planning_context, generate_planning_prompt +) + +logger = logging.getLogger(__name__) + + +class PlanningScenario(str, Enum): + """Planning scenario types""" + REPLAN = "replan" + SUPPLEMENT = "supplement" + + +@dataclass +class PlanningResult: + """Planning result data structure""" + success: bool + scenario: str + subtasks: List[Dict] + supplement: str + reason: str + created_at: str + + +class PlanningHandler: + """Handles task planning and DAG generation""" + + def __init__(self, global_state, planner_agent, dag_translator_agent, + knowledge_base, search_engine, platform, enable_search, enable_narrative, + objective_alignment_agent=None): + self.global_state = global_state + self.planner_agent = planner_agent + self.dag_translator_agent = dag_translator_agent + self.knowledge_base = knowledge_base + self.search_engine = search_engine + self.platform = platform + self.enable_search = enable_search + self.enable_narrative = enable_narrative + self.objective_alignment_agent = objective_alignment_agent + self.planning_history = [] + self.replan_attempts = 0 + + def handle_planning_scenario(self, scenario: PlanningScenario, trigger_code: str = "controller") -> PlanningResult: + """Handle planning scenarios (INITIAL_PLAN/REPLAN) with specific trigger_code context""" + # Get planning context with trigger_code + context = get_planning_context( + self.global_state, + self.platform, + self.replan_attempts, + self.planning_history, + trigger_code + ) + + # Step 0: Align/Rewrite objective using current screenshot if tool is available + try: + if self.objective_alignment_agent is not None: + raw_objective = context.get("task_objective", "") + screenshot = context.get("screenshot") + recent_subtasks_history = context.get("recent_subtasks_history") + # Construct complete alignment prompt with objective and recent history + alignment_prompt = "Original objective: " + raw_objective + if recent_subtasks_history: + alignment_prompt += "\n\nRecent subtasks history: " + str(recent_subtasks_history) + + if isinstance(raw_objective, str) and raw_objective.strip(): + aligned_text, a_tokens, a_cost = self.objective_alignment_agent.execute_tool( + "objective_alignment", + {"str_input": alignment_prompt, "img_input": screenshot} + ) + logger.info(f"Alignment Done.") + # Update context for downstream prompt generation + if isinstance(aligned_text, str) and aligned_text.strip() and not aligned_text.startswith("Error:"): + context["objective_alignment_raw"] = aligned_text + # Try parse JSON to extract final objective text and assumptions (robust against ```json ... ``` and extra text) + refined_obj = None + assumptions = None + constraints_from_screen = None + try: + import json as _json + _text = aligned_text.strip() + # Strip markdown fences if present + if _text.startswith("```"): + # Remove opening fence line (e.g., ```json or ```) + nl = _text.find("\n") + if nl != -1: + _candidate = _text[nl + 1 :] + else: + _candidate = _text + # Remove closing fence ``` if present + if _candidate.rstrip().endswith("```"): + _candidate = _candidate.rstrip()[:-3] + _text = _candidate.strip() + # If still fails, extract JSON object substring by braces + _parsed = None + try: + _parsed = _json.loads(_text) + except Exception: + lb = _text.find("{") + rb = _text.rfind("}") + if lb != -1 and rb != -1 and rb > lb: + _maybe = _text[lb: rb + 1] + try: + _parsed = _json.loads(_maybe) + except Exception: + _parsed = None + if isinstance(_parsed, dict): + # Extract all relevant fields from the parsed JSON + refined_obj = _parsed.get("rewritten_final_objective_text") + assumptions = _parsed.get("assumptions") + constraints_from_screen = _parsed.get("constraints_from_screen") + except Exception: + refined_obj = None + assumptions = None + constraints_from_screen = None + + if isinstance(refined_obj, str) and refined_obj.strip(): + context["objective_alignment"] = refined_obj + context["task_objective"] = refined_obj + else: + # Fallback: use full aligned_text as objective + context["objective_alignment"] = aligned_text + context["task_objective"] = aligned_text + + # Store assumptions and constraints for planning + # if assumptions is not None: + # context["objective_assumptions"] = assumptions + # if constraints_from_screen is not None: + # context["objective_constraints"] = constraints_from_screen + # Log the alignment action + self.global_state.log_llm_operation( + "manager", "objective_alignment", { + "tokens": a_tokens, + "cost": a_cost, + "llm_output": aligned_text, + }, + str_input=raw_objective + ) + except Exception as e: + logger.warning(f"Objective alignment step failed: {e}") + + + # Retrieve external knowledge (web + narrative) and optionally fuse + integrated_knowledge = self._retrieve_and_fuse_knowledge(context) + logger.info(f"Knowledge integrated.") + + # Generate planning prompt (with integrated knowledge if any) based on trigger_code + # Includes generic configuration persistence and role assignment guidance (see planning_helpers.generate_planning_prompt) + assumptions = context.get("objective_assumptions") + # constraints_from_screen = context.get("objective_constraints") + prompt = generate_planning_prompt( + context, + integrated_knowledge=integrated_knowledge, + trigger_code=trigger_code, + assumptions=assumptions, # type: ignore + # constraints_from_screen=constraints_from_screen + ) + + # Execute planning using the registered planner tool + plan_result, total_tokens, cost_string = self.planner_agent.execute_tool( + "planner_role", { + "str_input": prompt, + "img_input": context.get("screenshot") + } + ) + logger.info(f"Planner Executed.") + + # Parse manager completion flag from planner output and strip the flag line + manager_complete_flag = True + try: + match = re.search(r"^\s*MANAGER_COMPLETE:\s*(true|false)\s*$", str(plan_result), re.IGNORECASE | re.MULTILINE) + if match: + manager_complete_flag = match.group(1).lower() == "true" + # Remove the flag line from plan_result to avoid polluting downstream DAG translation + plan_result = re.sub(r"^\s*MANAGER_COMPLETE:\s*(true|false)\s*$", "", str(plan_result), flags=re.IGNORECASE | re.MULTILINE).strip() + except Exception: + manager_complete_flag = True + + # Log planning operation (reflect initial vs replan based on attempts) + scenario_label = context.get("planning_scenario", scenario.value) + self.global_state.log_llm_operation( + "manager", "task_planning", { + "scenario": scenario_label, + "trigger_code": trigger_code, + "plan_result": plan_result, + "tokens": total_tokens, + "cost": cost_string + }, + str_input=prompt, + # img_input=context.get("screenshot") + ) + + # After planning, also generate DAG and action queue + dag_info, dag_obj = generate_dag(self.dag_translator_agent, self.global_state, context.get("task_objective", ""), plan_result) + + # Add DAG retry mechanism + max_dag_retries = 3 + dag_retry_count = 0 + action_queue: List[Node] = [] + + while dag_retry_count < max_dag_retries: + try: + action_queue = topological_sort(dag_obj) + # Validate if sorting result is reasonable + if len(action_queue) == len(dag_obj.nodes): + logger.info(f"DAG topological sort successful on attempt {dag_retry_count + 1}") + break + else: + raise ValueError(f"Topological sort result length mismatch: expected {len(dag_obj.nodes)}, got {len(action_queue)}") + except Exception as e: + dag_retry_count += 1 + logger.warning(f"DAG topological sort failed on attempt {dag_retry_count}: {e}") + + if dag_retry_count < max_dag_retries: + # Regenerate DAG + logger.info(f"Regenerating DAG (attempt {dag_retry_count + 1}/{max_dag_retries})") + dag_info, dag_obj = generate_dag(self.dag_translator_agent, self.global_state, context.get("task_objective", ""), plan_result) + else: + # Last attempt failed, use original node order + logger.error(f"All DAG retries failed, using original node order") + action_queue = dag_obj.nodes + self.global_state.add_event("manager", "dag_retry_failed", f"Used original node order after {max_dag_retries} failed attempts") + + # Parse planning result + try: + # Validate and enhance subtasks + enhanced_subtasks = enhance_subtasks(action_queue, self.global_state.task_id) + + # Determine if we are in re-plan phase based on attempts + is_replan_now = context.get("planning_scenario") == "replan" + first_new_subtask_id: Optional[str] = None + + if is_replan_now: + # Remove all not-yet-completed (pending) subtasks + task = self.global_state.get_task() + old_pending_ids = list(task.pending_subtask_ids or []) + if old_pending_ids: + self.global_state.delete_subtasks(old_pending_ids) + + # Append new subtasks and capture the first new subtask id + for i, subtask_dict in enumerate(enhanced_subtasks): + subtask_data = SubtaskData( + subtask_id=subtask_dict["subtask_id"], + task_id=subtask_dict["task_id"], + title=subtask_dict["title"], + description=subtask_dict["description"], + assignee_role=subtask_dict["assignee_role"], + status=subtask_dict["status"], + attempt_no=subtask_dict["attempt_no"], + reasons_history=subtask_dict["reasons_history"], + command_trace_ids=subtask_dict["command_trace_ids"], + gate_check_ids=subtask_dict["gate_check_ids"], + last_reason_text=subtask_dict["last_reason_text"], + last_gate_decision=subtask_dict["last_gate_decision"], + created_at=subtask_dict["created_at"], + updated_at=subtask_dict["updated_at"], + ) + new_id = self.global_state.add_subtask(subtask_data) + if first_new_subtask_id is None: + first_new_subtask_id = new_id + # Update managerComplete after adding subtasks + try: + self.global_state.set_manager_complete(manager_complete_flag) + except Exception: + logger.warning("Failed to update managerComplete flag in global state during replan") + else: + # Initial planning: append new subtasks; set current only if not set + for subtask_dict in enhanced_subtasks: + subtask_data = SubtaskData( + subtask_id=subtask_dict["subtask_id"], + task_id=subtask_dict["task_id"], + title=subtask_dict["title"], + description=subtask_dict["description"], + assignee_role=subtask_dict["assignee_role"], + status=subtask_dict["status"], + attempt_no=subtask_dict["attempt_no"], + reasons_history=subtask_dict["reasons_history"], + command_trace_ids=subtask_dict["command_trace_ids"], + gate_check_ids=subtask_dict["gate_check_ids"], + last_reason_text=subtask_dict["last_reason_text"], + last_gate_decision=subtask_dict["last_gate_decision"], + created_at=subtask_dict["created_at"], + updated_at=subtask_dict["updated_at"], + ) + self.global_state.add_subtask(subtask_data) + # Update managerComplete after adding subtasks + try: + self.global_state.set_manager_complete(manager_complete_flag) + except Exception: + logger.warning("Failed to update managerComplete flag in global state during initial planning") + + # Update planning history + self.planning_history.append({ + "scenario": scenario_label, + "trigger_code": trigger_code, + "subtasks": enhanced_subtasks, + "dag": dag_info.get("dag", ""), + "action_queue_len": len(action_queue), + "timestamp": datetime.now().isoformat(), + "tokens": total_tokens, + "cost": cost_string + }) + + # Bump attempts after any successful planning to distinguish initial vs replan next time + self.replan_attempts += 1 + + return PlanningResult( + success=True, + scenario=scenario_label, + subtasks=enhanced_subtasks, + supplement="", + reason=f"Successfully planned {len(enhanced_subtasks)} subtasks with trigger_code: {trigger_code}", + created_at=datetime.now().isoformat() + ) + + except json.JSONDecodeError as e: + logger.error(f"Failed to parse planning result: {e}") + return PlanningResult( + success=False, + scenario=scenario_label, + subtasks=[], + supplement="", + reason=f"Failed to parse planning result: {str(e)}", + created_at=datetime.now().isoformat() + ) + + def _retrieve_and_fuse_knowledge(self, context: Dict[str, Any]) -> str: + """Retrieve external knowledge (web + narrative) and optionally fuse""" + integrated_knowledge = "" + web_knowledge = None + most_similar_task = "" + retrieved_experience = None + + try: + objective = context.get("task_objective", "") + observation = {"screenshot": context.get("screenshot")} + + search_query = None + if self.enable_search and self.search_engine: + try: + # 1) formulate_query + formulate_start = time.time() + search_query, f_tokens, f_cost = self.knowledge_base.formulate_query( + objective, observation) + formulate_duration = time.time() - formulate_start + self.global_state.log_operation( + "manager", "formulate_query", { + "tokens": f_tokens, + "cost": f_cost, + "query": search_query, + "duration": formulate_duration + }) + # 2) websearch directly using search_engine + if search_query: + web_knowledge, ws_tokens, ws_cost = self.search_engine.execute_tool( + "websearch", {"query": search_query}) + # Not all tools return token/cost; guard format + self.global_state.log_llm_operation( + "manager", "web_knowledge", { + "query": search_query, + "tokens": ws_tokens, + "cost": ws_cost + }, + str_input=search_query + ) + except Exception as e: + logger.warning(f"Web search retrieval failed: {e}") + + if self.enable_narrative: + try: + most_similar_task, retrieved_experience, n_tokens, n_cost = ( + self.knowledge_base.retrieve_narrative_experience( + objective)) + self.global_state.log_llm_operation( + "manager", "retrieve_narrative_experience", { + "tokens": n_tokens, + "cost": n_cost, + "task": most_similar_task + }, + str_input=objective + ) + except Exception as e: + logger.warning(f"Narrative retrieval failed: {e}") + + # 3) Conditional knowledge fusion + try: + do_fusion_web = web_knowledge is not None and str( + web_knowledge).strip() != "" + do_fusion_narr = retrieved_experience is not None and str( + retrieved_experience).strip() != "" + if do_fusion_web or do_fusion_narr: + web_text = web_knowledge if do_fusion_web else None + similar_task = most_similar_task if do_fusion_narr else "" + exp_text = retrieved_experience if do_fusion_narr else "" + integrated_knowledge, k_tokens, k_cost = self.knowledge_base.knowledge_fusion( + observation=observation, + instruction=objective, + web_knowledge=web_text, + similar_task=similar_task, + experience=exp_text, + ) + self.global_state.log_llm_operation("manager", + "knowledge_fusion", { + "tokens": k_tokens, + "cost": k_cost + }, + str_input=f"Objective: {objective}, Web: {web_text}, Experience: {exp_text}") + except Exception as e: + logger.warning(f"Knowledge fusion failed: {e}") + + except Exception as e: + logger.warning(f"Knowledge retrieval pipeline failed: {e}") + + return integrated_knowledge \ No newline at end of file diff --git a/mm_agents/maestro/maestro/manager/planning_helpers.py b/mm_agents/maestro/maestro/manager/planning_helpers.py new file mode 100644 index 0000000..65cb330 --- /dev/null +++ b/mm_agents/maestro/maestro/manager/planning_helpers.py @@ -0,0 +1,662 @@ +""" +Planning helper functions for Manager +Contains helper functions for context building, prompt generation, and trigger code handling +""" + +import logging +from typing import Dict, Any, List +import json + +from ..enums import TRIGGER_CODE_BY_MODULE, SubtaskStatus +from ..manager.utils import ( + get_failed_subtasks_info, get_failure_reasons, get_current_failed_subtask, + get_quality_check_failure_info, get_final_check_failure_info, + get_execution_time_info, get_supplement_info, count_subtasks_from_info +) +from ..new_global_state import NewGlobalState + +logger = logging.getLogger(__name__) + + +def get_planning_context(global_state, platform: str, replan_attempts: int, + planning_history: list, trigger_code: str) -> Dict[str, Any]: + """Get context information for planning with trigger_code specific details""" + task = global_state.get_task() + subtasks = global_state.get_subtasks() + screenshot = global_state.get_screenshot() + + is_replan_now = replan_attempts > 0 + + context = { + "task_objective": task.objective or "", + "task_status": task.status or "", + "all_subtasks": subtasks, + "history_subtasks": get_history_subtasks_info(global_state), + "pending_subtasks": get_pending_subtasks_info(global_state), + "screenshot": screenshot, + "platform": platform, + "planning_scenario": "replan" if is_replan_now else "initial_plan", + "replan_attempts": replan_attempts, + "planning_history": planning_history[-3:] if planning_history else [], + "trigger_code": trigger_code, + + "commands": global_state.get_commands(), + "gate_checks": global_state.get_gate_checks(), + "artifacts": global_state.get_artifacts(), + "supplement": global_state.get_supplement(), + } + + # Add failure information only when truly re-planning + if is_replan_now: + context["failed_subtasks"] = get_failed_subtasks_info(global_state) + context["failure_reasons"] = get_failure_reasons(global_state) + # Add recent subtasks operation history (last two) + context["recent_subtasks_history"] = get_recent_subtasks_operation_history(global_state, limit=2) + + # Add trigger_code specific context information + context.update(get_trigger_code_specific_context(global_state, trigger_code)) + + return context + + +def get_trigger_code_specific_context(global_state, trigger_code: str) -> Dict[str, Any]: + """Get trigger_code specific context information""" + context = {} + + if trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["work_cannot_execute"]: + # Worker cannot execute situation + context["trigger_context"] = { + "type": "worker_cannot_execute", + "description": "Worker reported that the current subtask cannot be executed", + "focus": "Need to analyze why the subtask cannot be executed and find alternative approaches" + } + # Get current failed subtask information + current_subtask = get_current_failed_subtask(global_state) + if current_subtask: + context["current_failed_subtask"] = current_subtask + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["quality_check_failed"]: + # Quality check failed situation + context["trigger_context"] = { + "type": "quality_check_failed", + "description": "Quality check failed for the current subtask", + "focus": "Need to understand why quality check failed and improve the approach" + } + # Get specific information about quality check failure + context["quality_check_failure"] = get_quality_check_failure_info(global_state) + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["no_worker_decision"]: + # Worker has no decision situation + context["trigger_context"] = { + "type": "no_worker_decision", + "description": "Worker could not make a decision for the current subtask", + "focus": "Need to provide clearer instructions or break down the subtask" + } + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["get_action_error"]: + # GET_ACTION state error situation + context["trigger_context"] = { + "type": "get_action_error", + "description": "Error occurred during GET_ACTION state processing", + "focus": "Need to handle the error and provide alternative approaches" + } + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["quality_check_error"]: + # Quality check error situation + context["trigger_context"] = { + "type": "quality_check_error", + "description": "Error occurred during quality check process", + "focus": "Need to handle the quality check error and continue with alternative approaches" + } + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["final_check_failed"]: + # Final quality check failed situation + context["trigger_context"] = { + "type": "final_check_failed", + "description": "Final quality check failed for the entire task", + "focus": "Need to address the final quality issues and complete the task" + } + # Get final quality check failure information + context["final_check_failure"] = get_final_check_failure_info(global_state) + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["rule_replan_long_execution"]: + # Long execution time requiring replanning situation + context["trigger_context"] = { + "type": "long_execution_replan", + "description": "Task has been executing for too long, need to replan", + "focus": "Need to optimize the execution plan and reduce execution time" + } + # Get execution time information + context["execution_time_info"] = get_execution_time_info(global_state) + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["no_subtasks"]: + # No subtasks situation + context["trigger_context"] = { + "type": "no_subtasks", + "description": "No subtasks available for execution", + "focus": "Need to create initial subtasks for the task" + } + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["init_error"]: + # Initialization error situation + context["trigger_context"] = { + "type": "init_error", + "description": "Error occurred during task initialization", + "focus": "Need to handle initialization error and start fresh" + } + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["supplement_completed"]: + # Supplement completed situation + context["trigger_context"] = { + "type": "supplement_completed", + "description": "Supplement collection completed, ready to replan", + "focus": "Use the collected supplement information to improve planning" + } + # Get supplement information + context["supplement_info"] = get_supplement_info(global_state) + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["supplement_error"]: + # Supplement error situation + context["trigger_context"] = { + "type": "supplement_error", + "description": "Error occurred during supplement collection", + "focus": "Handle supplement error and continue with available information" + } + + else: + # Default situation + context["trigger_context"] = { + "type": "general_replan", + "description": f"General replanning triggered by: {trigger_code}", + "focus": "Analyze the current situation and improve the plan" + } + + return context + + +def generate_planning_prompt(context: Dict[str, Any], + integrated_knowledge: str = "", trigger_code: str = "controller", + assumptions: List[str] = []) -> str: + """Generate planning prompt based on scenario, context and trigger_code""" + + # Determine scenario from context to ensure auto mode works + planning_scenario: str = context.get("planning_scenario", "initial_plan") + history_subtasks: str = context.get("history_subtasks", "") + pending_subtasks: str = context.get("pending_subtasks", "") + is_replan: bool = planning_scenario == "replan" + trigger_context = context.get("trigger_context", {}) + + # Generate trigger_code specific planning guidance + trigger_specific_guidance = generate_trigger_specific_guidance(trigger_code, trigger_context, context) + + # Scenario-specific planning task section + if is_replan: + planning_task = f""" +# Current Planning Task +You need to RE-PLAN the task based on prior attempts and failures. + +# CRITICAL: Original Task Objective Alignment +**ALWAYS keep the original task objective as your north star**: {context.get('task_objective', '')} +- Every subtask MUST directly contribute to achieving the original objective +- Do NOT deviate from the core purpose or add unrelated functionality +- Ensure the overall plan logically leads to completing the original task + +# Planning Focus (Re-plan) +- Analyze why previous attempts failed and identify bottlenecks +- Preserve valid progress; DO NOT duplicate completed subtasks +- Adjust ordering, refine steps, or replace failing subtasks +- Ensure dependencies remain valid and achievable +- **CRITICAL**: Verify each new subtask is necessary and sufficient for the original objective +- **ALLOWED**: Consider alternative approaches only when previous methods have failed +- **MANDATORY WHEN SWITCHING APPROACH (e.g., GUI → CLI/Technician)**: Preserve all key parameters from the preferred plan. Keep time offsets, durations/ranges, fps/frame rate, resolution/aspect ratio, sample rate, bitrate/quality, formats/containers, file names/paths, and input/output selection consistent unless there is a strong, stated reason to change. +- **PARAMETER MAPPING**: Explicitly enumerate how each original parameter maps to the new commands/flags. +- **MEDIA VIA ffmpeg (if applicable)**: Keep clip start and duration identical. Default to using the source video's native frame rate; if you change fps, state the reason, the target fps value, and ensure playback speed remains 1x (no unintended speedup/slowdown). + +{trigger_specific_guidance} +""" + decision = """ +# Planning Decision (Re-plan) +- Prioritize resolving blockers and mitigating risks found previously +- Introduce new/modified subtasks only where necessary for the ORIGINAL objective +- Keep completed subtasks out of the list; reference them only in dependencies +- **MANDATORY**: Before finalizing, review the entire plan to ensure it directly serves the original task objective +- **ALLOWED**: Use alternative approaches when previous methods have proven ineffective +- **FORBIDDEN**: Add verification/validation-only subtasks (Verify/Review/Confirm/Test/Check/QA). Evaluator performs quality checks and the system will re-plan on failures. +""" + else: + planning_task = f""" +# Current Planning Task +You need to perform INITIAL PLANNING to decompose the objective into executable subtasks. + +# Planning Focus (Initial) +- Cover the full path from start to completion +- Define clear, verifiable completion criteria for each subtask +- Keep reasonable granularity; avoid overly fine steps unless needed for reliability +- **CRITICAL**: Generate only ONE optimal execution path for each subtask +- **FORBIDDEN**: Do NOT create alternative approaches, backup plans, or fallback strategies +- **FORBIDDEN**: Do NOT create standalone verification/validation-only subtasks (e.g., "Verify", "Validation", "Review", "Confirm", "Test", "Check", "QA"). Quality checks are handled automatically by the Evaluator. + +{trigger_specific_guidance} +""" + decision = """ +# Planning Decision (Initial) +- Decompose the user objective into an ordered set of executable subtasks +- Make dependencies explicit and minimize unnecessary coupling +- Assign appropriate worker roles to each subtask +- **MANDATORY**: Ensure every subtask passes the rationality self-check +- **MANDATORY**: Verify the complete plan directly achieves the stated objective +- **FORBIDDEN**: Do NOT include alternative approaches or backup strategies +- **SINGLE PATH**: Focus on the most likely successful approach for each subtask +""" + + # Common guidance and output schema + common_guidance = f""" +# Task Information +Objective: {context.get('task_objective', '')} +Planning Scenario: {planning_scenario} +Trigger Code: {trigger_code} +Current Progress: {count_subtasks_from_info(context.get('history_subtasks', ''))} subtask completed, {count_subtasks_from_info(context.get('pending_subtasks', ''))} subtask pending +History Subtasks: {history_subtasks} +Pending Subtasks: {pending_subtasks} +Platform: {context.get('platform', '')} + +# Objective Alignment Information +**IMPORTANT**: The following information represents the user's original objective that has been refined and contextualized based on the current desktop screenshot. This is NOT the final plan - it's a reference to help you understand what the user actually wants to achieve in the current screen context. + +**What happened here:** +- The original user objective was analyzed and rewritten to be more specific and actionable +- The rewritten objective is grounded in what's currently visible on screen +- Assumptions and constraints were identified based on the visible UI elements +- Intent alignment was checked to ensure the rewritten objective preserves the user's original intent + +**Use this information to:** +- Understand the user's true goal in the current context +- Plan subtasks that directly serve the refined objective +- Consider the identified assumptions and constraints when planning +- Pay attention to any intent alignment warnings or gaps + +**Key Planning Guidelines:** +- **Screenshot-First Approach**: The refined objective is based on what's currently visible - plan to use existing on-screen elements when possible +- **Contextual Constraints**: Respect the identified screen constraints (e.g., available buttons, read-only states, visible data) +- **Assumption Awareness**: Build your plan considering the documented assumptions about the current state +- **Intent Verification**: If alignment score is low (<8), be extra careful to ensure your plan serves the user's original goal + +{format_assumptions_and_constraints(assumptions, context)} +""" + + # Replan-specific extra diagnostic information + replan_info = "" + if is_replan: + recent_ops = context.get("recent_subtasks_history", "") + replan_info = f""" +# Re-planning Information +Re-planning Attempts: {context.get('replan_attempts', 0)} +Failed Subtasks: {context.get('failed_subtasks', '')} +Failure Reasons: {context.get('failure_reasons', '')} + +# Recent Subtasks Operation History (latest 2) +{recent_ops if recent_ops else 'N/A'} + +# Re-plan Output Constraints +- Only include new subtasks in the JSON list +- Do not include already completed subtasks +- Keep or update dependencies to reference existing subtask IDs when applicable +- Before outputting, perform final alignment check with original objective +""" + + # Environment information + env_info = f""" +# Current Environment Information +Screenshot Available: {'Yes' if context.get('screenshot') else 'No'} + +# Complete State Information for Planning +**CRITICAL**: The following information provides complete context about the current environment state. Use this to understand what has been done, what resources exist, and what needs to be done next. + +## Quality Check Results +All quality check decisions and notes: +{json.dumps(context.get('gate_checks', []), indent=2, default=str) if context.get('gate_checks') else 'No quality checks performed yet'} + +## Generated Artifacts +All files, resources, and artifacts created: +{json.dumps(context.get('artifacts', []), indent=2, default=str) if context.get('artifacts') else 'No artifacts created yet'} + +## Supplement Information +Additional context and supplementary data: +{json.dumps(context.get('supplement', {}), indent=2, default=str) if context.get('supplement') else 'No supplement information available'} + +# Retrieved/Integrated Knowledge +You may refer to some retrieved knowledge if you think they are useful.{integrated_knowledge if integrated_knowledge else 'N/A'} + +# FINAL REMINDER +**Before submitting your plan, perform one final check:** +1. Does every subtask directly serve the original objective: "{context.get('task_objective', '')}"? +2. Is the sequence logical and efficient? +3. Are there any unnecessary or redundant steps? +4. Will completing all subtasks actually achieve the stated goal? +5. **CRITICAL ANALYST CHECK**: + - Is the first subtask assigned to Analyst? (FORBIDDEN - Analyst cannot be first) + - For any Analyst subtask, has Operator written required information to memory first? + - Can Analyst work with only memory data (no desktop access needed)? +6. **HUMAN WORKFLOW CHECK**: + - Would a normal person take these exact steps to achieve the objective? + - Are there any unnecessary intermediate steps that a human wouldn't naturally take? + - **SPECIFIC CHECK**: Did I add any layout changes, placeholder selections, or preparation steps that aren't explicitly required? + - **TEXT OPERATION CHECK**: For text-related tasks, am I adding the text directly or unnecessarily preparing the environment first? +7. **INTENT ALIGNMENT CHECK**: + - Review the Intent Alignment Check section above for any warnings about low alignment scores + - If alignment score is below 8, ensure the plan addresses the identified gaps + - Verify that the rewritten objective truly serves the user's original intent + - Consider requesting clarification if there are significant intent misalignments + +# Manager Completion Flag (MANDATORY) +At the very end of your output, add exactly one line: +MANAGER_COMPLETE: true +or +MANAGER_COMPLETE: false +- Use true if the currently planned subtasks, when executed, are sufficient to complete the overall objective without further planning. +- Use false if you expect further planning will be needed after more environment information is obtained. + +Please output the planning solution based on the above information: +""" + + planning_prompt = f""" +{planning_task} +{decision} +{common_guidance} +{replan_info} +{env_info} +""" + + return planning_prompt + + +def format_assumptions_and_constraints(assumptions: List[str] = [], context: Dict[str, Any] = {}) -> str: + """Format assumptions and constraints for inclusion in planning prompt""" + # Try to get assumptions and constraints from parameters first, then from context + # if assumptions is None: + # assumptions = context.get("objective_assumptions") + # if constraints_from_screen is None and context is not None: + # constraints_from_screen = context.get("objective_constraints") + + lines = [] + + # # Format assumptions + # if assumptions and isinstance(assumptions, list) and len(assumptions) > 0: + # lines.append("## Objective Assumptions") + # for i, assumption in enumerate(assumptions, 1): + # if isinstance(assumption, str) and assumption.strip(): + # lines.append(f"{i}. {assumption.strip()}") + # lines.append("") + + # Format intent alignment check if available + intent_alignment = context.get("objective_intent_alignment_check") + if intent_alignment and isinstance(intent_alignment, dict): + lines.append("## Intent Alignment Check") + alignment_score = intent_alignment.get("alignment_score", "N/A") + gap_analysis = intent_alignment.get("gap_analysis", "") + justification = intent_alignment.get("justification", "") + confidence_level = intent_alignment.get("confidence_level", "N/A") + + lines.append(f"**Alignment Score**: {alignment_score}/10") + lines.append(f"**Confidence Level**: {confidence_level}") + + if gap_analysis and gap_analysis.strip(): + lines.append(f"**Gap Analysis**: {gap_analysis.strip()}") + + if justification and justification.strip(): + lines.append(f"**Justification**: {justification.strip()}") + + # Add warning if alignment score is low + try: + score = int(alignment_score) if alignment_score.isdigit() else 0 + if score < 8: + lines.append("⚠️ **WARNING**: Low intent alignment score detected. Review the rewritten objective carefully.") + except (ValueError, AttributeError): + pass + + lines.append("") + + # If no assumptions or constraints, add a note + if not lines: + lines.append("No specific assumptions or screen constraints identified.") + lines.append("") + + return "\n".join(lines) + + +def generate_trigger_specific_guidance(trigger_code: str, trigger_context: Dict[str, Any], + context: Dict[str, Any]) -> str: + """Generate trigger_code specific planning guidance""" + + if trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["work_cannot_execute"]: + return """ +# Worker Cannot Execute - Specific Guidance +- The Worker reported that the current subtask cannot be executed +- Analyze the specific reason for failure and find alternative approaches +- Consider breaking down the subtask into smaller, more manageable steps +- Look for alternative methods or tools to achieve the same goal +- Ensure the new plan addresses the specific execution barriers identified +""" + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["quality_check_failed"]: + quality_info = context.get("quality_check_failure", {}) + return f""" +# Quality Check Failed - Specific Guidance +- The quality check failed for the current subtask +- Review the quality check notes: {quality_info.get('notes', 'No notes available')} +- Identify what specific quality criteria were not met +- Improve the approach to meet the quality standards +- Consider adding intermediate verification steps +- Ensure the new plan includes better quality control measures +""" + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["no_worker_decision"]: + return """ +# No Worker Decision - Specific Guidance +- Worker could not make a decision for the current subtask +- Provide clearer, more specific instructions +- Break down the subtask into smaller, more obvious steps +- Add more context or examples to guide the worker +- Consider using a different worker role that might be better suited +- Ensure the new plan has clear decision criteria and fallback options +""" + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["get_action_error"]: + return """ +# GET_ACTION Error - Specific Guidance +- Error occurred during GET_ACTION state processing +- Handle the error gracefully and provide alternative approaches +- Consider simplifying the action generation process +- Add error handling and recovery mechanisms +- Ensure the new plan is more robust and error-resistant +""" + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["quality_check_error"]: + return """ +# Quality Check Error - Specific Guidance +- Error occurred during quality check process +- Handle the quality check error and continue with alternative approaches +- Consider using simpler quality criteria +- Add fallback quality assessment methods +- Ensure the new plan includes error handling for quality checks +""" + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["final_check_failed"]: + final_info = context.get("final_check_failure", {}) + return f""" +# Final Check Failed - Specific Guidance +- Final quality check failed for the entire task +- Total gate checks: {final_info.get('total_gate_checks', 0)} +- Failed gate checks: {final_info.get('failed_gate_checks', 0)} +- Address the final quality issues and complete the task +- Review all completed subtasks for completeness +- Add missing steps or verification procedures +- Ensure the new plan addresses the root causes of final check failure +""" + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["rule_replan_long_execution"]: + exec_info = context.get("execution_time_info", {}) + return f""" +# Long Execution Replan - Specific Guidance +- Task has been executing for too long, need to replan +- Current step number: {exec_info.get('step_num', 0)} +- Current plan number: {exec_info.get('plan_num', 0)} +- Optimize the execution plan and reduce execution time +- Consider parallel execution where possible +- Simplify complex subtasks into more efficient steps +- Add timeouts and progress monitoring +- Ensure the new plan is more time-efficient +""" + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["no_subtasks"]: + return """ +# No Subtasks - Specific Guidance +- No subtasks available for execution +- Create initial subtasks for the task +- Break down the main objective into logical steps +- Ensure all necessary steps are covered +- Consider dependencies and execution order +- Assign appropriate worker roles to each subtask +""" + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["init_error"]: + return """ +# Init Error - Specific Guidance +- Error occurred during task initialization +- Handle initialization error and start fresh +- Simplify the initial setup process +- Add error recovery mechanisms +- Ensure the new plan has better initialization procedures +""" + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["supplement_completed"]: + supplement_info = context.get("supplement_info", {}) + return f""" +# Supplement Completed - Specific Guidance +- Supplement collection completed, ready to replan +- Supplement content length: {supplement_info.get('supplement_length', 0)} characters +- Use the collected supplement information to improve planning +- Incorporate the new information into the task plan +- Update subtasks based on the additional context +- Ensure the new plan leverages all available information +""" + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_REPLAN_CODES["supplement_error"]: + return """ +# Supplement Error - Specific Guidance +- Error occurred during supplement collection +- Handle supplement error and continue with available information +- Work with the information that is already available +- Consider alternative information sources +- Ensure the new plan can work with limited information +""" + + else: + return f""" +# General Replanning - Specific Guidance +- General replanning triggered by: {trigger_code} +- Analyze the current situation and improve the plan +- Consider all available context and information +- Address any identified issues or bottlenecks +- Ensure the new plan is more robust and effective +""" + + +def get_history_subtasks_info(global_state) -> str: + """Get information about completed subtasks""" + from .utils import get_history_subtasks_info as _get_history_subtasks_info + return _get_history_subtasks_info(global_state) + + +def get_pending_subtasks_info(global_state) -> str: + """Get information about pending subtasks""" + from .utils import get_pending_subtasks_info as _get_pending_subtasks_info + return _get_pending_subtasks_info(global_state) + +def get_recent_subtasks_operation_history(global_state: NewGlobalState, limit: int = 2) -> str: + """Get operation history of recently completed subtasks (up to limit), formatted as readable text. + Rules: + - Prioritize using Task.history_subtask_ids to get recent (non-READY) subtasks from back to front + - If insufficient quantity, supplement with all subtasks with non-READY status, sorted by updated_at in descending order (deduplicated) + - List commands for each subtask (in reverse chronological order), including type, status, message and execution summary + """ + try: + task = global_state.get_task() + all_subtasks = global_state.get_subtasks() + id_to_subtask = {s.subtask_id: s for s in all_subtasks} + + # Only select subtasks with non-READY status + def _is_non_ready(subtask): + try: + return getattr(subtask, 'status', '') != SubtaskStatus.READY.value + except Exception: + return True + + recent_ids = list(reversed(task.history_subtask_ids)) if getattr(task, 'history_subtask_ids', None) else [] + recent_subtasks = [] + for sid in recent_ids: + st = id_to_subtask.get(sid) + if st and _is_non_ready(st): + recent_subtasks.append(st) + if len(recent_subtasks) >= limit: + break + if len(recent_subtasks) < limit: + # Filter non-READY from all subtasks, supplement in descending order by updated_at + remaining = [s for s in all_subtasks if s.subtask_id not in {st.subtask_id for st in recent_subtasks} and _is_non_ready(s)] + remaining.sort(key=lambda x: getattr(x, 'updated_at', ''), reverse=True) + for s in remaining: + recent_subtasks.append(s) + if len(recent_subtasks) >= limit: + break + + lines = [] + if not recent_subtasks: + return "No historical operation records" + + for idx, subtask in enumerate(recent_subtasks, 1): + lines.append(f"=== Subtask {idx} ===") + lines.append(f"ID: {subtask.subtask_id}") + title = getattr(subtask, 'title', '') or '' + if title: + lines.append(f"Title: {title}") + # Command history + commands = list(global_state.get_commands_for_subtask(subtask.subtask_id)) + if not commands: + lines.append("No operation command records") + lines.append("") + continue + for i, cmd in enumerate(commands, 1): + action_type = "Unknown operation" + action_desc = "" + try: + if isinstance(cmd.action, dict): + if "type" in cmd.action: + action_type = cmd.action["type"] + if "message" in cmd.action: + action_desc = cmd.action["message"] + elif "element_description" in cmd.action: + action_desc = f"Operate element: {cmd.action['element_description']}" + elif "text" in cmd.action: + action_desc = f"Input text: {cmd.action['text']}" + elif "keys" in cmd.action: + action_desc = f"Keys: {cmd.action['keys']}" + except Exception: + pass + status = getattr(cmd, 'worker_decision', '') + message = getattr(cmd, 'message', '') or '' + exec_status = getattr(cmd, 'exec_status', '') + exec_message = getattr(cmd, 'exec_message', '') + + lines.append(f"{i}. [{action_type}] - Status: {status}") + if action_desc: + lines.append(f" Description: {action_desc}") + if message: + lines.append(f" Message: {message}") + if exec_status: + lines.append(f" Execution status: {exec_status}") + if exec_message: + lines.append(f" Execution message: {exec_message}") + lines.append("") + return "\n".join(lines) + except Exception: + return "" \ No newline at end of file diff --git a/mm_agents/maestro/maestro/manager/supplement.py b/mm_agents/maestro/maestro/manager/supplement.py new file mode 100644 index 0000000..71464e8 --- /dev/null +++ b/mm_agents/maestro/maestro/manager/supplement.py @@ -0,0 +1,438 @@ +""" +Supplement module for Manager +Handles supplement collection and processing logic based on trigger_code +""" + +import json +import logging +import time +from datetime import datetime +from typing import Dict, Any + +from ..enums import TRIGGER_CODE_BY_MODULE +from ..manager.utils import get_supplement_info +from ..new_global_state import NewGlobalState + +logger = logging.getLogger(__name__) + + +class SupplementHandler: + """Handles supplement collection and processing based on trigger_code""" + + def __init__(self, global_state: NewGlobalState, supplement_agent, search_engine, knowledge_base, enable_search): + self.global_state = global_state + self.supplement_agent = supplement_agent + self.search_engine = search_engine + self.knowledge_base = knowledge_base + self.supplement_attempts = 0 + self.enable_search = enable_search + def handle_supplement_scenario(self) -> Dict[str, Any]: + """Handle supplement collection scenario based on current trigger_code""" + try: + self.global_state.log_operation("manager", "supplement_start", { + "attempt": self.supplement_attempts, + "timestamp": time.time() + }) + + self.supplement_attempts += 1 + + # Get supplement context with trigger_code + context = self._get_supplement_context() + + # Get current trigger_code to determine supplement strategy + current_trigger_code = self._get_current_trigger_code() + + # Validate trigger_code + if not current_trigger_code or current_trigger_code.strip() == "": + logger.warning("No valid trigger_code found, using default supplement strategy") + current_trigger_code = "general_supplement" + + # Generate supplement prompt based on trigger_code + prompt = self._generate_supplement_prompt(context, current_trigger_code) + + # Execute supplement collection using LLM if available + if self.supplement_agent: + try: + supplement_result, total_tokens, cost_string = self.supplement_agent.execute_tool( + "supplement_role", + {"str_input": prompt}, + ) + + # Log supplement operation with LLM details + self.global_state.log_llm_operation( + "manager", + "supplement_collection", + { + "attempt": self.supplement_attempts, + "trigger_code": current_trigger_code, + "tokens": total_tokens, + "cost": cost_string + }, + str_input=prompt + ) + + # Parse and execute supplement plan + collected_data = self._execute_supplement_plan(supplement_result, current_trigger_code) + except Exception as e: + logger.error(f"Supplement agent execution failed: {e}") + # Fallback to fallback strategy if LLM fails + collected_data = self._fallback_supplement_strategy(context, current_trigger_code) + else: + # No supplement tool configured; use fallback strategy + logger.info("No supplement agent configured, using fallback strategy") + collected_data = self._fallback_supplement_strategy(context, current_trigger_code) + + # Update supplement content + self._update_supplement_content(collected_data, current_trigger_code) + + return { + "success": True, + "scenario": "supplement", + "subtasks": [], + "supplement": collected_data, + "reason": f"Successfully collected supplement data for trigger_code: {current_trigger_code}", + "created_at": datetime.now().isoformat() + } + + except Exception as e: + logger.error(f"Supplement collection failed: {e}") + return { + "success": False, + "scenario": "supplement", + "subtasks": [], + "supplement": "", + "reason": f"Supplement collection failed: {str(e)}", + "created_at": datetime.now().isoformat() + } + + def _get_current_trigger_code(self) -> str: + """Get current trigger_code""" + controller_state = self.global_state.get_controller_state() + trigger_code = controller_state.get("trigger_code", "") + + # Validate trigger_code + if not trigger_code or trigger_code.strip() == "": + logger.warning("No trigger_code found in controller state") + return "" + + # Check if it's a valid supplement trigger code + valid_supplement_codes = [ + TRIGGER_CODE_BY_MODULE.MANAGER_SUPPLEMENT_CODES["worker_supplement"], + TRIGGER_CODE_BY_MODULE.MANAGER_SUPPLEMENT_CODES["quality_check_supplement"] + ] + + if trigger_code not in valid_supplement_codes: + logger.warning(f"Trigger code '{trigger_code}' is not a recognized supplement code") + # Still return it for fallback handling + + return trigger_code + + def _get_supplement_context(self) -> Dict[str, Any]: + """Get context information for supplement collection""" + task = self.global_state.get_task() + subtasks = self.global_state.get_subtasks() + supplement = self.global_state.get_supplement() + + # Get current subtask that needs supplement + current_subtask = None + supplement_reason = "" + if task.current_subtask_id: + current_subtask = self.global_state.get_subtask(task.current_subtask_id) + # Get the reason for supplement collection from the current command's message field + if current_subtask: + current_command = self.global_state.get_current_command_for_subtask(current_subtask.subtask_id) + if current_command: + # Check if the command has a message field that explains why supplement is needed + if hasattr(current_command, 'message') and current_command.message: + supplement_reason = current_command.message + # Fallback to action type if message is not available + elif hasattr(current_command, 'action') and current_command.action: + action_type = current_command.action.get('type', '') + if action_type == 'Supplement': + supplement_reason = current_command.action.get('message', 'Worker requested supplement') + else: + supplement_reason = "" + + return { + "task_objective": task.objective or "", + "current_subtask": current_subtask, + "all_subtasks": subtasks, + "existing_supplement": supplement, + "supplement_attempts": self.supplement_attempts, + "supplement_reason": supplement_reason + } + + def _generate_supplement_prompt(self, context: Dict[str, Any], trigger_code: str) -> str: + """Generate supplement collection prompt based on trigger_code""" + + # Get trigger_code specific context + trigger_context = self._get_trigger_code_specific_context(trigger_code, context) + + # Generate trigger_code specific supplement guidance + trigger_specific_guidance = self._generate_trigger_specific_supplement_guidance(trigger_code, trigger_context, context) + + system_info = f""" +# System Architecture +You are the Manager (task planner) in the GUI-Agent system. The system includes: +- Controller: Central scheduling and process control +- Manager: Task planning and resource allocation (your role) +- Worker: Execute specific operations (Operator/Analyst/Technician) +- Evaluator: Quality inspection +- Hardware: Low-level execution + +# Current Supplement Task +During execution, necessary information was found to be missing. You need to collect supplementary materials based on the specific trigger_code: {trigger_code} + +# Collection Tools +- Web Search: Search for latest information from the internet (primary tool) +- RAG Retrieval: Retrieve relevant documents from knowledge base (if needed) + +# Collection Strategy +1. Clearly identify the type and importance of required information based on trigger_code +2. Choose appropriate search queries for web search +3. Verify and organize collected information +4. Update supplement.md file + +{trigger_specific_guidance} + +# Output Format +You must output the following JSON format: +{{ + "needed_info": "Detailed description of required information based on trigger_code", + "search_queries": ["search query1", "search query2", "search query3"], + "priority_keywords": ["keyword1", "keyword2"], + "expected_outcome": "What we expect to achieve with this supplement" +}} +""" + + supplement_prompt = f""" +{system_info} + +# Missing Information Situation +Task Objective: {context.get('task_objective', '')} +Current Subtask: {context.get('current_subtask', {})} +Existing Supplement: {context.get('existing_supplement', '')} +Supplement Attempts: {context.get('supplement_attempts', 0)} +Trigger Code: {trigger_code} +Supplement Reason: {context.get('supplement_reason', '')} + +Please output the supplementary material collection solution based on the trigger_code and execute it: +""" + + return supplement_prompt + + def _get_trigger_code_specific_context(self, trigger_code: str, context: Dict[str, Any]) -> Dict[str, Any]: + """Get trigger_code specific context information""" + trigger_context = {} + + if trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_SUPPLEMENT_CODES["worker_supplement"]: + # Worker needs supplementary information situation + trigger_context = { + "type": "worker_supplement", + "description": "Worker reported that supplementary information is needed to proceed", + "focus": "Need to understand what specific information the worker needs and collect it" + } + # Get current subtask information + if context.get("current_subtask"): + trigger_context["current_subtask"] = context["current_subtask"] + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_SUPPLEMENT_CODES["quality_check_supplement"]: + # Quality check needs supplementary information situation + trigger_context = { + "type": "quality_check_supplement", + "description": "Quality check requires supplementary information to proceed", + "focus": "Need to collect information that will help pass the quality check" + } + # Get quality check failure information + trigger_context["quality_check_context"] = self._get_quality_check_context() #type: ignore + + else: + # Default situation + trigger_context = { + "type": "general_supplement", + "description": f"General supplement collection triggered by: {trigger_code}", + "focus": "Analyze the current situation and collect relevant supplementary information" + } + + return trigger_context + + def _get_quality_check_context(self) -> Dict[str, Any]: + """Get quality check context for supplement""" + try: + # Get latest quality check information + task = self.global_state.get_task() + if task.current_subtask_id: + latest_gate = self.global_state.get_latest_gate_check_for_subtask(task.current_subtask_id) + if latest_gate: + return { + "gate_decision": latest_gate.decision, + "gate_notes": latest_gate.notes, + "gate_trigger": latest_gate.trigger + } + except Exception as e: + logger.warning(f"Failed to get quality check context: {e}") + + return {} + + def _generate_trigger_specific_supplement_guidance(self, trigger_code: str, trigger_context: Dict[str, Any], + context: Dict[str, Any]) -> str: + """Generate trigger_code specific supplement guidance""" + + if trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_SUPPLEMENT_CODES["worker_supplement"]: + current_subtask = context.get("current_subtask", {}) + return f""" +# Worker Supplement - Specific Guidance +- The Worker reported that supplementary information is needed to proceed +- Current subtask: {current_subtask.get('title', 'Unknown')} +- Focus on collecting information that will help the worker complete this specific subtask +- Consider what technical details, examples, or context the worker might need +- Prioritize practical, actionable information over general knowledge +- Use web search to find current, up-to-date information relevant to the subtask +""" + + elif trigger_code == TRIGGER_CODE_BY_MODULE.MANAGER_SUPPLEMENT_CODES["quality_check_supplement"]: + quality_context = trigger_context.get("quality_check_context", {}) + return f""" +# Quality Check Supplement - Specific Guidance +- Quality check requires supplementary information to proceed +- Gate decision: {quality_context.get('gate_decision', 'Unknown')} +- Gate notes: {quality_context.get('gate_notes', 'No notes available')} +- Focus on collecting information that addresses the specific quality criteria that failed +- Look for examples, standards, or best practices that can help meet quality requirements +- Use web search to find authoritative sources and current standards +- Consider what additional context would help the evaluator make a positive decision +""" + + else: + return f""" +# General Supplement - Specific Guidance +- General supplement collection triggered by: {trigger_code} +- Analyze the current situation and identify what information is missing +- Focus on practical, relevant information that will help move the task forward +- Use web search to find current, authoritative information +- Consider what context or examples would be most helpful +""" + + def _execute_supplement_plan(self, supplement_result: str, trigger_code: str) -> str: + """Execute supplement collection plan based on LLM output""" + try: + # Parse LLM output + plan_data = json.loads(supplement_result) + + # Extract search queries + search_queries = plan_data.get("search_queries", []) + needed_info = plan_data.get("needed_info", "") + expected_outcome = plan_data.get("expected_outcome", "") + + # Validate search queries + if not search_queries or not isinstance(search_queries, list): + logger.warning("Invalid search queries from LLM, using fallback") + return self._fallback_supplement_strategy({}, trigger_code) + + # Execute web search for each query + collected_data = [] + + if search_queries and self.search_engine: + for query in search_queries: + try: + logger.info(f"Executing web search for query: {query}") + search_result, _, _ = self.search_engine.execute_tool("websearch", {"query": query}) + if search_result: + collected_data.append(f"Web Search Result for '{query}':\n{search_result}") + else: + collected_data.append(f"Web Search returned no results for '{query}'") + except Exception as e: + logger.warning(f"Web search failed for query '{query}': {e}") + collected_data.append(f"Web Search Failed for '{query}': {str(e)}") + else: + if not search_queries: + collected_data.append("No search queries provided by LLM") + if not self.search_engine: + collected_data.append("Search engine not available") + + # Combine collected data with context + combined_data = f""" +# Supplement Collection Results +Trigger Code: {trigger_code} +Needed Information: {needed_info} +Expected Outcome: {expected_outcome} + +## Collected Data: +{chr(10).join(collected_data) if collected_data else "No data collected"} + +## Summary: +This supplement was collected to address the specific needs identified by trigger_code: {trigger_code} +""" + + return combined_data + + except json.JSONDecodeError as e: + logger.warning(f"Failed to parse supplement result: {e}") + logger.warning(f"Raw supplement result: {supplement_result[:200]}...") # Log first 200 chars + return self._fallback_supplement_strategy({}, trigger_code) + except Exception as e: + logger.error(f"Unexpected error in supplement plan execution: {e}") + return self._fallback_supplement_strategy({}, trigger_code) + + def _fallback_supplement_strategy(self, context: Dict[str, Any], trigger_code: str) -> str: + """Fallback supplement strategy when LLM is unavailable""" + objective = context.get("task_objective", "").strip() + + # Fix: Ensure search_queries is a list, not a string + if objective: + search_queries = [objective] # Convert string to list + else: + search_queries = ["supplement information", "task details"] # Default queries + + # Execute fallback web search + collected_data = [] + if self.search_engine: + for query in search_queries: + try: + search_result, _, _ = self.search_engine.execute_tool("websearch", {"query": query}) + if search_result: + collected_data.append(f"Fallback Web Search Result for '{query}':\n{search_result}") + except Exception as e: + logger.warning(f"Fallback web search failed for query '{query}': {e}") + else: + collected_data.append("Search engine not available for fallback strategy") + + combined_data = f""" +# Fallback Supplement Collection +Trigger Code: {trigger_code} +Strategy: Automatic fallback due to LLM unavailability + +## Search Queries Used: +{chr(10).join(f"- {q}" for q in search_queries)} + +## Collected Data: +{chr(10).join(collected_data) if collected_data else "No data collected in fallback mode"} +""" + + return combined_data + + def _update_supplement_content(self, collected_data: str, trigger_code: str): + """Update supplement content in global state""" + current_supplement = self.global_state.get_supplement() + + # Add new supplement entry + entry_id = f"supplement-{int(time.time() * 1000)}" + timestamp = datetime.now().isoformat() + + new_entry = f""" +## Supplement Entry - {entry_id} +- **Created**: {timestamp} +- **Type**: Collected Information +- **Trigger Code**: {trigger_code} +- **Content**: {collected_data} +- **Status**: Collected + +--- +""" + + updated_content = current_supplement + new_entry + self.global_state.set_supplement(updated_content) + + # Also add to events for tracking + self.global_state.add_event("manager", "supplement_added", + f"Added supplement artifact {entry_id} for trigger_code {trigger_code}") diff --git a/mm_agents/maestro/maestro/manager/utils.py b/mm_agents/maestro/maestro/manager/utils.py new file mode 100644 index 0000000..4fa6fdb --- /dev/null +++ b/mm_agents/maestro/maestro/manager/utils.py @@ -0,0 +1,358 @@ +""" +Utility functions for Manager module +Contains helper functions for subtask management, DAG operations, and context building +""" + +import json +import logging +from datetime import datetime +from typing import Dict, List, Optional, Any +from collections import defaultdict + +from ...utils.common_utils import Node, Dag, parse_dag +from ...utils.id_utils import generate_uuid +from ..enums import SubtaskStatus, GateDecision + +logger = logging.getLogger(__name__) + + +def enhance_subtasks(subtasks: List[Node], task_id: str) -> List[Dict]: + """Enhance subtasks with additional metadata. + Accepts a list of Node where: + - name -> title + - info -> description + - assignee_role -> assignee_role + """ + enhanced_subtasks = [] + + for i, node in enumerate(subtasks): + node_title = getattr(node, "name", None) or f"Subtask {i+1}" + node_description = getattr(node, "info", "") or "" + node_role = getattr(node, "assignee_role", None) or "operator" + + # Validate assignee role + if node_role not in ["operator", "analyst", "technician"]: + node_role = "operator" + + enhanced_subtask = { + "subtask_id": f"subtask-{generate_uuid()[:4]}-{i+1}", + "task_id": task_id, + "title": node_title, + "description": node_description, + "assignee_role": node_role, + "status": SubtaskStatus.READY.value, + "attempt_no": 1, + "reasons_history": [], + "command_trace_ids": [], + "gate_check_ids": [], + "last_reason_text": "", + "last_gate_decision": "", + "created_at": datetime.now().isoformat(), + "updated_at": datetime.now().isoformat(), + } + + enhanced_subtasks.append(enhanced_subtask) + + return enhanced_subtasks + + +def generate_dag(dag_translator_agent, global_state, instruction: str, plan: str) -> tuple[Dict, Dag]: + """Generate a DAG from instruction and plan using dag_translator, with retries and fallback.""" + max_retries = 3 + retry = 0 + dag_obj: Optional[Dag] = None + dag_raw = "" + total_tokens = 0 + cost_string = "" + dag_input = f"Instruction: {instruction}\nPlan: {plan}" + + while retry < max_retries and dag_obj is None: + dag_raw, total_tokens, cost_string = dag_translator_agent.execute_tool( + "dag_translator", {"str_input": dag_input} + ) + global_state.log_llm_operation( + "manager", "generated_dag", { + "dag_raw": dag_raw, + "tokens": total_tokens, + "cost": cost_string, + "retry_count": retry + }, + str_input=dag_input + ) + dag_obj = parse_dag(dag_raw) + retry += 1 if dag_obj is None else 0 + + # global_state.log_llm_operation( + # "manager", "generated_dag", { + # "dag_obj": str(dag_obj), + # "tokens": total_tokens, + # "cost": cost_string, + # "retry_count": retry-1 + # }, + # str_input=dag_input + # ) + + if dag_obj is None: + # Fallback to simple DAG + default_node = Node(name="Execute Task", info=f"Execute instruction: {instruction}", assignee_role="operator") + dag_obj = Dag(nodes=[default_node], edges=[]) + global_state.add_event("manager", "default_dag_created", "fallback simple DAG used") + + return {"dag": dag_raw}, dag_obj + + +def topological_sort(dag: Dag) -> List[Node]: + """Topological sort of the DAG using DFS; returns node list on error.""" + if not getattr(dag, 'nodes', None): + return [] + if len(dag.nodes) == 1: + return dag.nodes + + def dfs(node_name, visited, temp_visited, stack): + if node_name in temp_visited: + raise ValueError(f"Cycle detected in DAG involving node: {node_name}") + if visited.get(node_name, False): + return + temp_visited.add(node_name) + visited[node_name] = True + for neighbor in adj_list.get(node_name, []): + if not visited.get(neighbor, False): + dfs(neighbor, visited, temp_visited, stack) + temp_visited.remove(node_name) + stack.append(node_name) + + try: + # Build adjacency list + adj_list = defaultdict(list) + for edge in dag.edges: + # Handle edges that contain complete Node objects + if isinstance(edge, (list, tuple)) and len(edge) >= 2: + source = edge[0] + target = edge[1] + + # If edge contains Node objects, get their names + if hasattr(source, 'name') and hasattr(target, 'name'): + source_name = source.name + target_name = target.name + else: + logger.warning(f"Skipping invalid edge: {edge}") + continue + + adj_list[source_name].append(target_name) + else: + logger.warning(f"Skipping malformed edge: {edge}") + + # Build in-degree table + in_degree = defaultdict(int) + for node in dag.nodes: + in_degree[node.name] = 0 + + for neighbors in adj_list.values(): + for neighbor in neighbors: + in_degree[neighbor] += 1 + + # Use Kahn's algorithm for topological sorting (more reliable) + queue = [node.name for node in dag.nodes if in_degree[node.name] == 0] + sorted_names = [] + + while queue: + current = queue.pop(0) + sorted_names.append(current) + + for neighbor in adj_list.get(current, []): + in_degree[neighbor] -= 1 + if in_degree[neighbor] == 0: + queue.append(neighbor) + + # Check if all nodes are sorted + if len(sorted_names) != len(dag.nodes): + logger.warning(f"Topological sort incomplete: {len(sorted_names)}/{len(dag.nodes)} nodes sorted") + # Add unsorted nodes + for node in dag.nodes: + if node.name not in sorted_names: + sorted_names.append(node.name) + + # Build sorted node list + sorted_nodes = [] + for name in sorted_names: + matching = [n for n in dag.nodes if n.name == name] + if matching: + sorted_nodes.append(matching[0]) + + logger.info(f"Topological sort completed: {[n.name for n in sorted_nodes]}") + return sorted_nodes + + except Exception as e: + logger.error(f"Topological sort failed: {e}, returning original node order") + return dag.nodes + + +def get_failed_subtasks_info(global_state) -> str: + """Get information about failed subtasks""" + failed_subtasks = [] + all_subtasks = global_state.get_subtasks() + + for subtask in all_subtasks: + if subtask.status == SubtaskStatus.REJECTED.value: + failed_subtasks.append({ + "id": subtask.subtask_id, + "title": subtask.title, + "description": subtask.description, + "assignee_role": subtask.assignee_role, + "reason": subtask.last_reason_text or "Unknown reason", + }) + + if not failed_subtasks: + return "No failed subtasks" + + return json.dumps(failed_subtasks, indent=2) + + +def get_failure_reasons(global_state) -> str: + """Get failure reasons from subtask history""" + failure_reasons = [] + all_subtasks = global_state.get_subtasks() + + for subtask in all_subtasks: + if subtask.status == SubtaskStatus.REJECTED.value: + reasons = subtask.reasons_history or [] + if reasons: + failure_reasons.extend([r.get("text", "") for r in reasons]) + + return "; ".join(failure_reasons) if failure_reasons else "No specific failure reasons" + + +def get_history_subtasks_info(global_state) -> str: + """Get information about completed subtasks""" + history_subtasks = [] + task = global_state.get_task() + all_subtasks = global_state.get_subtasks() + + if task.history_subtask_ids: + for subtask_id in task.history_subtask_ids: + subtask = next((s for s in all_subtasks if s.subtask_id == subtask_id), None) + if subtask: + history_subtasks.append({ + "id": subtask.subtask_id, + "title": subtask.title, + "description": subtask.description, + "assignee_role": subtask.assignee_role, + "status": subtask.status, + "completion_reason": subtask.last_reason_text or "Completed successfully", + "last_gate_decision": subtask.last_gate_decision, + }) + + if not history_subtasks: + return "No completed subtasks" + + return json.dumps(history_subtasks, indent=2) + + +def get_pending_subtasks_info(global_state) -> str: + """Get information about pending subtasks""" + pending_subtasks = [] + task = global_state.get_task() + all_subtasks = global_state.get_subtasks() + + if task.pending_subtask_ids: + for subtask_id in task.pending_subtask_ids: + subtask = next((s for s in all_subtasks if s.subtask_id == subtask_id), None) + if subtask: + pending_subtasks.append({ + "id": subtask.subtask_id, + "title": subtask.title, + "description": subtask.description, + "assignee_role": subtask.assignee_role, + "status": subtask.status, + "attempt_no": subtask.attempt_no, + }) + + if not pending_subtasks: + return "No pending subtasks" + + return json.dumps(pending_subtasks, indent=2) + + +def count_subtasks_from_info(subtasks_info: str) -> int: + """Count subtasks from the JSON string info returned by _get_*_subtasks_info methods""" + if not subtasks_info or subtasks_info in ["No completed subtasks", "No pending subtasks"]: + return 0 + try: + subtasks_list = json.loads(subtasks_info) + return len(subtasks_list) if isinstance(subtasks_list, list) else 0 + except (json.JSONDecodeError, TypeError): + return 0 + + +def get_current_failed_subtask(global_state) -> Optional[Dict[str, Any]]: + """Get information about currently failed subtask""" + task = global_state.get_task() + if task.current_subtask_id: + subtask = global_state.get_subtask(task.current_subtask_id) + if subtask and subtask.status == SubtaskStatus.REJECTED.value: + return { + "subtask_id": subtask.subtask_id, + "title": subtask.title, + "description": subtask.description, + "assignee_role": subtask.assignee_role, + "last_reason_text": subtask.last_reason_text, + "reasons_history": subtask.reasons_history + } + return None + + +def get_quality_check_failure_info(global_state) -> Dict[str, Any]: + """Get detailed information about quality check failure""" + task = global_state.get_task() + if task.current_subtask_id: + # Get latest quality check record + latest_gate = global_state.get_latest_gate_check_for_subtask(task.current_subtask_id) + if latest_gate: + return { + "gate_check_id": latest_gate.gate_check_id, + "decision": latest_gate.decision, + "notes": latest_gate.notes, + "trigger": latest_gate.trigger, + "created_at": latest_gate.created_at + } + return {"error": "No quality check information available"} + + +def get_final_check_failure_info(global_state) -> Dict[str, Any]: + """Get information about final quality check failure""" + # Get all quality check records + gate_checks = global_state.get_gate_checks() + if gate_checks: + latest_gate = max(gate_checks, key=lambda x: x.created_at) + return { + "latest_gate_check": { + "gate_check_id": latest_gate.gate_check_id, + "decision": latest_gate.decision, + "notes": latest_gate.notes, + "trigger": latest_gate.trigger, + "created_at": latest_gate.created_at + }, + "total_gate_checks": len(gate_checks), + "failed_gate_checks": len([gc for gc in gate_checks if gc.decision == GateDecision.GATE_FAIL.value]) + } + return {"error": "No final check information available"} + + +def get_execution_time_info(global_state) -> Dict[str, Any]: + """Get execution time information""" + task = global_state.get_task() + return { + "step_num": task.step_num, + "plan_num": task.plan_num, + "task_created_at": task.created_at, + "current_time": datetime.now().isoformat() + } + + +def get_supplement_info(global_state) -> Dict[str, Any]: + """Get supplementary information""" + supplement_content = global_state.get_supplement() + return { + "supplement_content": supplement_content, + "supplement_length": len(supplement_content) if supplement_content else 0 + } diff --git a/mm_agents/maestro/maestro/new_executor.py b/mm_agents/maestro/maestro/new_executor.py new file mode 100644 index 0000000..baf80db --- /dev/null +++ b/mm_agents/maestro/maestro/new_executor.py @@ -0,0 +1,520 @@ +""" +New Executor Implementation +Executor module specifically responsible for retrieving and executing actions from global state +""" + +import time +import logging +import re +from typing import Dict, Any, Optional, List, Tuple +from .data_models import CommandData +from .hardware_interface import HardwareInterface +from .new_global_state import NewGlobalState +from .enums import ExecStatus, SubtaskStatus +from desktop_env.desktop_env import DesktopEnv +from PIL import Image +from .Action import Screenshot +from .utils.screenShot import scale_screenshot_dimensions + +# Setup logging +logger = logging.getLogger(__name__) + + +class NewExecutor: + """Action Executor - Responsible for retrieving and executing actions""" + + def __init__(self, global_state: NewGlobalState, hardware_interface: HardwareInterface, env_controller: Optional[DesktopEnv] = None): + """ + Initialize executor + + Args: + global_state: Global state manager + hardware_interface: Hardware interface + env_controller: Environment controller for executing code scripts + """ + self.global_state = global_state + self.hwi = hardware_interface + self.env_controller = env_controller.controller if env_controller is not None else None + self.execution_timeout = 30 # Single execution timeout (seconds) + + logger.info("NewExecutor initialized") + + # ========== Pure Execution (No global_state Updates) ========== + def _run_code_blocks(self, code_blocks: List[Tuple[str, str]]) -> Tuple[bool, str, Optional[str]]: + """Execute code blocks only, return (success, combined_output, last_status). No global_state updates.""" + if not self.env_controller: + return False, "No environment controller available for code execution", None + results = [] + last_status: Optional[str] = None + for lang, code in code_blocks: + try: + if lang in ["bash", "shell", "sh"]: + output_dict = self.env_controller.run_bash_script(code, timeout=600) + status = (output_dict or {}).get("status") + last_status = status + if status == "success": + results.append(f"[BASH] Success: {(output_dict or {}).get('output', '')}") + else: + out = (output_dict or {}).get('output', '') + err = (output_dict or {}).get('error', '') + msg = out if out else err + results.append(f"[BASH] Error: {msg}") + elif lang in ["python", "py"]: + output_dict = self.env_controller.run_python_script(code) + status = (output_dict or {}).get("status") + last_status = status + if status == "error": + out = (output_dict or {}).get('output', '') + err = (output_dict or {}).get('error', '') + msg = out if out else err + results.append(f"[PYTHON] Error: {msg}") + else: + results.append(f"[PYTHON] Success: {(output_dict or {}).get('message', '')}") + else: + results.append(f"[{lang.upper()}] Unsupported language") + except Exception as e: + last_status = "error" + results.append(f"[{lang.upper()}] Execution error: {str(e)}") + success = last_status == "success" + return success, "\n".join(results), last_status + + # ========== Execute Hardware Actions ========== + def _run_hardware_action(self, action: Dict[str, Any]) -> Tuple[bool, Optional[str], Optional[Image.Image]]: + """Execute actions through hardware interaction only, return (success, error_message, screenshot). No global_state updates.""" + try: + self.hwi.dispatchDict(action) + time.sleep(3) + screenshot: Image.Image = self.hwi.dispatch(Screenshot()) # type: ignore + return True, None, screenshot + except Exception as e: + return False, str(e), None + + # ========== Execute Command ========== + def execute_command(self, command: CommandData) -> Dict[str, Any]: + """ + Execute the passed command (execution only, no global_state modifications): + - Choose execution path based on subtask's assignee_role + - operator -> hardware execution only + - technician -> code execution only + - analyst -> no artifact storage, return intent information only + - No artifact writing, no exec_status updates, no screenshot updates, no step increment + """ + try: + subtask_id: Optional[str] = getattr(command, "subtask_id", None) + if not subtask_id: + return self._create_execution_result(False, "command.subtask_id is empty") + subtask = self.global_state.get_subtask(subtask_id) + if not subtask: + return self._create_execution_result(False, "Subtask not found") + + # Minimal Stale handling: execute candidate_action if provided + command_action = command.action + if isinstance(command_action, dict) and str(command_action.get("type", "")).strip().lower() == "stale": + cand = command_action.get("candidate_action") + if isinstance(cand, dict) and cand: + command_action = cand + + assignee_role = getattr(subtask, "assignee_role", "operator") + start_ts = time.time() + + if assignee_role == "operator": + ok, err, _shot = self._run_hardware_action(command_action) + return self._create_execution_result( + success=ok, + error_message=err, + execution_time=time.time() - start_ts, + action=command_action, + ) + + if assignee_role == "technician": + # Code blocks or extract code blocks from string + if isinstance(command.action, list) and command.action: + code_blocks: List[Tuple[str, str]] = [] + for item in command.action: + if isinstance(item, list) and len(item) == 2: + lang, code = item + if isinstance(lang, str) and isinstance(code, str): + code_blocks.append((lang, code)) + if not code_blocks: + return self._create_execution_result(False, "Invalid code blocks format in command.action") + success, combined_output, _ = self._run_code_blocks(code_blocks) + return self._create_execution_result( + success=success, + execution_time=time.time() - start_ts, + action={"type": "code_blocks_execution", "result": combined_output} + ) + elif isinstance(command.action, str): + code_blocks = self._extract_code_blocks(command.action) + if not code_blocks: + return self._create_execution_result(False, "No code blocks found in string action") + success, combined_output, _ = self._run_code_blocks(code_blocks) + return self._create_execution_result( + success=success, + execution_time=time.time() - start_ts, + action={"type": "code_blocks_execution", "result": combined_output} + ) + else: + return self._create_execution_result(False, "Action is not in expected format for technician role") + + if assignee_role == "analyst": + # No artifact writing, return intent information only + return self._create_execution_result( + success=True, + execution_time=time.time() - start_ts, + action={"type": "analysis_intent", "payload": command.action} + ) + + # Fallback: unknown roles execute as hardware + ok, err, _shot = self._run_hardware_action(command.action) + return self._create_execution_result( + success=ok, + error_message=err, + execution_time=time.time() - start_ts, + action=command.action, + ) + except Exception as e: + return self._create_execution_result(False, f"execute_command failed: {e}") + + # ========== Execute Current Action ========== + def execute_current_action(self) -> Dict[str, Any]: + """ + Execute the action of specified subtask (using the "current latest command" of that subtask). + Retained for compatibility with old usage; use execute_command for executing specific commands. + """ + try: + subtask_id = self.global_state.get_task().current_subtask_id + logger.info(f"Starting action execution for subtask: {subtask_id}") + + # Get related command (current latest) + if not subtask_id: + error_msg = f"No subtask_id found" + logger.warning(error_msg) + return self._create_execution_result(False, error_msg) + command = self.global_state.get_current_command_for_subtask(subtask_id) + if not command: + error_msg = f"No command found for subtask {subtask_id}" + logger.warning(error_msg) + return self._create_execution_result(False, error_msg) + command_id = command.command_id + # Check if there's an action to execute + if not command.action: + error_msg = f"No action defined in command for subtask {subtask_id}" + logger.warning(error_msg) + return self._create_execution_result(False, error_msg) + + # Minimal Stale handling: execute candidate_action if provided + command_action = command.action + if isinstance(command_action, dict) and str(command_action.get("type", "")).strip().lower() == "stale": + cand = command_action.get("candidate_action") + if isinstance(cand, dict) and cand: + command_action = cand + + # Get current subtask's assignee_role + subtask = self.global_state.get_subtask(subtask_id) + if not subtask: + return self._create_execution_result(False, "Subtask not found") + + assignee_role = getattr(subtask, "assignee_role", "operator") + + # Choose different execution methods based on assignee_role + if assignee_role == "operator": + return self._execute_action(subtask_id, command_action, command_id) + + elif assignee_role == "technician": + if isinstance(command_action, list) and command_action: + code_blocks = [] + for item in command_action: + if isinstance(item, list) and len(item) == 2: + lang, code = item + if isinstance(lang, str) and isinstance(code, str): + code_blocks.append((lang, code)) + if code_blocks: + return self._execute_code_blocks(code_blocks, subtask_id, command_id) + else: + return self._create_execution_result(False, "Invalid code blocks format in action") + elif isinstance(command_action, str): + code_blocks = self._extract_code_blocks(command_action) + if code_blocks: + return self._execute_code_blocks(code_blocks, subtask_id) + else: + return self._create_execution_result(False, "No code blocks found in string action") + else: + return self._create_execution_result(False, "Action is not in expected format for technician role") + + elif assignee_role == "analyst": + try: + if isinstance(command_action, dict) and "analysis" in command_action: + analysis_result = command_action.get("analysis", "") + recommendations = command_action.get("recommendations", []) + artifact_data = { + "subtask_id": subtask_id, + "type": "analysis_result", + "analysis": analysis_result, + "recommendations": recommendations, + "timestamp": time.time(), + "source": "analyst_memorize_analysis" + } + self.global_state.add_artifact("analysis_result", artifact_data) + self.global_state.update_command_exec_status( + command_id, # type: ignore + ExecStatus.EXECUTED, + exec_message='', + ) + return self._create_execution_result(True, action={"type": "analysis_artifact_stored", "artifact": artifact_data}) + else: + artifact_data = { + "subtask_id": subtask_id, + "action": command_action, + "timestamp": time.time(), + "type": "action_artifact" + } + self.global_state.update_command_exec_status( + command_id, # type: ignore + ExecStatus.EXECUTED, + exec_message='', + ) + self.global_state.add_artifact("action_artifact", artifact_data) + return self._create_execution_result(True, action={"type": "artifact_stored", "artifact": artifact_data}) + except Exception as e: + error_msg = f"Failed to store artifact: {str(e)}" + logger.error(error_msg) + return self._create_execution_result(False, error_msg) + + else: + logger.warning(f"Unknown assignee_role '{assignee_role}', falling back to hardware execution") + return self._execute_action(subtask_id, command_action, command_id) + + except Exception as e: + error_msg = f"Exception in execute_current_action: {str(e)}" + logger.error(error_msg) + return self._create_execution_result(False, error_msg) + + + # ========== Execute Code Blocks ========== + def _execute_code_blocks(self, code_blocks: List[Tuple[str, str]], subtask_id: str, command_id: Optional[str] = None) -> Dict[str, Any]: + """ + Execute list of code blocks + + Args: + code_blocks: List of code blocks, each element is a tuple of (language, code) + subtask_id: Subtask ID, used to update command's post_screenshot_id + command_id: If provided, precisely update the execution status of that command; otherwise fallback to current latest command + """ + if not self.env_controller: + error_msg = "No environment controller available for code execution" + logger.warning(error_msg) + return self._create_execution_result(False, error_msg) + + execution_start = time.time() + + try: + # Pure execution + success, combined_output, last_status = self._run_code_blocks(code_blocks) + + execution_time = time.time() - execution_start + + # Record execution results (status updates) + self.global_state.add_event("executor", "code_blocks_execution_completed", + f"Code blocks execution completed in {execution_time:.2f}s") + exec_status = ExecStatus.EXECUTED if success else ExecStatus.ERROR + # Precise or fallback execution status update + self.global_state.update_command_exec_status( + command_id, # type: ignore + exec_status, + combined_output, + ) + + # Hardware screenshot (separated from execution logic) + ok, err, screenshot = self._run_hardware_action({"type": "Screenshot"}) + if ok and screenshot is not None: + self.global_state.set_screenshot( + scale_screenshot_dimensions(screenshot, self.hwi)) + + # Get new screenshot ID and update command's post_screenshot_id + new_screenshot_id = self.global_state.get_screenshot_id() + if new_screenshot_id: + self.global_state.update_command_post_screenshot(command_id, new_screenshot_id) # type: ignore + logger.info(f"Updated post_screenshot_id for command {command_id}: {new_screenshot_id}") + + + self.global_state.increment_step_num() + + return self._create_execution_result( + success=success, + execution_time=execution_time, + action={"type": "code_blocks_execution", "result": combined_output} + ) + + except Exception as e: + execution_time = time.time() - execution_start + error_msg = f"Code blocks execution failed: {str(e)}" + logger.error(error_msg) + return self._create_execution_result(False, error_msg, execution_time) + + # ========== Extract Code Blocks ========== + def _extract_code_blocks(self, text: str) -> List[Tuple[str, str]]: + """Extract code blocks from markdown-style text""" + # Match ```language\ncode\n``` pattern + pattern = r'```(\w+)\n(.*?)\n```' + matches = re.findall(pattern, text, re.DOTALL) + + code_blocks = [] + for lang, code in matches: + lang = lang.lower() + code = code.strip() + if code: + code_blocks.append((lang, code)) + + return code_blocks + + # ========== Execute Action ========== + def _execute_action(self, subtask_id: str, action: Dict[str, Any], command_id: Optional[str] = None) -> Dict[str, Any]: + """ + Execute specific action + + Args: + subtask_id: subtask ID + action: Dictionary of action to execute + command_id: If provided, precisely update the screenshot and execution status of that command + """ + execution_start = time.time() + + try: + logger.info(f"Executing action for subtask {subtask_id}: {action}") + + # Special handling for memorize actions (write to artifact), moderate separation of execution logic and status updates + if isinstance(action, dict) and action.get("type") == "Memorize": + information = action.get("information", "") + if information: + # Business write operation + self.global_state.add_memorize_artifact(subtask_id, information) + logger.info(f"Memorize action processed for subtask {subtask_id}: {information[:100]}...") + + execution_success = True + error_message = None + execution_time = time.time() - execution_start + + # Status update + self._record_execution_result(subtask_id, execution_success, error_message, execution_time) + + + msg_preview = information.replace("\n", " ").strip()[:200] + exec_msg = f"Memorize stored ({len(information)} chars): {msg_preview}{'...' if len(information) > 200 else ''}" + self.global_state.update_command_exec_status( + command_id, # type: ignore + ExecStatus.EXECUTED, + exec_message=exec_msg, + ) + + + return self._create_execution_result( + success=execution_success, + error_message=error_message, + execution_time=execution_time, + action=action + ) + + # Pure execution (hardware interaction) + ok, err, screenshot = self._run_hardware_action(action) + execution_success = ok + error_message = err + + # Screenshot writing + if ok and screenshot is not None: + self.global_state.set_screenshot( + scale_screenshot_dimensions(screenshot, self.hwi)) + + # Get new screenshot ID and update command's post_screenshot_id + new_screenshot_id = self.global_state.get_screenshot_id() + if new_screenshot_id: + self.global_state.update_command_post_screenshot(command_id, new_screenshot_id) # type: ignore + logger.info(f"Updated post_screenshot_id for command {command_id}: {new_screenshot_id}") + + + self.global_state.increment_step_num() + # Recording and status updates + execution_time = time.time() - execution_start + self._record_execution_result(subtask_id, execution_success, error_message, execution_time) + + # Optional: write the status of this command based on execution results + self.global_state.update_command_exec_status( + command_id, # type: ignore + ExecStatus.EXECUTED if execution_success else ExecStatus.ERROR, + exec_message=("Action executed" if execution_success else f"Action failed: {error_message}") + ) + + + # Return results + result = self._create_execution_result( + success=execution_success, + error_message=error_message, + execution_time=execution_time, + action=action + ) + + logger.info(f"Action execution completed for subtask {subtask_id} in {execution_time:.2f}s") + return result + + except Exception as e: + execution_time = time.time() - execution_start + error_msg = f"Exception during action execution: {str(e)}" + logger.error(error_msg) + + # Record execution exception + self._record_execution_result(subtask_id, False, error_msg, execution_time) + + return self._create_execution_result(False, error_msg, execution_time) + + # ========== Record Execution Results ========== + def _record_execution_result(self, subtask_id: str, success: bool, error_message: Optional[str], execution_time: float): + """Record execution result to global state""" + try: + if success: + # Record successful execution event + self.global_state.log_operation("executor", "action_success", { + "subtask_id": subtask_id, + "execution_time": execution_time + }) + + # Can choose to update subtask status to executed, but state management should be decided by Controller + # self.global_state.update_subtask_status(subtask_id, SubtaskStatus.FULFILLED, "Action executed successfully") + + else: + # Record execution failure event + self.global_state.log_operation("executor", "action_error", { + "subtask_id": subtask_id, + "error_message": error_message, + "execution_time": execution_time + }) + + # Can choose to update subtask status to failed, but state management should be decided by Controller + # self.global_state.update_subtask_status(subtask_id, SubtaskStatus.REJECTED, f"Action execution failed: {error_message}") + + except Exception as e: + logger.warning(f"Failed to record execution result: {e}") + + # Create standardized execution results + def _create_execution_result(self, success: bool, error_message: Optional[str] = None, execution_time: float = 0.0, action: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: + """Create standardized execution results""" + result = { + "success": success, + "execution_time": execution_time, + "timestamp": time.time() + } + + if error_message: + result["error_message"] = error_message + + if action: + result["action"] = action + + return result + + + def get_execution_status(self) -> Dict[str, Any]: + """Get executor status information""" + return { + "executor_type": "NewExecutor", + "hardware_backend": getattr(self.hwi, 'backend', 'unknown'), + "execution_timeout": self.execution_timeout + } \ No newline at end of file diff --git a/mm_agents/maestro/maestro/new_global_state.py b/mm_agents/maestro/maestro/new_global_state.py new file mode 100644 index 0000000..58d7b00 --- /dev/null +++ b/mm_agents/maestro/maestro/new_global_state.py @@ -0,0 +1,1117 @@ +# new_global_state.py +import json +import os +import time +import logging +import io +import shutil +from pathlib import Path +from typing import List, Optional, Dict, Any, Union +from datetime import datetime +from enum import Enum + +from PIL import Image + +from ..utils.common_utils import Node +from ..utils.file_utils import ( + locked, safe_json_dump, safe_json_load, + safe_write_json, safe_read_json, safe_write_text, safe_read_text +) +from ..utils.id_utils import generate_uuid, generate_timestamp_id + +logger = logging.getLogger(__name__) + +# ========= Import Enums ========= +from .enums import ( + TaskStatus, SubtaskStatus, GateDecision, GateTrigger, + ControllerState, ExecStatus, WorkerDecision +) + +# ========= Import Data Models ========= +from .data_models import ( + TaskData, SubtaskData, CommandData, GateCheckData, ControllerStateData, + create_task_data, create_subtask_data, create_command_data, + create_gate_check_data, create_controller_state_data +) + +# ========= Import Simple Snapshot System ========= +from .simple_snapshot import SimpleSnapshot + +# ========= New GlobalState ========= +class NewGlobalState: + """Enhanced global state management for new architecture with role-based access""" + + def __init__( + self, + *, + screenshot_dir: str, + state_dir: str, + task_id: Optional[str] = None, + display_info_path: str = "", + ): + self.screenshot_dir = Path(screenshot_dir) + self.state_dir = Path(state_dir) + self.task_id = task_id or f"task-{generate_uuid()[:8]}" + + # State file paths + self.task_path = self.state_dir / "task.json" + self.subtasks_path = self.state_dir / "subtasks.json" + self.commands_path = self.state_dir / "commands.json" + self.gate_checks_path = self.state_dir / "gate_checks.json" + self.artifacts_path = self.state_dir / "artifacts.md" + self.supplement_path = self.state_dir / "supplement.md" + self.events_path = self.state_dir / "events.json" + self.controller_state_path = self.state_dir / "controller_state.json" + + # Legacy paths for compatibility + self.display_info_path = Path(display_info_path) if display_info_path else self.state_dir / "display.json" + + # Initialize snapshot system + self.snapshot_system = SimpleSnapshot(str(self.state_dir.parent)) + + # Ensure necessary directories and files exist + self._initialize_directories_and_files() + + def _initialize_directories_and_files(self): + """Initialize directories and create default files""" + # Create directories + self.screenshot_dir.mkdir(parents=True, exist_ok=True) + self.state_dir.mkdir(parents=True, exist_ok=True) + + # Initialize state files with default content + self._init_task_file() + self._init_subtasks_file() + self._init_commands_file() + self._init_gate_checks_file() + self._init_artifacts_file() + self._init_supplement_file() + self._init_events_file() + self._init_controller_state_file() + + # Initialize legacy files + if not self.display_info_path.exists(): + self.display_info_path.parent.mkdir(parents=True, exist_ok=True) + safe_write_text(self.display_info_path, "{}") + + def _init_task_file(self): + """Initialize task.json with default content""" + if not self.task_path.exists(): + default_task = { + "task_id": self.task_id, + "created_at": datetime.now().isoformat(), + "objective": "", + "status": TaskStatus.CREATED.value, + "current_subtask_id": None, + "step_num": 0, + "plan_num": 0, + "completed_subtasks": [], + "pending_subtasks": [], + # "qa_policy": { + # "per_subtask": True, + # "final_gate": True, + # "risky_actions": ["open", "submit", "hotkey"] + # } + } + safe_write_json(self.task_path, default_task) + + def _init_subtasks_file(self): + """Initialize subtasks.json with empty list""" + if not self.subtasks_path.exists(): + safe_write_text(self.subtasks_path, "[]") + + def _init_commands_file(self): + """Initialize commands.json with empty list""" + if not self.commands_path.exists(): + safe_write_text(self.commands_path, "[]") + + def _init_gate_checks_file(self): + """Initialize gate_checks.json with empty list""" + if not self.gate_checks_path.exists(): + safe_write_text(self.gate_checks_path, "[]") + + def _init_artifacts_file(self): + """Initialize artifacts.md with header""" + if not self.artifacts_path.exists(): + default_content = "" + safe_write_text(self.artifacts_path, default_content) + + def _init_supplement_file(self): + """Initialize supplement.md with header""" + if not self.supplement_path.exists(): + default_content = "" + safe_write_text(self.supplement_path, default_content) + + def _init_events_file(self): + """Initialize events.json with empty list""" + if not self.events_path.exists(): + safe_write_text(self.events_path, "[]") + + def _init_controller_state_file(self): + """Initialize controller_state.json with default content""" + if not self.controller_state_path.exists(): + default_controller_state = { + "current_state": ControllerState.GET_ACTION.value, + "trigger": "controller", + "trigger_details": "initialization", + "history_state": [], + "updated_at": datetime.now().isoformat() + } + safe_write_json(self.controller_state_path, default_controller_state) + + # ========= Utility Methods ========= + # _safe_write_json and _safe_read_json methods removed - now using safe_write_json and safe_read_json from file_utils + + def _generate_id(self, prefix: str) -> str: + """Generate unique ID with prefix""" + return f"{prefix}-{generate_uuid()[:4]}" + + # ========= Screenshot Management ========= + def get_screenshot(self) -> Optional[bytes]: + """Get latest screenshot as bytes""" + pngs = sorted(self.screenshot_dir.glob("*.png")) + if not pngs: + logger.warning("No screenshot found in %s", self.screenshot_dir) + return None + latest = pngs[-1] + screenshot = Image.open(latest) + buf = io.BytesIO() + screenshot.save(buf, format="PNG") + return buf.getvalue() + + def set_screenshot(self, img: Image.Image) -> str: + """Save screenshot and return screenshot ID""" + ts = int(time.time() * 1000) + screenshot_id = f"shot-{ts:06d}" + out = self.screenshot_dir / f"{screenshot_id}.png" + img.save(out) + logger.debug("Screenshot saved to %s", out) + return screenshot_id + + def get_screenshot_id(self) -> Optional[str]: + """Return the latest screenshot's ID (file stem) or None if none exist""" + pngs = sorted(self.screenshot_dir.glob("*.png")) + if not pngs: + logger.warning("No screenshot found in %s", self.screenshot_dir) + return None + latest = pngs[-1] + return latest.stem + + def get_screen_size(self) -> List[int]: + """Get current screen size from latest screenshot""" + pngs = sorted(self.screenshot_dir.glob("*.png")) + if not pngs: + logger.warning("No screenshot found, returning default size [1920, 1080]") + return [1920, 1080] + + latest = pngs[-1] + try: + screenshot = Image.open(latest) + width, height = screenshot.size + logger.info("Current screen size: [%d, %d]", width, height) + return [width, height] + except Exception as e: + logger.error("Failed to get screen size: %s", e) + return [1920, 1080] + + # ========= Task Management ========= + def get_task(self) -> TaskData: + """Get current task information""" + task_dict = safe_read_json(self.task_path, {}) + return TaskData.from_dict(task_dict) + + def set_task(self, task_data: TaskData) -> None: + """Update task information""" + safe_write_json(self.task_path, task_data.to_dict()) + + def update_task_status(self, status: TaskStatus) -> None: + """Update task status""" + task = self.get_task() + task.status = status.value + self.set_task(task) + + def set_task_objective(self, objective: str) -> None: + """Set task objective""" + task = self.get_task() + task.objective = objective + self.set_task(task) + + def set_manager_complete(self, complete: bool) -> None: + """Set whether manager has completed planning for the whole task""" + task = self.get_task() + task.managerComplete = bool(complete) + self.set_task(task) + + def set_current_subtask_id(self, subtask_id: str) -> None: + """Set current subtask ID""" + task = self.get_task() + task.current_subtask_id = subtask_id + self.set_task(task) + + def increment_step_num(self) -> None: + """Increment step number""" + task = self.get_task() + task.step_num += 1 + self.set_task(task) + + def increment_plan_num(self) -> None: + """Increment plan number""" + task = self.get_task() + task.plan_num += 1 + self.set_task(task) + + def get_plan_num(self) -> int: + """Get current plan number""" + task = self.get_task() + return task.plan_num + + def advance_to_next_subtask(self) -> None: + """ + Advance to the next subtask + + + Logic: + 1. If there is a current_subtask_id, move it to history_subtask_ids + 2. Set new current_subtask_id + 3. Remove the first element from pending_subtask_ids (if exists) + """ + task = self.get_task() + + # 1. Move current current_subtask_id to history_subtask_ids + if task.current_subtask_id: + if task.current_subtask_id not in task.history_subtask_ids: + task.history_subtask_ids.append(task.current_subtask_id) + + # 2. Set new current_subtask_id to the first element of pending_subtask_ids + task.current_subtask_id = task.pending_subtask_ids[0] + + # 3. Remove the first element from pending_subtask_ids (if exists and matches new ID) + if task.pending_subtask_ids and task.pending_subtask_ids[0] == task.current_subtask_id: + task.pending_subtask_ids.pop(0) + + self.set_task(task) + + def add_history_subtask(self, subtask_id: str) -> None: + """Add subtask to completed list""" + task = self.get_task() + if subtask_id not in task.history_subtask_ids: + task.history_subtask_ids.append(subtask_id) + self.set_task(task) + + def add_pending_subtask(self, subtask_id: str) -> None: + """Add subtask to pending list""" + task = self.get_task() + if subtask_id not in task.pending_subtask_ids: + task.pending_subtask_ids.append(subtask_id) + self.set_task(task) + + def remove_pending_subtask(self, subtask_id: str) -> None: + """Remove subtask from pending list""" + task = self.get_task() + if subtask_id in task.pending_subtask_ids: + task.pending_subtask_ids.remove(subtask_id) + self.set_task(task) + + # ========= Subtask Management ========= + def get_subtasks(self) -> List[SubtaskData]: + """Get all subtasks""" + subtasks_list = safe_read_json(self.subtasks_path, []) + return [SubtaskData.from_dict(subtask) for subtask in subtasks_list] + + def get_subtask(self, subtask_id: str) -> Optional[SubtaskData]: + """Get specific subtask by ID""" + subtasks = self.get_subtasks() + for subtask in subtasks: + if subtask.subtask_id == subtask_id: + return subtask + return None + + def add_subtask(self, subtask_data: SubtaskData) -> str: + """Add new subtask and return subtask ID""" + subtasks = self.get_subtasks() + subtask_id = subtask_data.subtask_id or self._generate_id("subtask") + subtask_data.subtask_id = subtask_id + subtask_data.task_id = self.task_id + subtask_data.attempt_no = subtask_data.attempt_no or 1 + subtask_data.status = subtask_data.status or SubtaskStatus.READY.value + subtask_data.reasons_history = subtask_data.reasons_history or [] + subtask_data.command_trace_ids = subtask_data.command_trace_ids or [] + subtask_data.gate_check_ids = subtask_data.gate_check_ids or [] + + subtasks.append(subtask_data) + safe_write_json(self.subtasks_path, [subtask.to_dict() for subtask in subtasks]) + + # Add to pending list + self.add_pending_subtask(subtask_id) + + return subtask_id + + def delete_subtasks(self, subtask_ids: List[str]) -> None: + """Delete subtasks by IDs and update task's pending and current pointers.""" + if not subtask_ids: + return + # Filter out the subtasks to be removed + current_subtasks = self.get_subtasks() + remaining_subtasks = [s for s in current_subtasks if s.subtask_id not in subtask_ids] + safe_write_json(self.subtasks_path, [s.to_dict() for s in remaining_subtasks]) + # Update task lists + task = self.get_task() + task.pending_subtask_ids = [sid for sid in task.pending_subtask_ids if sid not in subtask_ids] + # Defensive: also remove from completed list if present + # task.history_subtask_ids = [sid for sid in task.history_subtask_ids if sid not in subtask_ids] + # Clear current pointer if it was deleted + # if task.current_subtask_id in subtask_ids: + # task.current_subtask_id = None + self.set_task(task) + + def update_subtask_status(self, subtask_id: str, status: SubtaskStatus, reason: Optional[str] = None) -> None: + """Update subtask status and optionally add reason""" + subtasks = self.get_subtasks() + for subtask in subtasks: + if subtask.subtask_id == subtask_id: + subtask.status = status.value + if reason: + reason_entry = { + "at": datetime.now().isoformat(), + "text": reason + } + subtask.reasons_history.append(reason_entry) + subtask.last_reason_text = reason + break + + safe_write_json(self.subtasks_path, [subtask.to_dict() for subtask in subtasks]) + + def add_subtask_reason(self, subtask_id: str, reason: str) -> None: + """Add reason to subtask history""" + subtasks = self.get_subtasks() + for subtask in subtasks: + if subtask.subtask_id == subtask_id: + reason_entry = { + "at": datetime.now().isoformat(), + "text": reason + } + subtask.reasons_history.append(reason_entry) + subtask.last_reason_text = reason + break + + safe_write_json(self.subtasks_path, [subtask.to_dict() for subtask in subtasks]) + + def add_subtask_command_trace(self, subtask_id: str, command_id: str) -> None: + """Add command ID to subtask trace""" + subtasks = self.get_subtasks() + for subtask in subtasks: + if subtask.subtask_id == subtask_id: + if command_id not in subtask.command_trace_ids: + subtask.command_trace_ids.append(command_id) + break + + safe_write_json(self.subtasks_path, [subtask.to_dict() for subtask in subtasks]) + + def add_subtask_gate_check(self, subtask_id: str, gate_check_id: str) -> None: + """Add gate check ID to subtask""" + subtasks = self.get_subtasks() + for subtask in subtasks: + if subtask.subtask_id == subtask_id: + if gate_check_id not in subtask.gate_check_ids: + subtask.gate_check_ids.append(gate_check_id) + break + + safe_write_json(self.subtasks_path, [subtask.to_dict() for subtask in subtasks]) + + def update_subtask_last_gate(self, subtask_id: str, gate_decision: GateDecision) -> None: + """Update subtask last gate decision""" + subtasks = self.get_subtasks() + for subtask in subtasks: + if subtask.subtask_id == subtask_id: + subtask.last_gate_decision = gate_decision.value + break + + safe_write_json(self.subtasks_path, [subtask.to_dict() for subtask in subtasks]) + + # ========= Command Management ========= + def get_commands(self) -> List[CommandData]: + """Get all commands""" + commands_list = safe_read_json(self.commands_path, []) + return [CommandData.from_dict(command) for command in commands_list] + + def get_commands_for_subtask(self, subtask_id: str) -> List[CommandData]: + """Get all commands for a specific subtask, ordered by creation time""" + try: + # Get the subtask to find its command trace + subtask = self.get_subtask(subtask_id) + if not subtask or not subtask.command_trace_ids: + return [] + + # Get all commands for this subtask + commands = [] + for command_id in subtask.command_trace_ids: + command = self.get_command(command_id) + if command: + commands.append(command) + + # Sort by creation time (newest first) + commands.sort(key=lambda x: x.created_at, reverse=True) + return commands + except Exception as e: + logger.error(f"Error getting commands for subtask {subtask_id}: {e}") + return [] + + def get_command(self, command_id: str) -> Optional[CommandData]: + """Get specific command by ID""" + commands = self.get_commands() + for command in commands: + if command.command_id == command_id: + return command + return None + + def get_current_command_for_subtask(self, subtask_id: str) -> Optional[CommandData]: + """Get the latest command for a specific subtask""" + try: + # Get the subtask to find its command trace + subtask = self.get_subtask(subtask_id) + if not subtask or not subtask.command_trace_ids: + return None + + # Get the latest command ID from the trace + latest_command_id = subtask.command_trace_ids[-1] + + # Get the command data + return self.get_command(latest_command_id) + except Exception as e: + logger.error(f"Error getting current command for subtask {subtask_id}: {e}") + return None + + def get_latest_command_for_subtask(self, subtask_id: str) -> Optional[CommandData]: + """Get the latest command for a specific subtask (alias for get_current_command_for_subtask)""" + return self.get_current_command_for_subtask(subtask_id) + + def add_command(self, command_data: CommandData) -> str: + """Add new command and return command ID""" + commands = self.get_commands() + command_id = command_data.command_id or self._generate_id("cmd") + + command_data.command_id = command_id + command_data.task_id = self.task_id + + commands.append(command_data) + safe_write_json(self.commands_path, [command.to_dict() for command in commands]) + + # Add to subtask trace + if command_data.subtask_id: + self.add_subtask_command_trace(command_data.subtask_id, command_id) + + return command_id + + def update_command_exec_status(self, command_id: str, exec_status: ExecStatus, + exec_message: str = "", exec_latency_ms: int = 0) -> None: + """Update command execution status""" + commands = self.get_commands() + for command in commands: + if command.command_id == command_id: + command.exec_status = exec_status.value + command.exec_message = exec_message + command.exec_latency_ms = exec_latency_ms + command.executed_at = datetime.now().isoformat() + break + + safe_write_json(self.commands_path, [command.to_dict() for command in commands]) + + def update_command_worker_decision(self, command_id: str, worker_decision: str) -> None: + """Update command worker decision""" + commands = self.get_commands() + for command in commands: + if command.command_id == command_id: + command.worker_decision = worker_decision + break + + safe_write_json(self.commands_path, [command.to_dict() for command in commands]) + + def update_command_fields(self, command_id: str, **kwargs) -> None: + """Update multiple command fields at once""" + commands = self.get_commands() + for command in commands: + if command.command_id == command_id: + for field, value in kwargs.items(): + if hasattr(command, field): + setattr(command, field, value) + command.updated_at = datetime.now().isoformat() + break + + safe_write_json(self.commands_path, [command.to_dict() for command in commands]) + + def update_command_post_screenshot(self, command_id: str, post_screenshot_id: str) -> None: + """Update command post_screenshot_id after hardware execution""" + commands = self.get_commands() + for command in commands: + if command.command_id == command_id: + command.post_screenshot_id = post_screenshot_id + command.updated_at = datetime.now().isoformat() + logger.debug(f"Updated post_screenshot_id for command {command_id}: {post_screenshot_id}") + break + + safe_write_json(self.commands_path, [command.to_dict() for command in commands]) + + def get_commands_by_worker_decision(self, worker_decision: str) -> List[CommandData]: + """Get all commands with specific worker decision""" + commands = self.get_commands() + return [cmd for cmd in commands if cmd.worker_decision == worker_decision] + + def get_subtask_worker_decision(self, subtask_id: str) -> Optional[str]: + """Get the worker decision for the current command of a subtask""" + command = self.get_current_command_for_subtask(subtask_id) + return command.worker_decision if command else None + + # ========= Gate Check Management ========= + def get_gate_checks(self) -> List[GateCheckData]: + """Get all gate checks""" + gate_checks_list = safe_read_json(self.gate_checks_path, []) + return [GateCheckData.from_dict(gate_check) for gate_check in gate_checks_list] + + def get_gate_check(self, gate_check_id: str) -> Optional[GateCheckData]: + """Get specific gate check by ID""" + gate_checks = self.get_gate_checks() + for gate_check in gate_checks: + if gate_check.gate_check_id == gate_check_id: + return gate_check + return None + + def get_latest_gate_check_for_subtask( + self, subtask_id: str) -> Optional[GateCheckData]: + """Get the latest gate check for a specific subtask""" + gate_checks = self.get_gate_checks() + latest_gate = None + + for gate_check in gate_checks: + if gate_check.subtask_id == subtask_id: + if not latest_gate or gate_check.created_at > latest_gate.created_at: + latest_gate = gate_check + + return latest_gate + + def add_gate_check(self, gate_check_data: GateCheckData) -> str: + """Add new gate check and return gate check ID""" + gate_checks = self.get_gate_checks() + gate_check_id = gate_check_data.gate_check_id or self._generate_id("gc") + + gate_check_data.gate_check_id = gate_check_id + gate_check_data.task_id = self.task_id + + gate_checks.append(gate_check_data) + safe_write_json(self.gate_checks_path, [gate_check.to_dict() for gate_check in gate_checks]) + + # Add to subtask if specified + if gate_check_data.subtask_id: + self.add_subtask_gate_check(gate_check_data.subtask_id, gate_check_id) + + return gate_check_id + + # ========= Artifacts Management ========= + def get_artifacts(self) -> str: + """Get artifacts content""" + return safe_read_text(self.artifacts_path) + + def set_artifacts(self, content: str) -> None: + """Set artifacts content""" + safe_write_text(self.artifacts_path, content) + + def add_artifact(self, artifact_type: str, artifact_data: Dict[str, Any]) -> None: + """Add new artifact to artifacts.md""" + current_content = self.get_artifacts() + + # Add new artifact section + artifact_id = self._generate_id("art") + timestamp = datetime.now().isoformat() + + # Check if it's structured memorize data + if isinstance(artifact_data, dict) and "type" in artifact_data: + artifact_type_display = artifact_data.get("type", artifact_type) + + if artifact_data.get("type") == "analysis_result": + # Handle analysis result type artifact + analysis = artifact_data.get("analysis", "") + recommendations = artifact_data.get("recommendations", []) + + new_artifact = f""" +## {artifact_type_display} - {artifact_id} +- **Created**: {timestamp} +- **Type**: {artifact_type_display} +- **Analysis**: {analysis} +- **Recommendations**: {json.dumps(recommendations, indent=2, ensure_ascii=False)} + +--- +""" + else: + # Handle other types of artifacts + new_artifact = f""" +## {artifact_type_display} - {artifact_id} +- **Created**: {timestamp} +- **Type**: {artifact_type_display} +- **Data**: {json.dumps(artifact_data, indent=2, ensure_ascii=False)} + +--- +""" + else: + # Handle regular artifact data + new_artifact = f""" +## {artifact_type} - {artifact_id} +- **Created**: {timestamp} +- **Type**: {artifact_type} +- **Data**: {json.dumps(artifact_data, indent=2, ensure_ascii=False)} + +--- +""" + + updated_content = current_content + new_artifact + self.set_artifacts(updated_content) + + def add_memorize_artifact(self, subtask_id: str, memorize_content: str) -> None: + """Add memorized information with structured format for analyst processing + + Args: + subtask_id: The subtask this memory belongs to + memorize_content: The content to memorize, ideally in structured format + """ + current_content = self.get_artifacts() + + # Generate artifact ID and timestamp + artifact_id = self._generate_id("mem") + timestamp = datetime.now().isoformat() + + # Try to parse structured memorize content + note = "" + guidance = "" + + # Parse structured memorize content if available + if "NOTE:" in memorize_content: + parts = memorize_content.split("NOTE:") + if len(parts) > 1: + note_part = parts[1] + if "GUIDANCE:" in note_part: + ng_parts = note_part.split("GUIDANCE:") + note = ng_parts[0].strip() + guidance = ng_parts[1].strip() + else: + note = note_part.strip() + else: + # Simple data memorization + note = memorize_content.strip() + + # Create structured artifact + new_artifact = f""" +## Memorized Information - {artifact_id} +- **Created**: {timestamp} +- **Type**: memorize +- **Subtask**: {subtask_id} +- **Note**: {note if note else memorize_content} +- **Guidance**: {guidance if guidance else "Use this information as needed"} + +--- +""" + + updated_content = current_content + new_artifact + self.set_artifacts(updated_content) + + # Also add to events for tracking + self.add_event("worker", "memorize_added", f"Added memorize artifact {artifact_id} for subtask {subtask_id}") + + # ========= Supplement Management ========= + def get_supplement(self) -> str: + """Get supplement content""" + return safe_read_text(self.supplement_path) + + def set_supplement(self, content: str) -> None: + """Set supplement content""" + safe_write_text(self.supplement_path, content) + + def add_supplement_entry(self, entry_type: str, description: str, + sla: Optional[str] = None, status: str = "open") -> None: + """Add new supplement entry""" + current_content = self.get_supplement() + + entry_id = self._generate_id("sup") + timestamp = datetime.now().isoformat() + + new_entry = f""" +## {entry_type} - {entry_id} +- **Created**: {timestamp} +- **Type**: {entry_type} +- **Description**: {description} +- **SLA**: {sla or "Not specified"} +- **Status**: {status} + +--- +""" + + updated_content = current_content + new_entry + self.set_supplement(updated_content) + + # ========= Events Management ========= + def get_events(self) -> List[Dict[str, Any]]: + """Get all events""" + return safe_read_json(self.events_path, []) + + def add_event(self, actor: str, action: str, details: Optional[str] = None) -> str: + """Add new event""" + events = self.get_events() + event_id = self._generate_id("evt") + + event = { + "event_id": event_id, + "task_id": self.task_id, + "actor": actor, + "action": action, + "details": details, + "timestamp": datetime.now().isoformat() + } + + events.append(event) + safe_write_json(self.events_path, events) + + return event_id + + # ========= Role-based Access Methods ========= + # Controller methods + def controller_get_task_state(self) -> Dict[str, Any]: + """Controller: Get current task state for decision making""" + task = self.get_task() + return { + "task": task, + "current_subtask": self.get_subtask(task.current_subtask_id or ""), + "pending_subtasks": task.pending_subtask_ids + } + + def controller_switch_state(self, new_state: str) -> None: + """Controller: Switch to new state""" + self.add_event("controller", f"state_switch_to_{new_state}") + + # Manager methods + def manager_get_planning_context(self) -> Dict[str, Any]: + """Manager: Get context needed for planning""" + return { + "task": self.get_task(), + "subtasks": self.get_subtasks(), + "artifacts": self.get_artifacts(), + "supplement": self.get_supplement() + } + + def manager_create_subtask(self, title: str, description: str, + assignee_role: str = "operator") -> str: + """Manager: Create new subtask""" + subtask_data = create_subtask_data( + subtask_id="", # Will be generated by add_subtask + task_id=self.task_id, + title=title, + description=description, + assignee_role=assignee_role + ) + subtask_id = self.add_subtask(subtask_data) + self.add_event("manager", "create_subtask", f"Created subtask: {title}") + return subtask_id + + # Worker methods + def worker_get_execution_context(self, subtask_id: str) -> Dict[str, Any]: + """Worker: Get context needed for execution""" + subtask = self.get_subtask(subtask_id) + if not subtask: + return {} + + return { + "subtask": subtask, + "task": self.get_task(), + "screenshot": self.get_screenshot(), + "artifacts": self.get_artifacts() + } + + def worker_report_result(self, subtask_id: str, result: str, + reason_code: Optional[str] = None) -> None: + """Worker: Report execution result""" + if result == "success": + self.update_subtask_status(subtask_id, SubtaskStatus.FULFILLED) + elif result == "CANNOT_EXECUTE": + self.update_subtask_status(subtask_id, SubtaskStatus.REJECTED, reason_code) + elif result == "STALE_PROGRESS": + self.update_subtask_status(subtask_id, SubtaskStatus.STALE) + elif result == "NEED_SUPPLEMENT": + self.update_subtask_status(subtask_id, SubtaskStatus.PENDING, "Need supplement") + + self.add_event("worker", f"report_{result}", f"Subtask {subtask_id}: {result}") + + # Evaluator methods + def evaluator_get_quality_context(self, subtask_id: str) -> Dict[str, Any]: + """Evaluator: Get context needed for quality check""" + subtask = self.get_subtask(subtask_id) + if not subtask: + return {} + + return { + "subtask": subtask, + "commands": [self.get_command(cmd_id) for cmd_id in subtask.command_trace_ids], + "gate_checks": [self.get_gate_check(gc_id) for gc_id in subtask.gate_check_ids], + "screenshot": self.get_screenshot() + } + + def evaluator_make_decision(self, subtask_id: str, decision: GateDecision, + notes: str, trigger: GateTrigger = GateTrigger.PERIODIC_CHECK) -> str: + """Evaluator: Make quality gate decision""" + gate_check_data = create_gate_check_data( + gate_check_id="", # Will be generated by add_gate_check + task_id=self.task_id, + decision=decision.value, + notes=notes, + trigger=trigger.value, + subtask_id=subtask_id + ) + + gate_check_id = self.add_gate_check(gate_check_data) + self.add_event("evaluator", f"gate_{decision.value}", f"Decision: {decision.value}") + + return gate_check_id + + # Hardware methods + def hardware_execute_command(self, subtask_id: str, action: Dict[str, Any], + pre_screenshot: Image.Image) -> str: + """Hardware: Execute command and record results""" + # Save pre-screenshot + pre_screenshot_id = self.set_screenshot(pre_screenshot) + + # Create command entry + command_data = CommandData( + command_id="", # Will be generated by add_command + task_id=self.task_id, + action=action, + subtask_id=subtask_id, + pre_screenshot_id=pre_screenshot_id, + pre_screenshot_analysis="Pre-execution screenshot captured" + ) + + command_id = self.add_command(command_data) + self.add_event("hardware", "execute_command", f"Executed command: {action}") + + return command_id + + def hardware_complete_command(self, command_id: str, post_screenshot: Image.Image, + exec_status: ExecStatus, exec_message: str = "", + exec_latency_ms: int = 0) -> None: + """Hardware: Complete command execution""" + # Save post-screenshot + post_screenshot_id = self.set_screenshot(post_screenshot) + + # Update command with results + commands = self.get_commands() + for command in commands: + if command.command_id == command_id: + command.post_screenshot_id = post_screenshot_id + command.exec_status = exec_status.value + command.exec_message = exec_message + command.exec_latency_ms = exec_latency_ms + command.executed_at = datetime.now().isoformat() + break + + safe_write_json(self.commands_path, [command.to_dict() for command in commands]) + self.add_event("hardware", "complete_command", f"Completed command: {exec_status.value}") + + # ========= Legacy Compatibility Methods ========= + def get_obs_for_manager(self): + """Legacy: Get observation for manager""" + task = self.get_task() + return { + "screenshot": self.get_screenshot(), + "task": task, + "current_subtask": self.get_subtask(task.current_subtask_id or "") + } + + def get_obs_for_grounding(self): + """Legacy: Get observation for grounding""" + return {"screenshot": self.get_screenshot()} + + def get_obs_for_evaluator(self): + """Legacy: Get observation for evaluator""" + return { + "screenshot": self.get_screenshot(), + "subtasks": self.get_subtasks(), + "commands": self.get_commands(), + "gate_checks": self.get_gate_checks() + } + + def log_operation(self, module: str, operation: str, data: Dict[str, Any]) -> None: + """Legacy: Log operation (redirects to new event system)""" + self.add_event(module, operation, str(data)) + + # Also log to display_info for backward compatibility + try: + display_info = safe_read_json(self.display_info_path, {}) + if "operations" not in display_info: + display_info["operations"] = {} + if module not in display_info["operations"]: + display_info["operations"][module] = {} + + # If module doesn't exist, initialize as list + if not isinstance(display_info["operations"][module], list): + display_info["operations"][module] = [] + + operation_entry = { + "operation": operation, + "timestamp": time.time(), + **data + } + display_info["operations"][module].append(operation_entry) + + safe_write_json(self.display_info_path, display_info) + except Exception as e: + logger.warning(f"Failed to update display_info: {e}") + + def log_llm_operation(self, module: str, operation: str, data: Dict[str, Any], + str_input: Optional[str] = None, img_input: Optional[bytes] = None) -> None: + """ + Record LLM call operations with detailed input information + + Args: + module: Module name + operation: Operation name + data: Basic data (tokens, cost, duration, etc.) + str_input: Input text + img_input: Input image (bytes) + """ + # Get current screenshot ID + screenshot_id = self.get_screenshot_id() + + # Build enhanced operation record + enhanced_data = { + **data, + "llm_input": { + "text": str_input if str_input else None, + "screenshot_id": screenshot_id if screenshot_id else None + } + } + + # Record to display.json + self.log_operation(module, operation, enhanced_data) + + # Also record to event system + self.add_event(module, f"{operation}_llm_call", + f"LLM call with {len(str_input) if str_input else 0} chars text and screenshot {screenshot_id}") + + # ========= Controller State Management ========= + def get_controller_state(self) -> Dict[str, Any]: + """Get current controller state""" + return safe_read_json(self.controller_state_path, {}) + + def set_controller_state(self, controller_state: Dict[str, Any]) -> None: + """Update controller state""" + safe_write_json(self.controller_state_path, controller_state) + + def get_controller_current_state(self) -> ControllerState: + """Get current controller state""" + controller_state = self.get_controller_state() + if controller_state and controller_state.get("current_state"): + try: + return ControllerState(controller_state["current_state"]) + except ValueError: + return ControllerState.INIT + return ControllerState.INIT + + def set_controller_current_state(self, state: ControllerState): + """Set current controller state""" + controller_state = self.get_controller_state() + if controller_state: + controller_state["current_state"] = state.value + controller_state["updated_at"] = datetime.now().isoformat() + self.set_controller_state(controller_state) + + def update_controller_state(self, + new_state: ControllerState, + trigger_role: str = "controller", + trigger_details: str = "", + trigger_code: str = "controller"): + """Update controller current state and add to history""" + controller_state = self.get_controller_state() + + # Add current state to history if it's different + current_state = controller_state.get("current_state") + if current_state and current_state != new_state.value: + if "history_state" not in controller_state: + controller_state["history_state"] = [] + controller_state["history_state"].append(current_state) + + # Update current state, trigger info, trigger_code, state start time and timestamp + controller_state["current_state"] = new_state.value + controller_state["trigger_role"] = trigger_role + controller_state["trigger_details"] = trigger_details + controller_state["trigger_code"] = trigger_code + controller_state["state_start_time"] = time.time() + controller_state["updated_at"] = datetime.now().isoformat() + + self.set_controller_state(controller_state) + self.add_event("controller", "state_change", f"State changed to: {new_state.value} (trigger_role: {trigger_role}, details: {trigger_details}, trigger_code: {trigger_code})") + + def get_controller_state_enum(self) -> ControllerState: + """Get current controller state as enum""" + controller_state = self.get_controller_state() + state_str = controller_state.get("current_state", ControllerState.GET_ACTION.value) + try: + return ControllerState(state_str) + except ValueError: + logger.warning(f"Invalid controller state: {state_str}, defaulting to GET_ACTION") + return ControllerState.GET_ACTION + + def get_controller_state_start_time(self) -> float: + """Get controller state start time""" + controller_state = self.get_controller_state() + return controller_state.get("state_start_time", time.time()) + + def get_controller_state_history(self) -> List[str]: + """Get controller state history""" + controller_state = self.get_controller_state() + return controller_state.get("history_state", []) + + def reset_controller_state(self) -> None: + """Reset controller state to default""" + default_controller_state = { + "current_state": ControllerState.INIT.value, + "trigger_role": "controller", + "trigger_details": "reset", + "trigger_code": "controller", + "history_state": [], + "state_start_time": time.time(), + "updated_at": datetime.now().isoformat() + } + self.set_controller_state(default_controller_state) + self.add_event("controller", "state_reset", "Controller state reset to default") + + # ========= Snapshot System ========= + + def create_snapshot(self, description: str = "", snapshot_type: str = "manual", + config_params: Optional[Dict[str, Any]] = None) -> str: + """ + Create snapshot + + Args: + description: Snapshot description + snapshot_type: Snapshot type + config_params: Key configuration parameters, including: + - tools_dict: Tool configuration dictionary + - platform: Platform information + - enable_search: Search toggle + - env_password: Environment password + - enable_takeover: Takeover toggle + - enable_rag: RAG toggle + - backend: Backend type + - max_steps: Maximum steps + """ + return self.snapshot_system.create_snapshot(description, snapshot_type, config_params) + + def restore_snapshot(self, snapshot_id: str, target_runtime_dir: Optional[str] = None) -> Dict[str, Any]: + """ + Restore snapshot + + Returns: + Dictionary containing restore information and configuration parameters + """ + return self.snapshot_system.restore_snapshot(snapshot_id, target_runtime_dir) + + def list_snapshots(self) -> list: + """List all snapshots""" + return self.snapshot_system.list_snapshots() + + def delete_snapshot(self, snapshot_id: str) -> bool: + """Delete snapshot""" + return self.snapshot_system.delete_snapshot(snapshot_id) \ No newline at end of file diff --git a/mm_agents/maestro/maestro/new_manager.py b/mm_agents/maestro/maestro/new_manager.py new file mode 100644 index 0000000..464f5b5 --- /dev/null +++ b/mm_agents/maestro/maestro/new_manager.py @@ -0,0 +1,311 @@ +""" +New Manager Module for GUI-Agent Architecture +Responsible for task planning, decomposition, and resource allocation +""" + +import logging +import time +from datetime import datetime +from typing import Dict, Any, Union + +from ..tools.new_tools import NewTools +from ..core.new_knowledge import NewKnowledgeBase + +from .new_global_state import NewGlobalState +from .enums import ManagerStatus +from .manager.plan import PlanningHandler, PlanningScenario, PlanningResult +from .manager.supplement import SupplementHandler + +logger = logging.getLogger(__name__) + + +class NewManager: + """ + Enhanced Manager module for GUI-Agent architecture + Responsible for task planning, decomposition, and resource allocation + + Note: Planning prompts include generic guidance for configuration persistence (prefer editing config files; GUI changes require Save/Exit) and role assignment (Technician vs Operator). See planning_helpers.generate_planning_prompt. + """ + + def __init__( + self, + tools_dict: Dict[str, Any], + global_state: NewGlobalState, + local_kb_path: str = "", + platform: str = "Windows", + enable_search: bool = False, + enable_narrative: bool = False, + max_replan_attempts: int = 3, + ): + """ + Initialize the Manager module + + Args: + tools_dict: Dictionary containing tool configurations + global_state: Global state instance + local_kb_path: Path to local knowledge base + platform: Target platform (Windows/Darwin/Ubuntu) + enable_search: Whether to enable web search + max_replan_attempts: Maximum replanning attempts + """ + self.tools_dict = tools_dict + self.global_state = global_state + self.local_kb_path = local_kb_path + self.platform = platform + self.enable_search = enable_search + self.enable_narrative = enable_narrative + self.max_replan_attempts = max_replan_attempts + + # Initialize status + self.status = ManagerStatus.IDLE + self.plan_scenario = PlanningScenario.REPLAN + + # Initialize tools + self._initialize_tools() + + # Initialize knowledge base + self._initialize_knowledge_base() + + # Initialize handlers + self._initialize_handlers() + + logger.info("NewManager initialized successfully") + + def _initialize_tools(self): + """Initialize required tools with backward-compatible keys""" + self.planner_agent_name = "planner_role" + self.supplement_agent_name = "supplement_role" + self.dag_translator_agent_name = "dag_translator" + + # planner_agent + self.planner_agent = NewTools() + self.planner_agent.register_tool( + self.planner_agent_name, + self.tools_dict[self.planner_agent_name]["provider"], + self.tools_dict[self.planner_agent_name]["model"], + ) + + # dag_translator_agent + self.dag_translator_agent = NewTools() + self.dag_translator_agent.register_tool( + self.dag_translator_agent_name, + self.tools_dict[self.dag_translator_agent_name]["provider"], + self.tools_dict[self.dag_translator_agent_name]["model"], + ) + + # supplement_agent + self.supplement_agent = NewTools() + self.supplement_agent.register_tool( + self.supplement_agent_name, + self.tools_dict[self.supplement_agent_name]["provider"], + self.tools_dict[self.supplement_agent_name]["model"] + ) + + # objective_alignment agent + self.objective_alignment_agent = None + if self.tools_dict.get("objective_alignment"): + try: + self.objective_alignment_agent = NewTools() + self.objective_alignment_agent.register_tool( + "objective_alignment", + self.tools_dict["objective_alignment"]["provider"], + self.tools_dict["objective_alignment"]["model"], + ) + except Exception: + self.objective_alignment_agent = None + + # Embedding engine for Memory + self.embedding_engine = NewTools() + self.embedding_engine.register_tool( + "embedding", + self.tools_dict["embedding"]["provider"], + self.tools_dict["embedding"]["model"], + ) + + # Web search engine (optional) + if self.enable_search and self.tools_dict.get("websearch"): + self.search_engine = NewTools() + self.search_engine.register_tool( + "websearch", + self.tools_dict["websearch"]["provider"], + self.tools_dict["websearch"]["model"], + ) + else: + self.search_engine = None + + def _initialize_knowledge_base(self): + """Initialize knowledge base for RAG operations""" + kb_tools_dict = { + "query_formulator": self.tools_dict.get("query_formulator", {}), + "context_fusion": self.tools_dict.get("context_fusion", {}), + "narrative_summarization": self.tools_dict.get("narrative_summarization", {}), + "episode_summarization": self.tools_dict.get("episode_summarization", {}), + } + + self.knowledge_base = NewKnowledgeBase( + embedding_engine=self.embedding_engine, + local_kb_path=self.local_kb_path, + platform=self.platform, + Tools_dict=kb_tools_dict, + ) + + def _initialize_handlers(self): + """Initialize the planning and supplement handlers""" + self.planning_handler = PlanningHandler( + global_state=self.global_state, + planner_agent=self.planner_agent, + dag_translator_agent=self.dag_translator_agent, + knowledge_base=self.knowledge_base, + search_engine=self.search_engine, + platform=self.platform, + enable_search=self.enable_search, + enable_narrative=self.enable_narrative, + objective_alignment_agent=self.objective_alignment_agent, + ) + + self.supplement_handler = SupplementHandler( + global_state=self.global_state, + supplement_agent=self.supplement_agent, + search_engine=self.search_engine, + knowledge_base=self.knowledge_base, + enable_search=self.tools_dict[self.supplement_agent_name]["enable_search"], + ) + + def plan_task(self, scenario: Union[PlanningScenario, str]) -> PlanningResult: + """ + Execute task planning based on scenario and current trigger_code + + Args: + scenario: Planning scenario (INITIAL_PLAN|REPLAN|SUPPLEMENT or enum) + + Returns: + PlanningResult: Planning result with subtasks or supplement + """ + try: + scenario_enum = self._normalize_scenario(scenario) + self.status = ManagerStatus.PLANNING + + # Get current trigger_code to determine specific planning strategy + current_trigger_code = self._get_current_trigger_code() + + self.global_state.log_operation("manager", "planning_start", { + "scenario": scenario_enum.value, + "trigger_code": current_trigger_code, + "timestamp": time.time() + }) + + if scenario_enum == PlanningScenario.SUPPLEMENT: + return self._handle_supplement_scenario() + else: + return self._handle_planning_scenario(scenario_enum, current_trigger_code) + + except Exception as e: + logger.error(f"Planning failed: {e}") + self.status = ManagerStatus.ERROR + self.global_state.log_operation("manager", "planning_error", { + "error": str(e), + "timestamp": time.time() + }) + + return PlanningResult( + success=False, + scenario=self._normalize_scenario(scenario).value if isinstance( + scenario, str) else scenario.value, + subtasks=[], + supplement="", + reason=f"Planning failed: {str(e)}", + created_at=datetime.now().isoformat()) + finally: + self.status = ManagerStatus.IDLE + + def _normalize_scenario( + self, scenario: Union[PlanningScenario, str]) -> PlanningScenario: + """Normalize string/enum scenario to PlanningScenario enum (case-insensitive).""" + if isinstance(scenario, PlanningScenario): + return scenario + s = str(scenario).strip().lower() + if s in {"replan", "re-plan"}: + return PlanningScenario.REPLAN + if s in {"supplement", "supp"}: + return PlanningScenario.SUPPLEMENT + # Default to INITIAL_PLAN if unknown + return PlanningScenario.REPLAN + + def _handle_planning_scenario(self, scenario: PlanningScenario, trigger_code: str = "controller") -> PlanningResult: + """Handle planning scenarios (INITIAL_PLAN/REPLAN) with specific trigger_code context""" + return self.planning_handler.handle_planning_scenario(scenario, trigger_code) + + def _handle_supplement_scenario(self) -> PlanningResult: + """Handle supplement collection scenario""" + try: + logger.info("Starting supplement scenario handling") + result = self.supplement_handler.handle_supplement_scenario() + + # Validate result structure + if not isinstance(result, dict): + logger.error(f"Invalid supplement result type: {type(result)}") + return PlanningResult( + success=False, + scenario="supplement", + subtasks=[], + supplement="", + reason="Invalid supplement result structure", + created_at=datetime.now().isoformat() + ) + + # Log supplement result + self.global_state.log_operation("manager", "supplement_completed", { + "success": result.get("success", False), + "supplement_length": len(result.get("supplement", "")), + "timestamp": time.time() + }) + + return PlanningResult(**result) + + except Exception as e: + logger.error(f"Supplement scenario handling failed: {e}") + self.global_state.log_operation("manager", "supplement_error", { + "error": str(e), + "timestamp": time.time() + }) + + return PlanningResult( + success=False, + scenario="supplement", + subtasks=[], + supplement="", + reason=f"Supplement handling failed: {str(e)}", + created_at=datetime.now().isoformat() + ) + + def get_planning_status(self) -> Dict[str, Any]: + """Get current planning status""" + return { + "status": self.status.value, + "replan_attempts": self.planning_handler.replan_attempts, + "supplement_attempts": self.supplement_handler.supplement_attempts, + "planning_history_count": len(self.planning_handler.planning_history), + "max_replan_attempts": self.max_replan_attempts, + } + + def reset_planning_state(self): + """Reset planning state (useful for new tasks)""" + self.planning_handler.replan_attempts = 0 + self.supplement_handler.supplement_attempts = 0 + self.planning_handler.planning_history = [] + self.status = ManagerStatus.IDLE + + self.global_state.add_event("manager", "planning_reset", "Planning state reset") + + def can_replan(self) -> bool: + """Check if replanning is still allowed""" + return self.planning_handler.replan_attempts < self.max_replan_attempts + + def _get_current_trigger_code(self) -> str: + """Get current trigger_code""" + controller_state = self.global_state.get_controller_state() + return controller_state.get("trigger_code", "") + + +# Export a friendly alias to match the interface name used elsewhere +Manager = NewManager diff --git a/mm_agents/maestro/maestro/new_worker.py b/mm_agents/maestro/maestro/new_worker.py new file mode 100644 index 0000000..5ca08a3 --- /dev/null +++ b/mm_agents/maestro/maestro/new_worker.py @@ -0,0 +1,286 @@ +""" +New Worker Module for GUI-Agent Architecture (agents3) +- Provides an Operator role that integrates action planning (LLM) and visual grounding +- Produces Action dicts compatible with agents3 `Action.py` and `hardware_interface.py` +- Uses `NewGlobalState` for observations and event logging + +This implementation merges the essential behaviors of the legacy `worker.py` and `grounding.py` into a +single, concise Operator that is easy to invoke from the Controller. +""" + +from __future__ import annotations + +import logging +from typing import Any, Dict, List, Optional +from desktop_env.desktop_env import DesktopEnv + +from .new_global_state import NewGlobalState +from .data_models import create_command_data +from .enums import WorkerDecision + +from .sub_worker.technician import Technician +from .sub_worker.analyst import Analyst +from .sub_worker.operator import Operator + +logger = logging.getLogger(__name__) + + +class NewWorker: + """Worker facade exposing specialized roles. + + Provides access to: + - Operator: GUI interface operations with visual grounding + - Technician: System-level operations via terminal commands + - Analyst: Data analysis and recommendations + """ + + def __init__( + self, + tools_dict: Dict[str, Any], + global_state: NewGlobalState, + platform: str = "Windows", + enable_search: bool = False, + client_password: str = "osworld-public-evaluation", + screen_size: List[int] = [1920, 1080], + ) -> None: + self.operator = Operator( + tools_dict=tools_dict, + global_state=global_state, + platform=platform, + enable_search=enable_search, + screen_size=screen_size, + client_password=client_password, + ) + + self.technician = Technician( + tools_dict=tools_dict, + global_state=global_state, + platform=platform, + client_password=client_password, + ) + + self.analyst = Analyst( + tools_dict=tools_dict, + global_state=global_state, + platform=platform, + enable_search=enable_search, + ) + self._global_state = global_state + self._tools_dict = tools_dict + self._platform = platform + + def _normalize_action_for_outcome(self, outcome: str, raw_action: Optional[Dict[str, Any]], message: str) -> Dict[str, Any]: + """Normalize action dict based on outcome for unified commands schema.""" + action: Dict[str, Any] = {} + if outcome == WorkerDecision.STALE_PROGRESS.value: + # Represent stale as an action with optional candidate_action + action = {"type": "Stale"} + if raw_action: + action["candidate_action"] = raw_action + return action + if outcome == WorkerDecision.CANNOT_EXECUTE.value: + # Keep historical style: empty action + return {} + if outcome == WorkerDecision.SUPPLEMENT.value: + return {"type": "Supplement", "message": message or "."} + if outcome == WorkerDecision.WORKER_DONE.value: + return {"type": "Done", "message": message or ""} + # GENERATE_ACTION or others: keep raw action + return raw_action or {} + + def process_subtask_and_create_command(self) -> Optional[str]: + """Route to the right role, create command/decision if applicable, and return worker_decision string. + Returns one of WorkerDecision values or None on no-op/error. + """ + subtask_id = self._global_state.get_task().current_subtask_id + subtask = self._global_state.get_subtask(subtask_id) #type: ignore + if not subtask: + logging.warning(f"Worker: subtask {subtask_id} not found") + return None + + # Get current trigger_code to adjust processing logic + current_trigger_code = self._get_current_trigger_code() + logger.info(f"Worker processing subtask {subtask_id} with trigger_code: {current_trigger_code}") + + role = (subtask.assignee_role or "operator").lower() + try: + if role == "operator": + res = self.operator.generate_next_action( + subtask=subtask.to_dict(), # type: ignore + trigger_code=current_trigger_code + ) + outcome = (res.get("outcome") or "").strip() + raw_action = res.get("action") + action_plan = res.get("action_plan", "") + screenshot_analysis = res.get("screenshot_analysis", "") + message = res.get("message", "") + + normalized_action = self._normalize_action_for_outcome(outcome, raw_action, message) + + # Create command with complete information + cmd = create_command_data( + command_id="", + task_id=self._global_state.task_id, + action=normalized_action, + subtask_id=subtask_id, + assignee_role=subtask.assignee_role or "operator" + ) + command_id = self._global_state.add_command(cmd) + + pre_screenshot_analysis = screenshot_analysis + pre_screenshot_id = self._global_state.get_screenshot_id() + + # Update command with all fields including message and reason_text + self._global_state.update_command_fields( + command_id, + assignee_role=subtask.assignee_role or "operator", + action=normalized_action, + pre_screenshot_id=pre_screenshot_id, + pre_screenshot_analysis=pre_screenshot_analysis, + message=message, + reason_text=message, + ) + + # Update worker decision based on outcome + if outcome == WorkerDecision.GENERATE_ACTION.value and raw_action: + self._global_state.update_command_worker_decision(command_id, WorkerDecision.GENERATE_ACTION.value) + elif outcome == WorkerDecision.WORKER_DONE.value: + self._global_state.update_command_worker_decision(command_id, WorkerDecision.WORKER_DONE.value) + elif outcome == WorkerDecision.SUPPLEMENT.value: + self._global_state.update_command_worker_decision(command_id, WorkerDecision.SUPPLEMENT.value) + elif outcome == WorkerDecision.CANNOT_EXECUTE.value: + self._global_state.update_command_worker_decision(command_id, WorkerDecision.CANNOT_EXECUTE.value) + elif outcome == WorkerDecision.STALE_PROGRESS.value: + self._global_state.update_command_worker_decision(command_id, WorkerDecision.STALE_PROGRESS.value) + + if role == "technician": + res = self.technician.execute_task( + subtask=subtask.to_dict(), # type: ignore + trigger_code=current_trigger_code + ) + outcome = (res.get("outcome") or "").strip() + raw_action = res.get("action") + screenshot_analysis = res.get("screenshot_analysis", "") + message = res.get("message", "") + + normalized_action = self._normalize_action_for_outcome(outcome, raw_action, message) + + # Create command with complete information + cmd = create_command_data( + command_id="", + task_id=self._global_state.task_id, + action=normalized_action, + subtask_id=subtask_id, + assignee_role=subtask.assignee_role or "technician" + ) + command_id = self._global_state.add_command(cmd) + + pre_screenshot_analysis = screenshot_analysis + pre_screenshot_id = self._global_state.get_screenshot_id() + + # Update command with all fields including message and reason_text + self._global_state.update_command_fields( + command_id, + assignee_role=subtask.assignee_role or "technician", + action=normalized_action, + pre_screenshot_id=pre_screenshot_id, + pre_screenshot_analysis=pre_screenshot_analysis, + message=message, + reason_text=message, + ) + + # Update worker decision based on outcome + if outcome == WorkerDecision.GENERATE_ACTION.value and raw_action: + self._global_state.update_command_worker_decision(command_id, WorkerDecision.GENERATE_ACTION.value) + elif outcome == WorkerDecision.WORKER_DONE.value: + self._global_state.update_command_worker_decision(command_id, WorkerDecision.WORKER_DONE.value) + elif outcome == WorkerDecision.STALE_PROGRESS.value: + self._global_state.update_command_worker_decision(command_id, WorkerDecision.STALE_PROGRESS.value) + elif outcome == WorkerDecision.SUPPLEMENT.value: + self._global_state.update_command_worker_decision(command_id, WorkerDecision.SUPPLEMENT.value) + elif outcome == WorkerDecision.CANNOT_EXECUTE.value: + self._global_state.update_command_worker_decision(command_id, WorkerDecision.CANNOT_EXECUTE.value) + + if role == "analyst": + # Get artifacts content for analysis + artifacts_content = self._global_state.get_artifacts() + + # Check if there are memorize-related artifacts that need analysis + if "memorize" in artifacts_content.lower() or "information" in artifacts_content.lower(): + # If there is memorize content, use specialized memorize analysis type + res = self.analyst.analyze_task( + subtask=subtask.to_dict(), + analysis_type="memorize_analysis", + guidance=artifacts_content + ) + else: + # General analysis + res = self.analyst.analyze_task( + subtask=subtask.to_dict(), + analysis_type="general" + ) + + outcome = (res.get("outcome") or "").strip() + analysis = res.get("analysis", "") + recommendations = res.get("recommendations", []) + message = res.get("message", "") + + # For analyst, keep action payload for artifacts, but still carry reason_text + normalized_action = {"analysis": analysis, "recommendations": recommendations} + if outcome == WorkerDecision.WORKER_DONE.value: + # When analyst decides the subtask is done, convert to a Done action + normalized_action = {"type": "Done", "message": message or ""} + + # Create command with complete information + cmd = create_command_data( + command_id="", + task_id=self._global_state.task_id, + action=normalized_action, + subtask_id=subtask_id, + assignee_role=subtask.assignee_role or "analyst" + ) + command_id = self._global_state.add_command(cmd) + + pre_screenshot_analysis = "" + pre_screenshot_id = self._global_state.get_screenshot_id() + + # Update command with all fields + self._global_state.update_command_fields( + command_id, + assignee_role=subtask.assignee_role or "analyst", + action=normalized_action, + pre_screenshot_id=pre_screenshot_id, + pre_screenshot_analysis=pre_screenshot_analysis, + reason_text=message, + ) + + if outcome == WorkerDecision.GENERATE_ACTION.value: + self._global_state.update_command_worker_decision(command_id, WorkerDecision.GENERATE_ACTION.value) + return WorkerDecision.GENERATE_ACTION.value + elif outcome == WorkerDecision.STALE_PROGRESS.value: + self._global_state.update_command_worker_decision(command_id, WorkerDecision.STALE_PROGRESS.value) + return WorkerDecision.STALE_PROGRESS.value + elif outcome == WorkerDecision.WORKER_DONE.value: + self._global_state.update_command_worker_decision(command_id, WorkerDecision.WORKER_DONE.value) + return WorkerDecision.WORKER_DONE.value + else: + self._global_state.update_command_worker_decision(command_id, WorkerDecision.CANNOT_EXECUTE.value) + return WorkerDecision.CANNOT_EXECUTE.value + + return WorkerDecision.CANNOT_EXECUTE.value + except Exception as e: + logging.error(f"Worker: error processing subtask {subtask_id}: {e}") + return WorkerDecision.CANNOT_EXECUTE.value + + def _get_current_trigger_code(self) -> str: + """Get current trigger_code""" + try: + controller_state = self._global_state.get_controller_state() + return controller_state.get("trigger_code", "") + except Exception as e: + logger.warning(f"Failed to get current trigger_code: {e}") + return "" + + +# Export friendly alias +Worker = NewWorker \ No newline at end of file diff --git a/mm_agents/maestro/maestro/simple_snapshot.py b/mm_agents/maestro/maestro/simple_snapshot.py new file mode 100644 index 0000000..3a4abfd --- /dev/null +++ b/mm_agents/maestro/maestro/simple_snapshot.py @@ -0,0 +1,249 @@ +import os +import shutil +import json +from datetime import datetime +from pathlib import Path +from typing import Optional, Dict, Any + + +class SimpleSnapshot: + """Enhanced snapshot system - copy state folder, record screenshot IDs, and save key configuration parameters""" + + def __init__(self, runtime_dir: str): + self.runtime_dir = Path(runtime_dir) + self.snapshots_dir = self.runtime_dir / "snapshots" + self.state_dir = self.runtime_dir / "state" + self.screenshots_dir = self.runtime_dir / "cache" / "screens" + + # Ensure snapshot directory exists + self.snapshots_dir.mkdir(exist_ok=True) + + def create_snapshot(self, description: str = "", snapshot_type: str = "manual", + config_params: Optional[Dict[str, Any]] = None) -> str: + """ + Create snapshot + + Args: + description: Snapshot description + snapshot_type: Snapshot type + config_params: Key configuration parameters, including: + - tools_dict: Tools configuration dictionary + - platform: Platform information + - enable_search: Search toggle + - env_password: Environment password + - enable_takeover: Takeover toggle + - enable_rag: RAG toggle + - backend: Backend type + - max_steps: Maximum steps + """ + # Generate snapshot ID + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + snapshot_id = f"snapshot_{timestamp}" + + # Create snapshot directory + snapshot_dir = self.snapshots_dir / snapshot_id + snapshot_dir.mkdir(exist_ok=True) + + # 1. Copy entire state folder + if self.state_dir.exists(): + state_backup = snapshot_dir / "state" + # If target directory already exists, delete it first + if state_backup.exists(): + shutil.rmtree(state_backup) + shutil.copytree(self.state_dir, state_backup) + # print(f"✅ Copied state folder to: {state_backup}") + + # 2. Get current screenshot ID list + screenshot_ids = [] + if self.screenshots_dir.exists(): + # Support multiple image formats + for ext in ['*.png', '*.jpg', '*.jpeg', '*.webp']: + for screenshot_file in self.screenshots_dir.glob(ext): + screenshot_ids.append(screenshot_file.stem) + + # 3. Record snapshot metadata and configuration parameters + metadata = { + "snapshot_id": snapshot_id, + "timestamp": timestamp, + "description": description, + "type": snapshot_type, + "screenshot_ids": screenshot_ids, + "state_folder_copied": True, + "config_params": config_params or {} + } + + # Save metadata + metadata_file = snapshot_dir / "metadata.json" + with open(metadata_file, 'w', encoding='utf-8') as f: + json.dump(metadata, f, indent=2, ensure_ascii=False) + + # print(f"🎯 Snapshot created successfully: {snapshot_id}") + # print(f" Description: {description}") + # print(f" Screenshot count: {len(screenshot_ids)}") + # if config_params: + # print(f" Config parameters: {list(config_params.keys())}") + + return snapshot_id + + def restore_snapshot(self, snapshot_id: str, target_runtime_dir: Optional[str] = None) -> Dict[str, Any]: + """ + Restore snapshot + + Returns: + Dictionary containing restore information and configuration parameters + """ + snapshot_dir = self.snapshots_dir / snapshot_id + + if not snapshot_dir.exists(): + print(f"❌ Snapshot does not exist: {snapshot_id}") + return {} + + # Read metadata + metadata_file = snapshot_dir / "metadata.json" + if not metadata_file.exists(): + print(f"❌ Snapshot metadata file does not exist: {metadata_file}") + return {} + + with open(metadata_file, 'r', encoding='utf-8') as f: + metadata = json.load(f) + + # Determine target directory + if target_runtime_dir is None: + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + target_path = self.runtime_dir.parent / f"{self.runtime_dir.name}_restored_from_{snapshot_id}_{timestamp}" + else: + target_path = Path(target_runtime_dir) + + target_path.mkdir(parents=True, exist_ok=True) + + # 1. Restore state folder + state_backup = snapshot_dir / "state" + if state_backup.exists(): + target_state = target_path / "state" + if target_state.exists(): + shutil.rmtree(target_state) + shutil.copytree(state_backup, target_state) + print(f"✅ Restored state folder to: {target_state}") + + # 2. Restore cache/screens folder + target_cache = target_path / "cache" + target_screenshots = target_cache / "screens" + target_screenshots.mkdir(parents=True, exist_ok=True) + + restored_count = 0 + for screenshot_id in metadata.get("screenshot_ids", []): + # Try multiple image formats + source_file = None + target_file = None + for ext in ['.png', '.jpg', '.jpeg', '.webp']: + test_source = self.screenshots_dir / f"{screenshot_id}{ext}" + if test_source.exists(): + source_file = test_source + target_file = target_screenshots / f"{screenshot_id}{ext}" + break + + if source_file and target_file: + shutil.copy2(source_file, target_file) + restored_count += 1 + + print(f"✅ Restored {restored_count} screenshots to: {target_screenshots}") + + # 3. Create display.json file (if it doesn't exist) + target_display = target_path / "display.json" + if not target_display.exists(): + default_display = { + "restored_from_snapshot": snapshot_id, + "restore_time": datetime.now().isoformat(), + "operations": {} + } + with open(target_display, 'w', encoding='utf-8') as f: + json.dump(default_display, f, indent=2, ensure_ascii=False) + print(f"✅ Created display.json file") + + # Save restore information + restore_info = { + "restored_from": snapshot_id, + "restore_time": datetime.now().strftime("%Y%m%d_%H%M%S"), + "target_directory": str(target_path), + "screenshots_restored": restored_count + } + + restore_file = target_path / "restore_info.json" + with open(restore_file, 'w', encoding='utf-8') as f: + json.dump(restore_info, f, indent=2, ensure_ascii=False) + + print(f"🎉 Snapshot restored successfully!") + print(f" Target directory: {target_path}") + print(f" Restored screenshots: {restored_count}") + + # Return restore information and configuration parameters + return { + "restore_info": restore_info, + "target_directory": str(target_path), + "config_params": metadata.get("config_params", {}), + "snapshot_metadata": metadata + } + + + def list_snapshots(self) -> list: + """List all snapshots""" + snapshots = [] + + for snapshot_dir in self.snapshots_dir.iterdir(): + if snapshot_dir.is_dir(): + metadata_file = snapshot_dir / "metadata.json" + if metadata_file.exists(): + try: + with open(metadata_file, 'r', encoding='utf-8') as f: + metadata = json.load(f) + snapshots.append(metadata) + except: + continue + + # Sort by time + snapshots.sort(key=lambda x: x.get("timestamp", ""), reverse=True) + return snapshots + + def delete_snapshot(self, snapshot_id: str) -> bool: + """Delete snapshot""" + snapshot_dir = self.snapshots_dir / snapshot_id + + if not snapshot_dir.exists(): + print(f"❌ Snapshot does not exist: {snapshot_id}") + return False + + try: + shutil.rmtree(snapshot_dir) + print(f"✅ Snapshot deleted successfully: {snapshot_id}") + return True + except Exception as e: + print(f"❌ Failed to delete snapshot: {e}") + return False + + +# Usage example +if __name__ == "__main__": + # Use current runtime directory + runtime_dir = "runtime/20250824_162344" + + # Create snapshot system + snapshot_system = SimpleSnapshot(runtime_dir) + + # Mock configuration parameters + config_params = { + "tools_dict": {"example": "config"}, + "platform": "darwin", + "enable_search": True, + "env_password": "osworld-public-evaluation" + } + + # Create snapshot + snapshot_id = snapshot_system.create_snapshot("Test enhanced snapshot", "test", config_params) + + # List all snapshots + snapshots = snapshot_system.list_snapshots() + print(f"\n📋 Existing snapshot count: {len(snapshots)}") + + # Restore snapshot + # restore_result = snapshot_system.restore_snapshot(snapshot_id) + # print(f"Restore result: {restore_result}") \ No newline at end of file diff --git a/mm_agents/maestro/maestro/snapshot_restorer.py b/mm_agents/maestro/maestro/snapshot_restorer.py new file mode 100644 index 0000000..8c61d67 --- /dev/null +++ b/mm_agents/maestro/maestro/snapshot_restorer.py @@ -0,0 +1,335 @@ +#!/usr/bin/env python3 +""" +Snapshot Restore Tool - Restore snapshots and create GlobalState +""" + +import os +import sys +import json +from pathlib import Path +import time +from typing import Optional, Dict, Any, Tuple + +# Add project root directory to Python path +project_root = Path(__file__).parent.parent.parent +sys.path.insert(0, str(project_root)) + +from .simple_snapshot import SimpleSnapshot +from .new_global_state import NewGlobalState +from .controller.main_controller import MainController +from desktop_env.desktop_env import DesktopEnv + + +def _load_task_config_by_id(os_word_task_id: str, os_type_value: Optional[str] = None, test_config_base_dir: Optional[str] = None) -> Optional[Dict[str, Any]]: + """ + Automatically load task configuration by task ID + + Args: + os_word_task_id: Task ID + os_type_value: OS type value + test_config_base_dir: Test configuration base directory, if None then auto-detect + + Returns: + Task configuration dictionary, returns None if not found + """ + try: + # If no base directory specified, auto-detect + if test_config_base_dir is None: + current_platform = os_type_value + if current_platform == "Ubuntu": + test_config_base_dir = os.path.join(project_root, "evaluation_examples", "examples") + else: + print(f"⚠️ Unable to determine platform type: {current_platform}") + return None + + # Iterate through all domain directories to find task configuration + # First search in the specified directory + for domain_dir in os.listdir(test_config_base_dir): + domain_path = os.path.join(test_config_base_dir, domain_dir) + if os.path.isdir(domain_path): + config_file = os.path.join(domain_path, f"{os_word_task_id}.json") + if os.path.exists(config_file): + with open(config_file, "r", encoding="utf-8") as f: + task_config = json.load(f) + print(f"✅ Found task configuration: {domain_dir}/{os_word_task_id}.json") + return task_config + + # If not found in specified directory, try searching in another directory + if "examples_windows" in test_config_base_dir: + fallback_dir = os.path.join(project_root, "evaluation_examples", "examples") + else: + fallback_dir = os.path.join(project_root, "evaluation_examples", "examples_windows") + + if os.path.exists(fallback_dir): + for domain_dir in os.listdir(fallback_dir): + domain_path = os.path.join(fallback_dir, domain_dir) + if os.path.isdir(domain_path): + config_file = os.path.join(domain_path, f"{os_word_task_id}.json") + if os.path.exists(config_file): + with open(config_file, "r", encoding="utf-8") as f: + task_config = json.load(f) + print(f"✅ Found task configuration in fallback directory: {domain_dir}/{os_word_task_id}.json") + return task_config + + print(f"❌ Configuration file not found for task ID: {os_word_task_id}") + return None + + except Exception as e: + print(f"❌ Failed to load task configuration: {e}") + return None + + +def _build_env_from_config(env_config: Dict[str, Any], os_word_task_id: Optional[str] = None) -> Optional[DesktopEnv]: + """ + Rebuild DesktopEnv from snapshot env configuration. Returns None on failure. + + Args: + env_config: Environment configuration dictionary + os_word_task_id: Task ID, if provided will automatically load task configuration and set up environment + """ + try: + if not env_config or not env_config.get("present"): + return None + + provider_name = env_config.get("provider_name", "vmware") + path_to_vm = env_config.get("path_to_vm") + action_space = env_config.get("action_space", "pyautogui") + headless = bool(env_config.get("headless", False)) + require_a11y_tree = bool(env_config.get("require_a11y_tree", False)) + os_type_value = env_config.get("os_type") or os.getenv("USE_PRECREATE_VM", "Ubuntu") + + if not path_to_vm: + # Cannot build when missing required VM path + return None + + env = DesktopEnv( + provider_name=provider_name, + path_to_vm=path_to_vm, + action_space=action_space, + headless=headless, + require_a11y_tree=require_a11y_tree, + os_type=os_type_value, + ) + + # If task ID provided, automatically load task configuration and set up environment + if os_word_task_id: + print(f"🔄 Loading configuration and setting up environment for task {os_word_task_id}...") + task_config = _load_task_config_by_id(os_word_task_id, os_type_value) + if task_config: + try: + # Call reset method to set task configuration + env.reset(task_config=task_config) + time.sleep(10) + print(f"✅ Task {os_word_task_id} environment setup completed") + print("task_config", task_config) + except Exception as e: + print(f"⚠️ Task environment setup failed (will continue running without task configuration): {e}") + else: + print(f"⚠️ Unable to load configuration for task {os_word_task_id}, will continue running without task configuration") + else: + # If needed, call reset to ensure internal state is ready + try: + env.reset() + except Exception: + pass + + return env + except Exception as e: + print(f"❌ Environment build failed: {e}") + return None + + +def restore_snapshot_and_create_globalstate(runtime_dir: str, snapshot_id: Optional[str] = None, target_dir: Optional[str] = None): + """ + Restore snapshot and create GlobalState + + Args: + runtime_dir: Runtime directory path + snapshot_id: Snapshot ID, if None then list all snapshots for selection + target_dir: Target restore directory, if None then auto-generate + """ + # Create snapshot system + snapshot_system = SimpleSnapshot(runtime_dir) + + # If no snapshot ID specified, list all snapshots for selection + if snapshot_id is None: + snapshots = snapshot_system.list_snapshots() + if not snapshots: + print("❌ No snapshots found") + return None, None, {} + + print("📋 Available snapshots:") + for i, snapshot in enumerate(snapshots): + print(f" {i+1}. {snapshot['snapshot_id']}") + print(f" Description: {snapshot['description']}") + print(f" Type: {snapshot['type']}") + print(f" Time: {snapshot['timestamp']}") + print() + + + print(f"🔄 Restoring snapshot: {snapshot_id}") + + # Restore snapshot + restore_result = snapshot_system.restore_snapshot( + str(snapshot_id), target_dir + ) + + target_path = restore_result.get('target_directory') + + if not restore_result or not target_path: + print("❌ Snapshot restore failed") + return None, None, {} + + print(f"✅ Snapshot restore successful!") + print(f" Target directory: {target_path}") + + # Create GlobalState object + try: + # Build paths + state_dir = Path(target_path) / "state" + cache_dir = Path(target_path) / "cache" + screens_dir = cache_dir / "screens" + display_path = Path(target_path) / "display.json" + + # Ensure directories exist + state_dir.mkdir(exist_ok=True) + screens_dir.mkdir(parents=True, exist_ok=True) + + # Create GlobalState + global_state = NewGlobalState( + screenshot_dir=str(screens_dir), + state_dir=str(state_dir), + display_info_path=str(display_path) + ) + + print(f"🎉 GlobalState created successfully!") + print(f" Screenshot directory: {screens_dir}") + print(f" State directory: {state_dir}") + print(f" Display file: {display_path}") + + # Display configuration parameters + config_params = restore_result.get("config_params", {}) + if config_params: + print(f"\n📋 Snapshot configuration parameters:") + print(f" Platform: {config_params.get('platform', 'N/A')}") + print(f" Backend: {config_params.get('backend', 'N/A')}") + print(f" Max steps: {config_params.get('max_steps', 'N/A')}") + print(f" Search enabled: {config_params.get('enable_search', 'N/A')}") + print(f" Takeover enabled: {config_params.get('enable_takeover', 'N/A')}") + print(f" RAG enabled: {config_params.get('enable_rag', 'N/A')}") + + return global_state, target_path, config_params + + except Exception as e: + print(f"❌ Failed to create GlobalState: {e}") + return None, target_path, {} + + +def restore_maincontroller_from_globalstate( + runtime_dir: str, + snapshot_id: Optional[str] = None, + target_dir: Optional[str] = None, + os_word_task_id: Optional[str] = None + ) -> Optional[Tuple[MainController, str, Dict[str, Any]]]: + """ + Restore snapshot -> Build GlobalState -> Build MainController (skip initialization), and return controller, restore directory and configuration + """ + global_state, target_path, config_params = restore_snapshot_and_create_globalstate(runtime_dir, snapshot_id, target_dir) + if global_state is None: + return None + + # Extract controller-related settings from configuration parameters (provide reasonable defaults) + platform_value = config_params.get("platform", sys.platform) + backend_value = config_params.get("backend", "pyautogui") + enable_search_value = bool(config_params.get("enable_search", False)) + enable_takeover_value = bool(config_params.get("enable_takeover", False)) + enable_rag_value = bool(config_params.get("enable_rag", False)) + max_steps_value = int(config_params.get("max_steps", 50)) + env_password_value = config_params.get("env_password", "osworld-public-evaluation") + + # Protective check: target_path needs to be available + if not target_path: + print("❌ Unable to determine restore directory target_path") + return None + + # Try to extract task ID from GlobalState if user hasn't manually specified one + if os_word_task_id is None: + print(f"⚠️ No need to load task configuration") + else: + print(f"📋 Using user-specified task ID: {os_word_task_id}") + + # Restore environment information: prioritize env configuration from snapshot + env: Optional[DesktopEnv] = None + try: + env_config = config_params.get("env") or {} + env = _build_env_from_config(env_config, os_word_task_id) + except Exception as e: + print(f"⚠️ Environment restore failed (will continue running without environment): {e}") + env = None + + controller = MainController( + platform=platform_value, + enable_takeover=enable_takeover_value, + enable_search=enable_search_value, + enable_rag=enable_rag_value, + backend=backend_value, + user_query=(global_state.get_task().objective if hasattr(global_state, 'get_task') else ""), + max_steps=max_steps_value, + env=env, + env_password=env_password_value, + log_dir=str(Path(target_path)), + datetime_str=Path(target_path).name, + enable_snapshots=True, + global_state=global_state, + initialize_controller=False + ) + + print("✅ MainController restored from snapshot, ready to execute main loop") + return controller, target_path, config_params + + + +def main(): + """Main function""" + import argparse + + parser = argparse.ArgumentParser(description="Snapshot restore tool") + parser.add_argument("runtime_dir", help="Runtime directory path") + parser.add_argument("--snapshot", "-s", help="Snapshot ID") + parser.add_argument("--target", "-t", help="Target restore directory") + parser.add_argument("--task-id", help="Task ID for automatically loading task configuration and setting up environment") + parser.add_argument("--run", action="store_true", help="Run main loop immediately after restore") + + args = parser.parse_args() + + # Check if runtime directory exists + if not Path(args.runtime_dir).exists(): + print(f"❌ Runtime directory does not exist: {args.runtime_dir}") + return + + if args.run: + result = restore_maincontroller_from_globalstate(args.runtime_dir, args.snapshot, args.target, args.task_id) + if result is not None: + controller, target_path, _ = result + controller.execute_main_loop() + return + + # Only restore snapshot and create GlobalState + global_state, target_path, _ = restore_snapshot_and_create_globalstate( + args.runtime_dir, args.snapshot, args.target + ) + + if global_state: + print(f"\n🎯 Usage instructions:") + print(f" 1. GlobalState object created, can be used directly") + print(f" 2. Restored directory: {target_path}") + print(f" 3. Can call global_state.get_task() and other methods to read information") + print(f" 4. All state files restored to: {target_path}/state/") + print(f" 5. Screenshots restored to: {target_path}/cache/screens/") + print(f" 6. Call restore_maincontroller_from_globalstate(...).execute_main_loop() to continue execution") + if args.task_id: + print(f" 7. Task ID specified: {args.task_id}, will automatically load task configuration") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/mm_agents/maestro/maestro/sub_worker/__init__.py b/mm_agents/maestro/maestro/sub_worker/__init__.py new file mode 100644 index 0000000..bf76827 --- /dev/null +++ b/mm_agents/maestro/maestro/sub_worker/__init__.py @@ -0,0 +1,20 @@ +""" +Maestro Worker Package for GUI-Agent Architecture + +This package provides specialized worker roles for different types of tasks: +- Operator: GUI interface operations with visual grounding +- Technician: System-level operations via terminal commands +- Analyst: Data analysis and recommendations + +The main MaestroWorker class provides a unified interface to all these roles. +""" + +from .technician import Technician +from .analyst import Analyst +from .operator import Operator + +__all__ = [ + "Technician", + "Analyst", + "Operator", +] \ No newline at end of file diff --git a/mm_agents/maestro/maestro/sub_worker/analyst.py b/mm_agents/maestro/maestro/sub_worker/analyst.py new file mode 100644 index 0000000..8c869a1 --- /dev/null +++ b/mm_agents/maestro/maestro/sub_worker/analyst.py @@ -0,0 +1,603 @@ +import json +import re +import time +import logging +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Tuple, Union + +from ...tools.new_tools import NewTools +from ..new_global_state import NewGlobalState +from ..enums import SubtaskStatus, WorkerDecision + +logger = logging.getLogger(__name__) + + +@dataclass +class StepResult: + """Lightweight step result for controller/evaluator handoff.""" + step_id: str + ok: bool + error: Optional[str] + latency_ms: int + outcome: str + action: Optional[Dict[str, Any]] = None + + +class Analyst: + """Analyst role: analyze artifacts content and provide analytical support. + + Responsibilities: + - Analyze artifacts content and stored information + - Provide recommendations and insights based on data + - Extract and process information from stored content + - Support decision-making with data analysis + - Handle non-GUI interaction subtasks + + Tools_dict requirements: + - analyst_agent: {"provider": str, "model": str} - LLM for analysis + """ + + def __init__( + self, + tools_dict: Dict[str, Any], + global_state: NewGlobalState, + platform: str = "unknown", + enable_search: bool = False, + ) -> None: + self.tools_dict = tools_dict + self.global_state = global_state + self.platform = platform + + # LLM for analysis + self.analyst_agent_name = "analyst_role" + self.analyst_agent = NewTools() + self.analyst_agent.register_tool( + self.analyst_agent_name, + self.tools_dict[self.analyst_agent_name]["provider"], + self.tools_dict[self.analyst_agent_name]["model"], + ) + + def analyze_task( + self, + *, + subtask: Dict[str, Any], + guidance: Optional[str] = None, + analysis_type: str = "general", + ) -> Dict[str, Any]: + """Analyze the current state and provide recommendations based on artifacts content. + + Args: + subtask: Current subtask information + guidance: Optional guidance from manager + analysis_type: Type of analysis (kept for compatibility, not used in new design) + + Returns a dict containing: + - analysis: detailed analysis result + - recommendations: list of recommendations + - step_result: StepResult as dict + - outcome: + - message: optional message when outcome is WORKER_DONE or other decisions + """ + # Get all required context information + task = self.global_state.get_task() + artifacts_content = self.global_state.get_artifacts() + history_subtasks = self.global_state.get_subtasks() # All subtasks including completed ones + supplement_content = self.global_state.get_supplement() + + # Only keep top 2 subtasks whose status is NOT READY, ordered by updated_at desc + try: + non_ready_subtasks = [ + s for s in history_subtasks + if getattr(s, 'status', '') != SubtaskStatus.READY.value + ] + non_ready_subtasks.sort(key=lambda x: getattr(x, 'updated_at', ''), reverse=True) + history_subtasks = non_ready_subtasks[:2] + except Exception as e: + logger.warning(f"Filter non-ready subtasks failed: {e}") + + # Get current subtask commands + subtask_id = subtask.get('subtask_id') + subtask_commands = [] + if subtask_id: + subtask_obj = self.global_state.get_subtask(subtask_id) + if subtask_obj and hasattr(subtask_obj, 'command_trace_ids'): + for cmd_id in subtask_obj.command_trace_ids: + cmd = self.global_state.get_command(cmd_id) + if cmd: + subtask_commands.append(cmd.to_dict() if hasattr(cmd, 'to_dict') else cmd) + + # Check if we have sufficient information to analyze + if not artifacts_content and not history_subtasks and not supplement_content: + msg = "No content available for analysis (artifacts, history, or supplement)" + logger.warning(msg) + self.global_state.add_event("analyst", "no_content", msg) + result = StepResult( + step_id=f"{subtask.get('subtask_id','unknown')}.analyst-0", + ok=False, + error=msg, + latency_ms=0, + outcome=WorkerDecision.STALE_PROGRESS.value, + ) + return { + "analysis": "", + "recommendations": [], + "step_result": result.__dict__, + "outcome": WorkerDecision.STALE_PROGRESS.value, + } + + # Build analysis prompt with all context information + analysis_prompt = self._build_analysis_prompt( + subtask, task, artifacts_content, history_subtasks, + supplement_content, subtask_commands, guidance + ) + + # Call analyst agent + t0 = time.time() + try: + analysis_result, total_tokens, cost_string = self.analyst_agent.execute_tool( + self.analyst_agent_name, + {"str_input": analysis_prompt}, + ) + latency_ms = int((time.time() - t0) * 1000) + + self.global_state.log_llm_operation( + "analyst", + "analysis_completed", + { + "tokens": total_tokens, + "cost": cost_string, + "duration": latency_ms / 1000.0, + "llm_output": analysis_result + }, + str_input=analysis_prompt + ) + except Exception as e: + err = f"ANALYSIS_FAILED: {e}" + logger.warning(err) + self.global_state.add_event("analyst", "analysis_failed", err) + result = StepResult( + step_id=f"{subtask.get('subtask_id','unknown')}.analyst-1", + ok=False, + error=err, + latency_ms=int((time.time() - t0) * 1000), + outcome=WorkerDecision.CANNOT_EXECUTE.value, + ) + return { + "analysis": "", + "recommendations": [], + "step_result": result.__dict__, + "outcome": WorkerDecision.CANNOT_EXECUTE.value, + } + + # 1) Check explicit DONE decision markers or JSON fields + try: + decision = self._infer_done_decision(analysis_result) + except Exception: + decision = None + if decision == "done": + # Return a WORKER_DONE outcome with a message + msg = self._extract_done_message(analysis_result) + ok = True + outcome = WorkerDecision.WORKER_DONE.value + err = None + result = StepResult( + step_id=f"{subtask.get('subtask_id','unknown')}.analyst-1", + ok=ok, + error=err, + latency_ms=latency_ms, + outcome=outcome, + ) + self.global_state.add_event("analyst", "analysis_done", "Analyst marked subtask as done") + return { + "analysis": "", + "recommendations": [], + "message": msg, + "step_result": result.__dict__, + "outcome": outcome, + } + + # 2) Try to extract CandidateAction first (stale continuation) + try: + candidate_action = self._extract_candidate_action(analysis_result) + if isinstance(candidate_action, dict) and candidate_action: + ok = True + outcome = WorkerDecision.STALE_PROGRESS.value + err = None + result = StepResult( + step_id=f"{subtask.get('subtask_id','unknown')}.analyst-1", + ok=ok, + error=err, + latency_ms=latency_ms, + outcome=outcome, + action=candidate_action, # type: ignore + ) + self.global_state.add_event("analyst", "candidate_action_detected", "CandidateAction suggested in analysis") + return { + "analysis": "", + "recommendations": [], + "candidate_action": candidate_action, + "step_result": result.__dict__, + "outcome": outcome, + } + except Exception: + # Ignore and continue to parse JSON + pass + + # 3) Parse analysis JSON output + try: + parsed_result = self._parse_analysis_result(analysis_result) + ok = True + outcome = WorkerDecision.GENERATE_ACTION.value + err = None + except Exception as e: + ok = False + outcome = WorkerDecision.CANNOT_EXECUTE.value + parsed_result = { + "analysis": f"Failed to parse analysis: {str(e)}", + "recommendations": [], + "summary": "", + } + err = f"PARSE_ANALYSIS_FAILED: {e}" + logger.warning(err) + + result = StepResult( + step_id=f"{subtask.get('subtask_id','unknown')}.analyst-1", + ok=ok, + error=err, + latency_ms=latency_ms, + outcome=outcome, + ) + + # Log analysis result + self.global_state.add_event( + "analyst", + "analysis_ready" if ok else "analysis_failed", + f"outcome={outcome}", + ) + + return { + "analysis": parsed_result.get("analysis", ""), + "recommendations": parsed_result.get("recommendations", []), + "summary": parsed_result.get("summary", ""), + "step_result": result.__dict__, + "outcome": outcome, + } + + def _build_analysis_prompt( + self, + subtask: Dict[str, Any], + task: Any, + artifacts_content: str, + history_subtasks: List[Any], + supplement_content: str, + subtask_commands: List[Dict[str, Any]], + guidance: Optional[str] + ) -> str: + """Build comprehensive analysis prompt with all context information.""" + + # Format task information + task_info = [] + if task: + task_info.extend([ + f"**Task ID**: {task.task_id}", + f"**Task Objective**: {task.objective}", + ]) + else: + task_info.append("**Task Information**: Not available") + + # Format subtask information + subtask_info = [ + f"**Subtask Title**: {subtask.get('title', 'Not specified')}", + f"**Subtask Description**: {subtask.get('description', 'Not specified')}", + f"**Assignee Role**: {subtask.get('assignee_role', 'analyst')}", + ] + + # Format guidance information + guidance_info = [] + if guidance: + guidance_info.extend([ + "# Specific Guidance", + f"**Instructions**: {guidance}", + "" + ]) + + # Format artifacts content with analysis + artifacts_section = [] + if artifacts_content and artifacts_content.strip(): + artifacts_section.extend([ + "# Available Artifacts Content", + f"**Content Length**: {len(artifacts_content)} characters", + f"**Content**:", + "```", + artifacts_content, + "```", + "" + ]) + else: + artifacts_section.extend([ + "# Available Artifacts Content", + "**Status**: No artifacts content available", + "" + ]) + + # Format supplement content + supplement_section = [] + if supplement_content and supplement_content.strip(): + supplement_section.extend([ + "# Supplement Information", + f"**Content Length**: {len(supplement_content)} characters", + f"**Content**:", + "```", + supplement_content, + "```", + "" + ]) + else: + supplement_section.extend([ + "# Supplement Information", + "**Status**: No supplement content available", + "" + ]) + + # Format historical context + history_section = [] + if history_subtasks: + history_section.extend([ + "# Historical Subtasks Context", + f"**Total Subtasks**: {len(history_subtasks)}", + "**Recent Subtask Summary**:" + ]) + + # Show last 5 subtasks with status + recent_subtasks = history_subtasks[-5:] if len(history_subtasks) > 5 else history_subtasks + for i, hist_subtask in enumerate(recent_subtasks, 1): + if hasattr(hist_subtask, 'to_dict'): + hist_data = hist_subtask.to_dict() + else: + hist_data = hist_subtask + + title = hist_data.get('title', 'Unknown Task') + status = hist_data.get('status', 'Unknown') + role = hist_data.get('assignee_role', 'Unknown') + history_section.append(f"{i}. **{title}** (Role: {role}) - Status: {status}") + + history_section.append("") + else: + history_section.extend([ + "# Historical Subtasks Context", + "**Status**: No historical subtask information available", + "" + ]) + + # Format command execution context + commands_section = [] + if subtask_commands: + commands_section.extend([ + "# Current Subtask Command History", + f"**Total Commands**: {len(subtask_commands)}", + "**Recent Command Summary**:" + ]) + + # Show last 3 commands + recent_commands = subtask_commands[-3:] if len(subtask_commands) > 3 else subtask_commands + for i, cmd in enumerate(recent_commands, 1): + action_type = "Unknown" + if isinstance(cmd.get('action'), dict): + action_type = cmd.get('action', {}).get('type', 'Unknown') + + exec_status = cmd.get('exec_status', 'Unknown') + worker_decision = cmd.get('worker_decision', 'Unknown') + commands_section.append(f"{i}. **{action_type}** - Execution: {exec_status}, Decision: {worker_decision}") + + commands_section.append("") + else: + commands_section.extend([ + "# Current Subtask Command History", + "**Status**: No command execution history available", + "" + ]) + + # Build analysis requirements + requirements_section = [ + "# Analysis Requirements", + "As the Analyst role, you must:", + "", + "1. **Comprehensive Review**: Analyze all available information sources", + "2. **Context Integration**: Connect information across artifacts, history, and current state", + "3. **Accurate Extraction**: Extract precise, verifiable data and insights", + "4. **Actionable Recommendations**: Provide specific, implementable suggestions", + "5. **Clear Communication**: Present findings in structured, understandable format", + "", + "## Special Considerations:", + "- Focus on information that helps complete the current subtask", + "- If this is a 'memorize' analysis, prioritize information retention and recall", + "- For question-answering, provide comprehensive answers with evidence", + "- When data is insufficient, clearly state limitations", + "- Base all conclusions on available evidence, not assumptions", + "", + "## Completion Signaling:", + "- If you determine the current subtask is fully completed by analysis alone, you may explicitly mark it as DONE.", + "- You can signal completion using one of the following:", + " Structured markers:", + " DECISION_START\n Decision: DONE\n Message: [why it's done]\n DECISION_END", + "" + ] + + # Output format specification + output_section = [ + "# Required Output Format", + "Your response supports two mutually exclusive output modes. Do NOT mix them in the same response.", + "", + "- JSON Mode (default when not making a decision): Return exactly one JSON object with these fields:", + "```json", + "{", + ' "analysis": "Detailed analysis description explaining your findings and methodology",', + ' "recommendations": ["Specific actionable recommendation 1", "Specific actionable recommendation 2"],', + ' "summary": "Brief summary of key findings and conclusions"', + "}", + "```", + "", + "## Field Requirements:", + "- **analysis**: Comprehensive explanation of findings (required)", + "- **recommendations**: List of specific, actionable suggestions (required, can be empty list)", + "- **summary**: Concise overview of key points (required)", + "", + "- Decision Mode (when you must signal task state): Use the structured decision markers exactly as specified below and do not include JSON.", + "- If you determine the current subtask is fully completed by analysis alone, you may explicitly mark it as DONE so the controller can proceed.", + "- You can signal completion using one of the following methods:", + "Structured decision markers:", + " DECISION_START", + " Decision: DONE", + " Message: [why it's done and no further action is required]", + " DECISION_END", + ] + + # Combine all sections + prompt_sections = [ + "# Analysis Task", + "You are a professional data analyst responsible for analyzing task-related information.", + "", + "## Task Context", + *task_info, + "", + "## Current Subtask Information", + *subtask_info, + "" + ] + + if guidance_info: + prompt_sections.extend(guidance_info) + + prompt_sections.extend(artifacts_section) + prompt_sections.extend(supplement_section) + prompt_sections.extend(history_section) + prompt_sections.extend(commands_section) + prompt_sections.extend(requirements_section) + prompt_sections.extend(output_section) + + return "\n".join(prompt_sections) + + def _parse_analysis_result(self, result: str) -> Dict[str, Any]: + """Parse the analysis result from LLM response with improved error handling.""" + + # Try to extract JSON from markdown code blocks first + json_patterns = [ + r'```json\s*(\{.*?\})\s*```', # Standard JSON code block + r'```\s*(\{.*?\})\s*```', # Code block without json label + r'(\{[^{}]*"analysis"[^{}]*\})', # JSON with analysis field + ] + + for pattern in json_patterns: + json_match = re.search(pattern, result, re.DOTALL) + if json_match: + try: + parsed = json.loads(json_match.group(1)) + return self._validate_and_format_result(parsed) + except json.JSONDecodeError: + continue + + # Try to parse the entire result as JSON + try: + parsed = json.loads(result.strip()) + return self._validate_and_format_result(parsed) + except json.JSONDecodeError: + pass + + # Final fallback: extract information using regex patterns + analysis_match = re.search(r'"analysis":\s*"([^"]*)"', result, re.DOTALL) + recommendations_match = re.search(r'"recommendations":\s*\[(.*?)\]', result, re.DOTALL) + summary_match = re.search(r'"summary":\s*"([^"]*)"', result, re.DOTALL) + + analysis = analysis_match.group(1) if analysis_match else result + recommendations = [] + if recommendations_match: + rec_text = recommendations_match.group(1) + recommendations = re.findall(r'"([^"]*)"', rec_text) + + summary = summary_match.group(1) if summary_match else "Analysis completed" + + return { + "analysis": analysis, + "recommendations": recommendations, + "summary": summary + } + + def _validate_and_format_result(self, parsed: Dict[str, Any]) -> Dict[str, Any]: + """Validate and format the parsed result to ensure required fields.""" + return { + "analysis": str(parsed.get("analysis", "")).strip() or "No analysis provided", + "recommendations": list(parsed.get("recommendations", [])), + "summary": str(parsed.get("summary", "")).strip() or "Analysis completed" + } + + def _extract_candidate_action(self, text: str) -> Optional[Dict[str, Any]]: + """Try to extract a CandidateAction JSON block from the LLM output.""" + try: + # Pattern 1: explicit CandidateAction: { ... } + m = re.search(r"CandidateAction\s*:\s*(\{.*?\})", text, re.DOTALL) + if m: + return json.loads(m.group(1)) + + # Pattern 2: fenced json with a top-level object having type/selector + m2 = re.search(r"```json\s*(\{.*?\})\s*```", text, re.DOTALL) + if m2: + candidate = json.loads(m2.group(1)) + if isinstance(candidate, dict): + return candidate + except Exception as e: + logger.debug(f"No CandidateAction found: {e}") + return None + + def _infer_done_decision(self, text: str) -> Optional[str]: + """Infer DONE decision from LLM output. + Supports structured markers and JSON fields to indicate completion. + Returns 'done' if detected, otherwise None. + """ + try: + # Structured markers + if "DECISION_START" in text and "DECISION_END" in text: + start = text.find("DECISION_START") + len("DECISION_START") + end = text.find("DECISION_END") + if 0 < start < end: + content = text[start:end] + m = re.search(r"Decision:\s*(DONE)", content, re.IGNORECASE) + if m: + return "done" + # JSON fields + try: + parsed = json.loads(text) + decision = str(parsed.get("decision", "")).strip().lower() + outcome = str(parsed.get("outcome", "")).strip().upper() + if decision == "done" or outcome == "WORKER_DONE": + return "done" + except Exception: + pass + except Exception: + pass + return None + + def _extract_done_message(self, text: str) -> str: + """Extract a message explaining DONE decision, best-effort.""" + try: + if "DECISION_START" in text and "DECISION_END" in text: + start = text.find("DECISION_START") + len("DECISION_START") + end = text.find("DECISION_END") + if 0 < start < end: + content = text[start:end] + m = re.search(r"Message:\s*(.+)", content, re.IGNORECASE | re.DOTALL) + if m: + import re as _re + msg = m.group(1).strip() + msg = _re.sub(r'\s+', ' ', msg) + return msg + # JSON fields + try: + parsed = json.loads(text) + msg = str(parsed.get("message", "")).strip() + if msg: + return msg + except Exception: + pass + except Exception: + pass + return "Analysis indicates the subtask is complete." \ No newline at end of file diff --git a/mm_agents/maestro/maestro/sub_worker/operator.py b/mm_agents/maestro/maestro/sub_worker/operator.py new file mode 100644 index 0000000..c6642ef --- /dev/null +++ b/mm_agents/maestro/maestro/sub_worker/operator.py @@ -0,0 +1,723 @@ +""" +Operator Module for GUI-Agent Architecture (agents3) +- Merges action planning (LLM) and visual grounding +- Generates next UI action and grounds it to screen coordinates +- Produces Action dicts compatible with agents3 Action.py and hardware_interface.py +""" + +from __future__ import annotations + +import time +import logging +from typing import Any, Dict, List, Optional + +from ...tools.new_tools import NewTools +from ...utils.common_utils import ( + parse_single_code_from_string, + sanitize_code, + extract_first_agent_function, + parse_screenshot_analysis, +) +from ..grounding import Grounding +from ..new_global_state import NewGlobalState + +logger = logging.getLogger(__name__) + + +class Operator: + """Operator role: generate next UI action and ground it to screen coordinates. + + Responsibilities: + - Query the LLM action generator to propose the next grounded action (code snippet) + - Parse the proposed action and extract intent + arguments + - If necessary, run visual grounding to obtain precise coordinates + - Return a normalized action dict compatible with `Action.py` for hardware execution + + """ + + def __init__( + self, + tools_dict: Dict[str, Any], + global_state: NewGlobalState, + platform: str = "Windows", + enable_search: bool = False, + screen_size: List[int] = [1920, 1080], + client_password: str = "osworld-public-evaluation", + ) -> None: + self.tools_dict = tools_dict + self.global_state = global_state + self.platform = platform + self.enable_search = enable_search + self.screen_size = screen_size + self.client_password = client_password + + # Embedding engine for Memory + self.embedding_engine = NewTools() + self.embedding_engine.register_tool( + "embedding", + self.tools_dict["embedding"]["provider"], + self.tools_dict["embedding"]["model"], + ) + + # LLM for action generation + self.operator_agent_name = "operator_role" + tool_params = {} + action_gen_cfg = self.tools_dict.get("action_generator", {}) + if self.enable_search: + tool_params["enable_search"] = action_gen_cfg.get("enable_search", True) + tool_params["search_provider"] = action_gen_cfg.get("search_provider", "bocha") + tool_params["search_model"] = action_gen_cfg.get("search_model", "") + else: + tool_params["enable_search"] = False + self.operator_agent = NewTools() + self.operator_agent.register_tool( + self.operator_agent_name, + self.tools_dict[self.operator_agent_name]["provider"], + self.tools_dict[self.operator_agent_name]["model"], + **tool_params + ) + + # Visual grounding + self.grounding_agent = Grounding( + Tools_dict=self.tools_dict, + platform=self.platform, + global_state=self.global_state, + width=self.screen_size[0], + height=self.screen_size[1] + ) + + def _extract_candidate_action(self, text: str) -> Optional[Dict[str, Any]]: + """Extract CandidateAction JSON from LLM output if provided.""" + import re, json + try: + m = re.search(r"CandidateAction\s*:\s*(\{.*?\})", text, re.DOTALL) + if m: + return json.loads(m.group(1)) + m2 = re.search(r"```json\s*(\{.*?\})\s*```", text, re.DOTALL) + if m2: + candidate = json.loads(m2.group(1)) + if isinstance(candidate, dict): + return candidate + except Exception: + pass + return None + + def _get_command_history_for_subtask(self, subtask_id: str) -> str: + """Get command history for specified subtask, formatted as readable text""" + try: + commands = list(reversed(self.global_state.get_commands_for_subtask(subtask_id))) + if not commands: + return "No historical operation records" + + history_lines = [] + history_lines.append("=== Historical Operation Records ===") + + for i, cmd in enumerate(commands, 1): + # Format each command's information + action_type = "Unknown operation" + action_desc = "" + + if isinstance(cmd.action, dict): + action_type = cmd.action["type"] + action_desc = str(cmd.action) + elif isinstance(cmd.action, list): + action_type = "Code generation" + if cmd.action: + descs = [] + for idx, (lang, code) in enumerate(cmd.action, 1): + code_str = str(code) + descs.append(f"[{idx}] Language: {lang}, Code length: {len(code_str)} Code{code_str}") + action_desc = " | ".join(descs) + + # Add command status information + status = cmd.worker_decision + message = cmd.message if cmd.message else "" + exec_status = getattr(cmd, "exec_status", "") + exec_message = getattr(cmd, "exec_message", "") + created_at = getattr(cmd, "created_at", "") + + history_lines.append(f"{i}. [{action_type}] - Status: {status}") + if action_desc: + history_lines.append(f" Action details: {action_desc}") + if message: + history_lines.append(f" Message: {message}") + if exec_status: + history_lines.append(f" Execution status: {exec_status}") + if exec_message: + history_lines.append(f" Execution message: {exec_message}") + if cmd.pre_screenshot_analysis: + history_lines.append(f" Pre-execution screenshot analysis: {cmd.pre_screenshot_analysis}") + if created_at: + history_lines.append(f" Created at: {created_at}") + history_lines.append("") + + return "\n".join(history_lines) + except Exception as e: + logger.warning(f"Failed to get command history: {e}") + return "Failed to get historical records" + + def _get_previous_subtasks_command_history(self, current_subtask_id: str, limit: Optional[int] = None) -> str: + """Aggregate command histories of all previous subtasks (excluding current). + + Args: + current_subtask_id: ID of the current subtask to exclude + limit: Optional maximum number of previous subtasks to include (most recent first) + """ + try: + task = self.global_state.get_task() + all_subtasks = {s.subtask_id: s for s in self.global_state.get_subtasks()} + history_ids = list(getattr(task, 'history_subtask_ids', []) or []) + # Keep order from oldest to newest, then exclude current and optionally limit from the end (most recent) + previous_ids = [sid for sid in history_ids if sid != current_subtask_id and sid in all_subtasks] + if limit is not None and limit > 0: + previous_ids = previous_ids[-limit:] + if not previous_ids: + # Fallback: include any other subtasks except current, ordered by updated_at if available + others = [s for sid, s in all_subtasks.items() if sid != current_subtask_id] + if not others: + return "" + try: + others.sort(key=lambda x: getattr(x, 'updated_at', ''), reverse=True) + except Exception: + pass + previous_ids = [s.subtask_id for s in others] + if limit is not None and limit > 0: + previous_ids = previous_ids[:limit] + + lines: List[str] = [] + if not previous_ids: + return "" + + for idx, sid in enumerate(previous_ids, 1): + subtask = all_subtasks.get(sid) + title = getattr(subtask, 'title', '') if subtask else '' + lines.append(f"--- Subtask {idx} ---") + if title: + lines.append(f"Title: {title}") + commands = list(reversed(self.global_state.get_commands_for_subtask(sid))) + if not commands: + lines.append("No operation command records") + lines.append("") + continue + for i, cmd in enumerate(commands, 1): + action_type = "Unknown operation" + action_desc = "" + if isinstance(cmd.action, dict): + if "type" in cmd.action: + action_type = cmd.action["type"] + if "message" in cmd.action: + action_desc = cmd.action["message"] + elif "element_description" in cmd.action: + action_desc = f"Operate element: {cmd.action['element_description']}" + elif "text" in cmd.action: + action_desc = f"Input text: {cmd.action['text']}" + elif "keys" in cmd.action: + action_desc = f"Keys: {cmd.action['keys']}" + status = getattr(cmd, 'worker_decision', '') + message = getattr(cmd, 'message', '') or '' + exec_status = getattr(cmd, 'exec_status', '') + exec_message = getattr(cmd, 'exec_message', '') + pre_screenshot_analysis = getattr(cmd, 'pre_screenshot_analysis', '') + created_at = getattr(cmd, 'created_at', '') + lines.append(f"{i}. [{action_type}] - Status: {status}") + if action_desc: + lines.append(f" Description: {action_desc}") + if message: + lines.append(f" Message: {message}") + if exec_status: + lines.append(f" Execution status: {exec_status}") + if exec_message: + lines.append(f" Execution message: {exec_message}") + if pre_screenshot_analysis: + lines.append(f" Pre-execution screenshot analysis: {pre_screenshot_analysis}") + if created_at: + lines.append(f" Created at: {created_at}") + lines.append("") + return "\n".join(lines) + except Exception as e: + logger.warning(f"Failed to get previous subtasks history: {e}") + return "" + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + def _should_inject_spreadsheet_protocol(self, *texts: str) -> bool: + """Detect if current context is about spreadsheets/tables/cell ranges. + + This aims to reduce misaligned inputs (e.g., F10:F23 vs F11:F24) by injecting zoom-first guidance. + """ + try: + keywords = [ + "A1", "B2", "C3", "F10", "F11", "F23", ":F", "F10:F23", "F11:F24", "cell", "range", "sheet", "Sheet", "Excel", "Calc", "工作表", "工作簿" + ] + haystack = "\n".join([t for t in texts if isinstance(t, str)]).lower() + return any(k.lower() in haystack for k in keywords) + except Exception: + return False + + def _should_inject_vscode_protocol(self, *texts: str) -> bool: + """Detect if current context is about VS Code operations, especially settings. + + This aims to ensure proper command palette usage with ">" symbol for settings. + """ + try: + keywords = [ + "vs code", "vscode", "visual studio code", "code editor", "settings", "preferences", + "command palette", "ctrl+shift+p", "__pycache__", "files.exclude", "explorer view", + "extension", "theme", "configuration", "workspace" + ] + haystack = "\n".join([t for t in texts if isinstance(t, str)]).lower() + return any(k.lower() in haystack for k in keywords) + except Exception: + return False + + def generate_next_action( + self, + subtask: Dict[str, Any], + guidance: Optional[str] = None, + trigger_code: str = "", + ) -> Dict[str, Any]: + """Generate and ground the next action for the given subtask. + + Args: + subtask: Subtask information + guidance: Optional guidance for the task + trigger_code: Current trigger code to adjust behavior + + Returns a dict containing: + - plan: raw LLM output text + - action: JSON action dict (if ok) + - step_result: StepResult as dict + - outcome: one of {"generate_action", "CANNOT_EXECUTE", "STALE_PROGRESS"} + """ + task = self.global_state.get_task() + screenshot_bytes = self.global_state.get_screenshot() + if not screenshot_bytes: + # Without screenshot, we cannot ground; signal stale + msg = "No screenshot available for action generation" + logger.warning(msg) + self.global_state.log_operation("worker", "no_screenshot", {"error": msg}) + result = { + "step_id": f"{subtask.get('subtask_id','unknown')}.step-0", + "ok": False, + "error": msg, + "latency_ms": 0, + "outcome": "STALE_PROGRESS", + } + return { + "plan": "", + "action": None, + "step_result": result, + "outcome": "STALE_PROGRESS", + } + + # Get command history + subtask_id = subtask.get("subtask_id", "") + command_history = self._get_command_history_for_subtask(subtask_id) + previous_history = self._get_previous_subtasks_command_history(subtask_id) + + # Read artifacts and supplement as context + artifacts_content = "" + supplement_content = "" + try: + artifacts_content = self.global_state.get_artifacts() or "" + except Exception as e: + logger.warning(f"Failed to get artifacts: {e}") + try: + supplement_content = self.global_state.get_supplement() or "" + except Exception as e: + logger.warning(f"Failed to get supplement: {e}") + + # Adjust prompt based on trigger_code + context_aware_prompt = self._build_context_aware_prompt( + subtask, + task, + guidance, + command_history, + previous_history, + trigger_code, + artifacts_content, + supplement_content, + ) + + # Call action generator + t0 = time.time() + action_plan, total_tokens, cost_string = self.operator_agent.execute_tool( + self.operator_agent_name, + {"str_input": context_aware_prompt, "img_input": screenshot_bytes}, + ) + latency_ms = int((time.time() - t0) * 1000) + self.global_state.log_llm_operation( + "worker", + "action_plan_generated", + { + "tokens": total_tokens, + "cost": cost_string, + "duration": latency_ms / 1000.0, + "llm_output": action_plan + }, + str_input=context_aware_prompt, + ) + + # Parse screenshot analysis and action code + screenshot_analysis = parse_screenshot_analysis(action_plan) + self.global_state.add_event("worker", "screenshot_analysis_parsed", f"length={len(screenshot_analysis)}") + + try: + current_width, current_height = self.global_state.get_screen_size() + self.grounding_agent.reset_screen_size(current_width, current_height) + self.grounding_agent.assign_coordinates(action_plan, self.global_state.get_obs_for_grounding()) + + action_code = parse_single_code_from_string(action_plan.split("Grounded Action")[-1]) + action_code = sanitize_code(action_code) + self.global_state.log_operation("worker", "generated_action_code", {"action_code": action_code}) + except Exception as e: + err = f"PARSE_ACTION_FAILED: {e}" + logger.warning(err) + self.global_state.log_operation("worker", "parse_action_failed", {"error": err}) + result = { + "step_id": f"{subtask.get('subtask_id','unknown')}.step-1", + "ok": False, + "error": err, + "latency_ms": latency_ms, + "outcome": "CANNOT_EXECUTE", + } + return { + "action_plan": action_plan, + "action": None, + "step_result": result, + "outcome": "CANNOT_EXECUTE", + } + + # Convert code into a normalized action dict + agent: Grounding = self.grounding_agent + try: + plan_code = extract_first_agent_function(action_code) + exec_code = eval(plan_code) # type: ignore + self.global_state.log_operation("worker", "generated_exec_code", {"exec_code": str(exec_code)}) + ok = True + # Determine outcome based on action type + action_type = "" + message = "" + if isinstance(exec_code, dict): + action_type = str(exec_code.get("type", "")) + message = str(exec_code.get("message", "")) + if action_type == "Memorize": + if "information" not in exec_code: + if message: + exec_code["information"] = message + else: + exec_code["information"] = "Information memorized" + outcome = "worker_generate_action" + elif action_type == "Done": + outcome = "worker_done" + elif action_type == "Failed": + outcome = "worker_fail" + elif action_type == "Supplement": + outcome = "worker_supplement" + elif action_type == "NeedQualityCheck": + # For stale, try to extract CandidateAction JSON from action_plan + candidate = self._extract_candidate_action(action_plan) + exec_code = candidate if isinstance(candidate, dict) else {} + outcome = "worker_stale_progress" + else: + outcome = "worker_generate_action" + else: + outcome = "worker_generate_action" + + err = None + except Exception as e: + ok = False + outcome = "CANNOT_EXECUTE" + exec_code = None + err = f"BUILD_ACTION_FAILED: {e}" + message = "" + logger.warning(err) + + result = { + "step_id": f"{subtask.get('subtask_id','unknown')}.step-1", + "ok": ok, + "error": err, + "latency_ms": latency_ms, + "outcome": outcome, + "action": exec_code, + } + + # Log + self.global_state.add_event( + "worker", + "action_ready" if ok else "action_failed", + f"outcome={outcome}", + ) + return { + "action_plan": action_plan, + "action": exec_code, + "step_result": result, + "outcome": outcome, + "screenshot_analysis": screenshot_analysis, + "message": message, + } + + def _build_context_aware_prompt( + self, + subtask: Dict[str, Any], + task: Any, + guidance: Optional[str], + command_history: str, + previous_subtasks_history: str, + trigger_code: str, + artifacts_content: str, + supplement_content: str + ) -> str: + """Build context-aware prompt based on trigger_code and inject artifacts and supplement information""" + message = [] + # Format task information + if task: + message.extend([ + f"**Task Objective**: {task.objective}", + ]) + else: + message.append("**Task Information**: Not available") + + subtask_title = subtask.get("title", "") + subtask_desc = subtask.get("description", "") + + def _truncate(text: str, limit: int = 3000) -> str: + if not text: + return "" + if len(text) <= limit: + return text + return text[:limit] + "\n... (content too long, truncated)" + + # Add task objective alignment check + message.append("") + message.append("=== CRITICAL: Task Objective Alignment Check ===") + message.append("Before executing any action, carefully review whether the current subtask description conflicts with the main Task Objective.") + message.append("If there is any conflict or contradiction:") + message.append("- The Task Objective takes absolute priority") + message.append("- Adapt your approach to align with the Task Objective") + message.append("") + + message.append(f"The current subtask is: {subtask_title}") + message.append(f"You can use this extra information for completing the current subtask: {subtask_desc}") + if guidance: + message.append(f"GUIDANCE: {guidance}") + + # System context information + message.append("") + message.append("=== System Environment ===") + system_context = [ + f"- Linux username: \"user\"", + f"- [CLIENT_PASSWORD]: {self.client_password}", + f"- Platform: {self.platform}", + ] + message.append("\n".join(system_context)) + + # Add specific context information based on trigger_code + context_info = self._get_context_info_by_trigger_code(trigger_code) + if context_info: + message.append("") + message.append("=== Current Context Information ===") + message.append(context_info) + + + # Inject VS Code command palette protocol if relevant + if self._should_inject_vscode_protocol(subtask_title, subtask_desc, artifacts_content, supplement_content): + message.append("") + message.append("=== VS Code Command Palette Protocol ===") + message.append("- When using Ctrl+Shift+P to access VS Code command palette, ALWAYS ensure the \">\" symbol is present before typing setting names.") + message.append("- If the \">\" symbol is missing or deleted, type \">\" first before entering the setting name.") + message.append("- Examples: \">Preferences: Open Settings\", \">Files: Exclude\", \">Extensions: Install Extensions\".") + message.append("- The \">\" symbol is essential for accessing settings and preferences in VS Code command palette.") + message.append("- Do NOT proceed with typing setting names if the \">\" symbol is absent - add it first.") + message.append("") + message.append("=== VS Code Settings File Distinction ===") + message.append("- VS Code has TWO types of settings files:") + message.append(" * Default Settings (defaultSettings.json) - READ-ONLY system settings") + message.append(" Access: \">Preferences: Open Default Settings (JSON)\" - CANNOT be modified") + message.append(" * User Settings (settings.json) - EDITABLE user configuration") + message.append(" Access: \">Preferences: Open User Settings (JSON)\" - CAN be modified") + message.append("- When tasks require MODIFYING VS Code settings, ALWAYS use User Settings.") + message.append("- NEVER attempt to edit Default Settings - they are read-only and changes will fail.") + message.append("") + message.append("=== VS Code File Exclusion Format (MANDATORY) ===") + message.append("- When configuring file exclusion patterns in VS Code settings (e.g., files.exclude), use the format WITHOUT trailing slash.") + message.append("- This ensures exact matching with expected validation criteria and prevents comparison failures.") + message.append("") + message.append("=== VS Code Settings JSON Validation (CRITICAL) ===") + message.append("- After editing VS Code settings.json, ALWAYS verify the JSON format is valid:") + message.append(" * Ensure proper JSON structure with matching braces: {...}") + message.append(" * Use consistent indentation (2 or 4 spaces)") + message.append(" * No duplicate opening/closing braces") + message.append(" * Valid JSON syntax with proper comma placement") + message.append("- If JSON is malformed, fix it immediately before proceeding.") + message.append("- Invalid JSON will cause VS Code settings to fail and may corrupt the configuration.") + + # Inject available artifacts content + message.append("") + message.append("=== Available Artifacts Content (from memory records) ===") + if artifacts_content and artifacts_content.strip(): + # message.append(_truncate(artifacts_content)) + message.append(artifacts_content) # No truncation for now, memory optimization can be added here later + else: + message.append("No available artifacts content") + + # Inject supplement materials + message.append("") + message.append("=== Supplement Materials (possibly from retrieval/external tools) ===") + if supplement_content and supplement_content.strip(): + # message.append(_truncate(supplement_content)) + message.append(supplement_content) # No truncation for now, memory optimization can be added here later + else: + message.append("No supplement materials") + + # Add previous subtasks operation records + if previous_subtasks_history and previous_subtasks_history.strip(): + message.append("") + message.append("=== Previous Subtasks Operation Records ===") + message.append(previous_subtasks_history) + + # Add historical operation records to prompt - this is very important context information + message.append("") + message.append("=== Historical Operation Records (from current subtask) ===") + message.append(command_history) + + message.append("") + message.append("=== Action Description Guidelines ===") + message.append("- When describing elements to click, be as specific and clear as possible") + message.append("- For color-related elements, include RGB values if visible (e.g., 'red button (RGB: 255,0,0)')") + message.append("- For color adjustments: NEVER click directly on color swatches/blocks - instead find and use color input panels") + message.append("- Look for 'Custom Color', 'More Colors', 'Color Picker', or similar panels where you can input color codes") + message.append("- Use hex color codes: yellow=#FFFF00, gold=#FFBF00, orange=#FF8000, brick=#FF4000, red=#FF0000") + message.append("- More colors: magenta=#BF0041, purple=#800080, indigo=#55308D, blue=#2A6099, teal=#158466, green=#00A933, lime=#81D41A") + message.append("- For text color changes: ALWAYS select the text first (Ctrl+A), then use color input panel - direct clicking on color options won't work") + message.append("- Avoid vague descriptions like 'the button' - use 'the blue Save button below the text input field'") + message.append("") + message.append("=== File Extension Handling ===") + message.append("- When changing file formats in Save/Open dialogs, selecting a supported file type will automatically update the file extension; do NOT retype the filename.") + message.append("- Only when 'All files' or 'All formats' is selected should you manually edit the filename extension.") + message.append("- Prefer keeping the original filename and only change the extension unless the task explicitly requires renaming the base name.") + message.append("") + message.append("=== Terminal Python Execution Rule ===") + message.append("Do NOT use here-doc to run Python in the current opened terminal. If you need to run Python, create a .py file first, then use `python3 your_file.py` to execute it.") + message.append("") + message.append("=== Browser Reuse Guideline ===") + message.append("- Before opening a browser, check whether a browser window/tab is already open. Unless explicitly instructed to open a new browser/page, continue in the existing browser window/tab.") + message.append("- **Smart Tab Usage**: If the current tab is empty (blank page, new tab page, or about:blank), use it directly instead of opening a new tab.") + message.append("- If the browser already has open pages with content, avoid closing them. For searches or opening links/files, prefer opening a new tab unless the task explicitly requires closing pages.") + message.append("- Avoid using Ctrl+O to open files in existing browser tabs, as this replaces the current page. Instead, open a new tab first, then use Ctrl+O.") + message.append("") + message.append("=== CRITICAL: Data Integrity and Accuracy Guidelines ===") + message.append("- NEVER create fake URLs like 'scholar.google.com/citations?user=VjJmJUsAAAAJ' or similar patterns") + message.append("- NEVER generate random user IDs, citation numbers, or profile identifiers") + message.append(" * Do not create placeholder or example data") + message.append(" * If you need to search for someone, use the actual search functionality rather than guessing") + message.append("") + message.append("=== WEB QUERY RETRY STRATEGY ===") + message.append("- When searching for information online, implement systematic retry before concluding failure") + message.append("") + message.append("Based on the above history, current screenshot, artifacts and supplement, decide on the next action.") + message.append("Return exactly one action as an agent.* function in Python fenced code under (Grounded Action).") + message.append("") + + + return "\n\n".join(message) + + def _get_context_info_by_trigger_code(self, trigger_code: str) -> str: + """Return corresponding detailed context information and guidance based on trigger_code""" + from ..enums import TRIGGER_CODE_BY_MODULE + + # Check if it belongs to WORKER_GET_ACTION_CODES + worker_codes = TRIGGER_CODE_BY_MODULE.WORKER_GET_ACTION_CODES + + if trigger_code == worker_codes["subtask_ready"]: + return """ +# New Subtask Ready - Context Information +- This is a new subtask that has just been assigned +- Carefully analyze the current screen state and identify the starting point +- Review the subtask requirements and understand what needs to be accomplished +- Consider the most efficient approach to begin execution +- Look for any UI elements or visual cues that indicate the next action +- Ensure you understand the context before proceeding with any actions +""" + + elif trigger_code == worker_codes["execution_error"]: + return """ +# Previous Execution Error - Context Information +- The previous action execution encountered an error or failure +- Analyze the current screen state to understand what went wrong +- Look for error messages, unexpected UI states, or failed operations +- Consider alternative approaches or methods to achieve the same goal +- Review the error context and adapt your strategy accordingly +- Ensure the new action addresses the specific failure point identified + +# Web Query Retry Strategy (for search/information gathering tasks) +- If this is a web search or information gathering task that failed: + * Do NOT immediately conclude the information is unavailable after just one failed attempt +""" + + elif trigger_code == worker_codes["command_completed"]: + return """ +# Command Successfully Completed - Context Information +- The previous command has been successfully executed +- Check the current screen state to verify the expected outcome +- Look for visual changes, new UI elements, or progress indicators +- Assess whether the current state matches the expected result +- Identify the next logical step based on the current progress +- Continue with the next action in the subtask sequence +""" + + elif trigger_code == worker_codes["no_command"]: + return """ +# No Executable Command Found - Context Information +- No suitable command could be identified for the current situation +- Re-analyze the task requirements and current screen state +- Look for alternative approaches or different UI interaction methods +- Consider whether the task needs to be broken down into smaller steps +- Review the subtask description for any missed requirements +- Generate a more appropriate action based on the current context +""" + + elif trigger_code == worker_codes["quality_check_passed"]: + return """ +# Quality Check Passed - Context Information +- The quality check for the previous action has been successfully completed +- The action met all quality criteria and requirements +- Continue with the next step in the subtask execution +- Look for the next logical action based on the current progress +- Ensure continuity in the task execution flow +- Maintain the same level of quality for subsequent actions +""" + + elif trigger_code == worker_codes["subtask_ready_after_plan"]: + return """ +# Subtask Ready After Replanning - Context Information +- This subtask has been prepared after a replanning process +- The previous plan may have had issues that have been addressed +- Start fresh with the improved understanding and approach +- Pay attention to any specific guidance provided during replanning +- Ensure you follow the updated strategy and requirements +- Look for any changes in the approach or methodology +""" + + elif trigger_code == worker_codes["final_check_pending"]: + return """ +# Final Check Pending - Context Information +- The system is approaching the final verification stage +- Ensure all necessary steps for this subtask have been completed +- Review the current state against the subtask completion criteria +- Look for any missing elements or incomplete actions +- Verify that the current state meets the expected final outcome +- Prepare for the final quality assessment of the entire task +""" + + else: + # Default case + return """ +# General Context Information +- Analyze the current screen state and task requirements +- Consider the most appropriate action based on the current context +- Ensure your action aligns with the subtask objectives +- Look for visual cues and UI elements that guide the next step +- Maintain consistency with the overall task execution strategy +""" \ No newline at end of file diff --git a/mm_agents/maestro/maestro/sub_worker/technician.py b/mm_agents/maestro/maestro/sub_worker/technician.py new file mode 100644 index 0000000..706821c --- /dev/null +++ b/mm_agents/maestro/maestro/sub_worker/technician.py @@ -0,0 +1,640 @@ +""" +Technician Module for GUI-Agent Architecture (agents3) +- Provides system-level operations via terminal commands +- Generates and executes bash/python scripts +- Returns execution results and status +""" + +from __future__ import annotations + +import re +import time +import logging +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Tuple, Union +from desktop_env.desktop_env import DesktopEnv + +from ...tools.new_tools import NewTools +from ..new_global_state import NewGlobalState +from ..enums import WorkerDecision +from ...utils.common_utils import parse_technician_screenshot_analysis + +logger = logging.getLogger(__name__) + + +@dataclass +class StepResult: + """Lightweight step result for controller/evaluator handoff.""" + step_id: str + ok: bool + error: Optional[str] + latency_ms: int + outcome: str + action: Optional[Dict[str, Any]] = None + + +class Technician: + """Technician role: execute system-level operations via terminal commands. + + Responsibilities: + - Generate bash/python scripts to complete system tasks + - Execute scripts via environment controller + - Return execution results and status + + Tools_dict requirements: + - coding_agent: {"provider": str, "model": str} - LLM for code generation + """ + + def __init__( + self, + *, + tools_dict: Dict[str, Any], + global_state: NewGlobalState, + platform: str = "Ubuntu", + client_password: str = "osworld-public-evaluation", + # max_execution_time: int = 300, + ) -> None: + self.tools_dict = tools_dict + self.global_state = global_state + self.platform = platform + self.client_password = client_password + # self.max_execution_time = max_execution_time + + # LLM for code generation + self.technician_agent_name = "technician_role" + self.technician_agent = NewTools() + self.technician_agent.register_tool( + self.technician_agent_name, + self.tools_dict[self.technician_agent_name]["provider"], + self.tools_dict[self.technician_agent_name]["model"], + ) + + def _get_command_history_for_subtask(self, subtask_id: str) -> str: + """Get command history for specified subtask, formatted as readable text""" + try: + commands = list(reversed(self.global_state.get_commands_for_subtask(subtask_id))) + if not commands: + return "No historical operation records" + + history_lines = [] + history_lines.append("=== Historical Operation Records ===") + + for i, cmd in enumerate(commands, 1): + # Format each command's information + action_type = "Unknown operation" + action_desc = "" + + if isinstance(cmd.action, dict): + action_type = cmd.action["type"] + action_desc = str(cmd.action) + elif isinstance(cmd.action, list): + action_type = "Code generation" + if cmd.action: + descs = [] + for idx, (lang, code) in enumerate(cmd.action, 1): + code_str = str(code) + descs.append(f"[{idx}] Language: {lang}, Code length: {len(code_str)} Code{code_str}") + action_desc = " | ".join(descs) + + # Add command status information + status = cmd.worker_decision + message = cmd.message if cmd.message else "" + exec_status = getattr(cmd, "exec_status", "") + exec_message = getattr(cmd, "exec_message", "") + + history_lines.append(f"{i}. [{action_type}] - Status: {status}") + if action_desc: + history_lines.append(f" Description: {action_desc}") + if message: + history_lines.append(f" Message: {message}") + if exec_status: + history_lines.append(f" Execution status: {exec_status}") + if exec_message: + history_lines.append(f" Execution message: {exec_message}") + if cmd.pre_screenshot_analysis: + history_lines.append(f" Pre-execution screenshot analysis: {cmd.pre_screenshot_analysis}") + history_lines.append("") + + return "\n".join(history_lines) + except Exception as e: + logger.warning(f"Failed to get command history: {e}") + return "Failed to get historical records" + + def execute_task( + self, + *, + subtask: Dict[str, Any], + guidance: Optional[str] = None, + trigger_code: str = "", + ) -> Dict[str, Any]: + """Execute a system-level task using terminal commands. + + Args: + subtask: Subtask information + guidance: Optional guidance for the task + trigger_code: Current trigger code to adjust behavior + + Returns a dict containing: + - plan: generated code/script + - execution_result: output from script execution + - step_result: StepResult as dict + - outcome: one of {"worker_done", "worker_fail", "worker_supplement", "worker_stale_progress", "worker_generate_action", "worker_fail"} + - action: when outcome is worker_generate_action, a list of (lang, code) blocks + """ + + # Get command history + subtask_id = subtask.get("subtask_id", "") + command_history = self._get_command_history_for_subtask(subtask_id) + task = self.global_state.get_task() + + # Check if this is a file modification task and enforce inspection-first workflow + file_modification_guidance = self._check_file_modification_workflow(subtask, command_history) + + # Build context-aware prompt based on trigger_code + context_aware_prompt = self._build_context_aware_prompt( + subtask, task, guidance, command_history, trigger_code, file_modification_guidance + ) + + # Get screenshot for context + # screenshot_bytes = self.global_state.get_screenshot() + + # Call coding agent + t0 = time.time() + try: + command_plan, total_tokens, cost_string = self.technician_agent.execute_tool( + self.technician_agent_name, + # {"str_input": context_aware_prompt, "img_input": screenshot_bytes}, + {"str_input": context_aware_prompt}, + ) + latency_ms = int((time.time() - t0) * 1000) + + self.global_state.log_llm_operation( + "technician", + "code_generated", + { + "tokens": total_tokens, + "cost": cost_string, + "duration": latency_ms / 1000.0, + "llm_output": command_plan + }, + str_input=context_aware_prompt, + ) + except Exception as e: + err = f"CODE_GENERATION_FAILED: {e}" + logger.warning(err) + self.global_state.add_event("technician", "code_generation_failed", err) + result = StepResult( + step_id=f"{subtask.get('subtask_id','unknown')}.tech-1", + ok=False, + error=err, + latency_ms=int((time.time() - t0) * 1000), + outcome=WorkerDecision.CANNOT_EXECUTE.value, + ) + return { + "plan": "", + "execution_result": "", + "step_result": result.__dict__, + "outcome": WorkerDecision.CANNOT_EXECUTE.value, + "action": None, + } + + # # Parse screenshot analysis and extract/classify + # screenshot_analysis = parse_technician_screenshot_analysis(command_plan) + # self.global_state.add_event("technician", "screenshot_analysis_parsed", f"length={len(screenshot_analysis)}") + + try: + decision = self._infer_decision_from_text(command_plan) + # code_blocks: List[Tuple[str, str]] = [] + code_blocks = self._extract_code_blocks(command_plan) + decision_message = "" + + # Only try to extract code blocks when no explicit decision is detected + # if decision is None: + # code_blocks = self._extract_code_blocks(command_plan) + + if decision is None and code_blocks: + ok = True + outcome = WorkerDecision.GENERATE_ACTION.value + err = None + action = code_blocks + message = "" + elif decision == "done": + ok = True + outcome = WorkerDecision.WORKER_DONE.value + err = None + decision_message = self._extract_decision_message(command_plan, decision) + action = {"type": "Done", "message": decision_message} + message = decision_message + elif decision == "failed": + ok = False + outcome = WorkerDecision.CANNOT_EXECUTE.value + err = None + decision_message = self._extract_decision_message(command_plan, decision) + action = {"type": "Failed", "message": decision_message} + message = decision_message + elif decision == "supplement": + ok = False + outcome = WorkerDecision.SUPPLEMENT.value + err = None + decision_message = self._extract_decision_message(command_plan, decision) + action = {"type": "Supplement", "message": decision_message} + message = decision_message + elif decision == "need_quality_check": + ok = True + outcome = WorkerDecision.STALE_PROGRESS.value + err = None + decision_message = self._extract_decision_message(command_plan, decision) + # Prefer CandidateAction JSON over placeholder + candidate = self._extract_candidate_action(command_plan) + action = candidate + message = decision_message + else: + # No clear signal; treat as cannot execute + ok = False + outcome = WorkerDecision.CANNOT_EXECUTE.value + err = "No code blocks or valid decision found" + action = None + message = "" + except Exception as e: + ok = False + outcome = WorkerDecision.CANNOT_EXECUTE.value + code_blocks = [] + err = f"CLASSIFICATION_FAILED: {e}" + action = None + message = "" + logger.warning(err) + + result = StepResult( + step_id=f"{subtask.get('subtask_id','unknown')}.tech-1", + ok=ok, + error=err, + latency_ms=latency_ms, + outcome=outcome, + action=action, # type: ignore + ) + + # Log execution result + self.global_state.add_event( + "technician", + "task_executed" if ok else "task_failed", + f"outcome={outcome}", + ) + + return { + "command_plan": command_plan, + "action": action, + "step_result": result.__dict__, + "outcome": outcome, + # "screenshot_analysis": screenshot_analysis, + "message": message, + } + + def _extract_code_blocks(self, text: str) -> List[Tuple[str, str]]: + """Extract code blocks from markdown-style text. + + Handles both literal newlines (\n) and escaped newlines (\\n) for compatibility + with different LLM output formats. + """ + import re + + # Normalize newlines: convert escaped newlines to literal newlines + # This handles cases where LLM outputs \\n instead of \n + text = text.replace('\\n', '\n') + + # Pattern to match ```language\ncode\n``` + pattern = r'```(\w+)\n(.*?)\n```' + matches = re.findall(pattern, text, re.DOTALL) + + code_blocks = [] + for lang, code in matches: + lang = lang.lower() + code = code.strip() + if code: + code_blocks.append((lang, code)) + + return code_blocks + + def _infer_decision_from_text(self, text: str) -> Optional[str]: + """Infer high-level decision from free-form LLM text. + Returns one of: "done", "failed", "supplement", "need_quality_check", or None. + """ + # First try to find structured decision format + if "DECISION_START" in text and "DECISION_END" in text: + # Extract content between markers + start_marker = "DECISION_START" + end_marker = "DECISION_END" + start_pos = text.find(start_marker) + len(start_marker) + end_pos = text.find(end_marker) + + if start_pos < end_pos: + decision_content = text[start_pos:end_pos].strip() + # Look for Decision: line + import re + decision_match = re.search(r"Decision:\s*(DONE|FAILED|SUPPLEMENT|NEED_QUALITY_CHECK)", decision_content, re.IGNORECASE) + if decision_match: + decision = decision_match.group(1).lower() + if decision == "need_quality_check": + return "need_quality_check" + return decision + + def _extract_decision_message(self, text: str, decision: str) -> str: + """Extract the detailed message associated with a decision. + Returns the message explaining the decision reason and context. + """ + import re + + # First try to extract from structured format + if "DECISION_START" in text and "DECISION_END" in text: + start_marker = "DECISION_START" + end_marker = "DECISION_END" + start_pos = text.find(start_marker) + len(start_marker) + end_pos = text.find(end_marker) + + if start_pos < end_pos: + decision_content = text[start_pos:end_pos].strip() + # Look for Message: line + message_match = re.search(r"Message:\s*(.+)", decision_content, re.IGNORECASE | re.DOTALL) + if message_match: + message = message_match.group(1).strip() + # Clean up the message (remove extra whitespace and newlines) + message = re.sub(r'\s+', ' ', message) + if message: + return message + + return f"Decision {decision} was made" + + def _check_file_modification_workflow(self, subtask: Dict[str, Any], command_history: str) -> str: + """Check if this is a file modification task and determine workflow guidance.""" + subtask_desc = subtask.get("description", "").lower() + subtask_title = subtask.get("title", "").lower() + + # Keywords that indicate file modification + file_mod_keywords = [ + "edit", "modify", "change", "update", "configure", "set", + "write to", "save to", "_config", ".yaml", ".json", ".txt", + ".conf", "config file", "configuration" + ] + + is_file_modification = any(keyword in subtask_desc or keyword in subtask_title + for keyword in file_mod_keywords) + + if not is_file_modification: + return "" + + # Check if we already have inspection commands in history + has_inspection = any( + cmd in command_history.lower() + for cmd in ["cat ", "head ", "tail ", "grep ", "less ", "more "] + ) + + if has_inspection: + return """ +**FILE MODIFICATION - STEP 2 CONTEXT:** +- Previous inspection commands were executed +- You can now proceed with the actual file modifications +- Base your changes on the inspection results from command history +""" + else: + return """ +**FILE MODIFICATION - STEP 1 REQUIRED:** +- This task involves file modification +- You MUST start with inspection commands ONLY (cat, head, grep, less, etc.) +- DO NOT perform any modifications in this first script +- After inspection, the system will call you again for the actual modifications +""" + + def _build_context_aware_prompt( + self, + subtask: Dict[str, Any], + task: Any, + guidance: Optional[str], + command_history: str, + trigger_code: str, + file_modification_guidance: str = "" + ) -> str: + task_message = [] + if task: + task_message.extend([ + f"**Task Objective**: {task.objective}", + ]) + else: + task_message.append("**Task Information**: Not available") + + subtask_title = subtask.get("title", "") + subtask_desc = subtask.get("description", "") + + # System context information + system_context = [ + "# System Environment", + f"- Linux username: \"user\"", + f"- Client password: {self.client_password}", + f"- Platform: {self.platform}", + f"- Starting directory: Same base directory for each new script execution", + "" + ] + + # Task objective alignment check + alignment_check = [ + "# CRITICAL: Task Objective Alignment Check", + "Before writing any script or making any decision, carefully review whether the current subtask description conflicts with the main Task Objective.", + "If there is any conflict or contradiction:", + "- The Task Objective takes absolute priority over subtask description", + "- Adapt your script/approach to align with the Task Objective", + "- Never execute scripts that would contradict or undermine the main Task Objective", + "" + ] + + # Task information + task_info = [ + "# Current Task", + f"**Title**: {subtask_title}", + f"**Description**: {subtask_desc}", + ] + + if guidance: + task_info.append(f"**Guidance**: {guidance}") + + # Add file modification guidance if applicable + if file_modification_guidance: + task_info.extend([ + "# File Modification Workflow Guidance", + file_modification_guidance, + "" + ]) + + # Add context-specific guidance based on trigger_code + context_guidance = self._get_context_info_by_trigger_code(trigger_code) + if context_guidance: + task_info.extend([ + "# Context-Specific Guidance", + context_guidance, + "" + ]) + + # Previous operations history + history_section = [ + "# Previous Operations History", + command_history, + "" + ] + + # Instructions for script generation + instructions = [ + "# Instructions", + "Based on the task description, previous history, and current context:", + "", + "**CRITICAL FILE OPERATION RULE**: If this task involves modifying any files, you MUST follow this two-step process:", + "- STEP 1: First execution must ONLY examine/inspect the target files (use cat, head, grep, less, etc.)", + "- STEP 2: Second execution performs the actual modifications based on inspection results", + "- NEVER combine inspection and modification in a single script", + "", + "1. **If you need to write a script**, provide exactly ONE complete code block:", + " - Use ```bash for bash scripts or ```python for python code", + " - For file modifications: FIRST script must only inspect, SECOND script modifies", + " - Include verification steps and progress reporting within the script", + " - Handle directory navigation explicitly or use absolute paths", + " - Include error checking and informative output messages", + "", + "2. **If you cannot proceed or task is complete**, use the decision format:", + " ```", + " DECISION_START", + " Decision: [DONE|FAILED|SUPPLEMENT|NEED_QUALITY_CHECK]", + " Message: [Your detailed explanation]", + " DECISION_END", + " ```", + "", + "3. **Important reminders**:", + " - Each script runs in a fresh terminal session", + " - You cannot see GUI changes - rely on command output for verification", + " - Use `echo osworld-public-evaluation | sudo -S [command]` format for sudo operations", + " - Include progress indicators and result verification in your scripts", + "", + "# Color Gradient Arrangement (CCT) Guidance", + "- If the task requires warm/cool gradient, compute Correlated Color Temperature (CCT) per segment (e.g., via CIE xy/XYZ and McCamy approximation).", + "- Avoid naive metrics like average red as the primary ordering signal. Do not recolor; only reorder unless color edits are explicitly part of the objective.", + "" + ] + + # Combine all sections + full_prompt = "\n".join( + system_context + + alignment_check + + task_message + + task_info + + history_section + + instructions + ) + + return full_prompt + + def _get_context_info_by_trigger_code(self, trigger_code: str) -> str: + """Build context-aware prompt based on trigger_code""" + from ..enums import TRIGGER_CODE_BY_MODULE + + # Check if it belongs to WORKER_GET_ACTION_CODES + worker_codes = TRIGGER_CODE_BY_MODULE.WORKER_GET_ACTION_CODES + + if trigger_code == worker_codes["subtask_ready"]: + return """ +**New System Task Context:** +- This is a fresh system-level task assignment +- Analyze system requirements and identify necessary tools/dependencies +- Plan script execution sequence for optimal efficiency +- Consider system permissions and resource requirements +- Verify system prerequisites before proceeding with main operations +""" + + elif trigger_code == worker_codes["execution_error"]: + return """ +**Error Recovery Context:** +- Previous script execution encountered an error or failure +- Analyze error patterns from command history to understand root cause +- Check for: permission issues, missing dependencies, resource constraints +- Implement alternative approach or error mitigation strategy +- Include additional error handling and verification in new script +- Consider breaking complex operations into smaller, safer steps +""" + + elif trigger_code == worker_codes["command_completed"]: + return """ +**Continuation Context:** +- Previous script executed successfully +- Verify expected system changes occurred as planned +- Check system state, file modifications, or process status +- Determine next logical step in the task sequence +- Build upon previous success while maintaining system integrity +- Continue with appropriate next operation based on current system state +""" + + elif trigger_code == worker_codes["no_command"]: + return """ +**Command Generation Context:** +- No suitable system command was previously identified +- Re-analyze task requirements with fresh perspective +- Look for alternative tools, approaches, or execution methods +- Consider task decomposition into simpler system operations +- Review system constraints and available resources +- Generate clear, executable system command based on current understanding +""" + + elif trigger_code == worker_codes["quality_check_passed"]: + return """ +**Quality Validated Context:** +- Previous system operation passed quality verification +- System state meets required standards and criteria +- Proceed confidently to next step in task execution +- Maintain same quality standards for subsequent operations +- Continue task flow with validated foundation +- Focus on next logical system operation in the sequence +""" + + elif trigger_code == worker_codes["subtask_ready_after_plan"]: + return """ +**Post-Replanning Context:** +- Task has been refined through replanning process +- Previous approach may have been updated or improved +- Apply new understanding and refined strategy +- Follow updated guidance and system requirements +- Start with fresh approach based on improved planning +- Consider any new constraints or methodology changes +""" + + elif trigger_code == worker_codes["final_check_pending"]: + return """ +**Final Verification Context:** +- System task is approaching completion stage +- Ensure all required system operations are complete +- Verify system state matches expected final outcome +- Complete any remaining system changes or cleanup +- Prepare system for final quality assessment +- Focus on completion criteria and system validation +""" + + else: + return """ +**General System Context:** +- Analyze current system state and task requirements +- Choose appropriate system tools and commands for the situation +- Ensure script operations align with task objectives and safety requirements +- Consider system security, resource usage, and operational impact +- Maintain consistency with overall task execution strategy +""" + + def _extract_candidate_action(self, text: str) -> Optional[Dict[str, Any]]: + """Try to extract a CandidateAction JSON block from the LLM output.""" + try: + # Pattern 1: explicit CandidateAction: { ... } + m = re.search(r"CandidateAction\s*:\s*(\{.*?\})", text, re.DOTALL) + if m: + import json + return json.loads(m.group(1)) + + # Pattern 2: fenced json with a top-level object having type/selector + m2 = re.search(r"```json\s*(\{.*?\})\s*```", text, re.DOTALL) + if m2: + import json + candidate = json.loads(m2.group(1)) + return candidate + except Exception as e: + logger.debug(f"No CandidateAction found: {e}") + return None \ No newline at end of file diff --git a/mm_agents/maestro/maestro/utils/screenShot.py b/mm_agents/maestro/maestro/utils/screenShot.py new file mode 100644 index 0000000..690f910 --- /dev/null +++ b/mm_agents/maestro/maestro/utils/screenShot.py @@ -0,0 +1,18 @@ +from PIL import Image + +from ..hardware_interface import HardwareInterface +from ..Backend.PyAutoGUIBackend import PyAutoGUIBackend +import pyautogui + + +# Handle Mac DPI scaling issues +def scale_screenshot_dimensions(screenshot: Image.Image, hwi_para: HardwareInterface): + screenshot_high = screenshot.height + screenshot_width = screenshot.width + if isinstance(hwi_para.backend, PyAutoGUIBackend): + screen_width, screen_height = pyautogui.size() + if screen_width != screenshot_width or screen_height != screenshot_high: + screenshot = screenshot.resize((screen_width, screen_height), Image.Resampling.LANCZOS) + + return screenshot + diff --git a/mm_agents/maestro/osworld_run_maestro.py b/mm_agents/maestro/osworld_run_maestro.py new file mode 100644 index 0000000..eac2834 --- /dev/null +++ b/mm_agents/maestro/osworld_run_maestro.py @@ -0,0 +1,549 @@ +import argparse +import json +import datetime +import io +import logging +import os +import platform +import sys +import time +from tqdm import tqdm +from pathlib import Path +from dotenv import load_dotenv +from gui_agents.maestro.controller.main_controller import MainController +# Import analyze_display functionality +from gui_agents.utils.analyze_display import analyze_display_json, format_output_line +from desktop_env.desktop_env import DesktopEnv +from gui_agents.utils.common_utils import ImageDataFilter, SafeLoggingFilter + +env_path = Path(os.path.dirname(os.path.abspath(__file__))) / '.env' +if env_path.exists(): + load_dotenv(dotenv_path=env_path) +else: + parent_env_path = Path(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) / '.env' + if parent_env_path.exists(): + load_dotenv(dotenv_path=parent_env_path) + +logger = logging.getLogger() +logger.setLevel(logging.DEBUG) + +vm_datetime_str: str = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + +log_dir = "runtime" +vm_log_dir = os.path.join(log_dir, f"vmrun_{vm_datetime_str}") +os.makedirs(vm_log_dir, exist_ok=True) + +file_handler = logging.FileHandler( + os.path.join(vm_log_dir, "vmrun_normal.log"), encoding="utf-8" +) +debug_handler = logging.FileHandler( + os.path.join(vm_log_dir, "vmrun_debug.log"), encoding="utf-8" +) +stdout_handler = logging.StreamHandler(sys.stdout) +sdebug_handler = logging.FileHandler( + os.path.join(vm_log_dir, "vmrun_sdebug.log"), encoding="utf-8" +) + +file_handler.setLevel(logging.INFO) +debug_handler.setLevel(logging.DEBUG) +stdout_handler.setLevel(logging.INFO) +sdebug_handler.setLevel(logging.DEBUG) + +# Add SafeLoggingFilter to prevent format errors from third-party libraries (like OpenAI) +safe_filter = SafeLoggingFilter() +debug_handler.addFilter(safe_filter) +sdebug_handler.addFilter(safe_filter) +file_handler.addFilter(safe_filter) +stdout_handler.addFilter(safe_filter) + +# Also apply SafeLoggingFilter to OpenAI library loggers +try: + import openai + openai_logger = logging.getLogger('openai') + openai_logger.addFilter(safe_filter) + httpx_logger = logging.getLogger('httpx') + httpx_logger.addFilter(safe_filter) +except ImportError: + pass + +if os.getenv('KEEP_IMAGE_LOGS', 'false').lower() != 'true': + image_filter = ImageDataFilter() + debug_handler.addFilter(image_filter) + sdebug_handler.addFilter(image_filter) + logger.info("Image data filtering enabled - image data in debug logs will be filtered") +else: + logger.info("Image data filtering disabled - debug logs will contain complete image data") + +logger.info("Safe logging filter enabled - prevents format errors from third-party libraries (OpenAI, HTTPX)") + +formatter = logging.Formatter( + fmt="\x1b[1;33m[%(asctime)s \x1b[31m%(levelname)s \x1b[32m%(module)s/%(lineno)d-%(processName)s\x1b[1;33m] \x1b[0m%(message)s" +) +file_handler.setFormatter(formatter) +debug_handler.setFormatter(formatter) +stdout_handler.setFormatter(formatter) +sdebug_handler.setFormatter(formatter) + +stdout_handler.addFilter(logging.Filter("desktopenv")) +sdebug_handler.addFilter(logging.Filter("desktopenv")) + +logger.addHandler(file_handler) +logger.addHandler(debug_handler) +logger.addHandler(stdout_handler) +logger.addHandler(sdebug_handler) + +logger = logging.getLogger("desktopenv.experiment") + +def config() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Run end-to-end evaluation on the benchmark" + ) + + current_platform = os.getenv("USE_PRECREATE_VM", "Windows") + if current_platform == "Ubuntu": + path_to_vm = os.path.join("vmware_vm_data", "Ubuntu0", "Ubuntu0.vmx") + test_config_base_dir = os.path.join("evaluation_examples", "examples") + test_all_meta_path = os.path.join("evaluation_examples", "test_tiny.json") + elif current_platform == "Windows": + path_to_vm = os.path.join("vmware_vm_data", "Windows0", "Windows0.vmx") + test_config_base_dir = os.path.join("evaluation_examples", "examples_windows") + test_all_meta_path = os.path.join("evaluation_examples", "test_tiny_windows.json") + else: + raise ValueError(f"USE_PRECREATE_VM={current_platform} is not supported. Please use Ubuntu or Windows.") + + # platform config + parser.add_argument( + "--current_platform", + type=str, + choices=["Ubuntu", "Windows"], + default=current_platform, + help="Platform to run on (Ubuntu or Windows)" + ) + + # environment config + # vm_path will be set based on platform + parser.add_argument("--path_to_vm", type=str, default=path_to_vm) + parser.add_argument( + "--headless", action="store_true", help="Run in headless machine" + ) + parser.add_argument( + "--action_space", type=str, default="pyautogui", help="Action type" + ) + parser.add_argument( + "--observation_type", + choices=["screenshot", "a11y_tree", "screenshot_a11y_tree", "som"], + default="screenshot", + help="Observation type", + ) + parser.add_argument("--max_steps", type=int, default=50) + + # agent config + parser.add_argument( + "--test_config_base_dir", type=str, default=test_config_base_dir + ) + + # example config + parser.add_argument("--domain", type=str, default="all") + parser.add_argument( + "--test_all_meta_path", type=str, default=test_all_meta_path + ) + + # logging related + parser.add_argument("--result_dir", type=str, default="./results") + + args = parser.parse_args() + + return args + + +def test(args: argparse.Namespace, test_all_meta: dict) -> None: + scores = [] + + # log args + logger.info("Args: %s", args) + cfg_args = { + "path_to_vm": args.path_to_vm, + "headless": args.headless, + "action_space": args.action_space, + "observation_type": args.observation_type, + "max_steps": args.max_steps, + "result_dir": args.result_dir, + } + + env = DesktopEnv( + provider_name="vmware", + path_to_vm=args.path_to_vm, + action_space=args.action_space, + headless=args.headless, + require_a11y_tree=False, + ) + + for domain in tqdm(test_all_meta, desc="Domain"): + domain_sanitized = str(domain).strip() + for example_id in tqdm(test_all_meta[domain], desc="Example", leave=False): + example_id_sanitized = str(example_id).strip() + config_file = os.path.join( + args.test_config_base_dir, + domain_sanitized, + f"{example_id_sanitized}.json" + ) + + if not os.path.exists(config_file): + try: + candidate_dir = os.path.join(args.test_config_base_dir, domain_sanitized) + existing_files = [] + if os.path.isdir(candidate_dir): + existing_files = os.listdir(candidate_dir) + logger.error(f"Config file not found: {config_file}") + logger.error(f"Existing files in {candidate_dir}: {existing_files}") + except Exception as e: + logger.error(f"Error while listing directory for debug: {e}") + raise FileNotFoundError(config_file) + + with open(config_file, "r", encoding="utf-8") as f: + example = json.load(f) + + logger.info(f"[Domain]: {domain_sanitized}") + logger.info(f"[Example ID]: {example_id_sanitized}") + + user_query = example["instruction"] + + logger.info(f"[User Query]: {user_query}") + # wandb each example config settings + cfg_args["user_query"] = user_query + cfg_args["start_time"] = datetime.datetime.now().strftime( + "%Y:%m:%d-%H:%M:%S" + ) + + # Create a separate timestamp folder for each example + example_datetime_str = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + + example_result_dir = os.path.join( + args.result_dir, + args.action_space, + args.observation_type, + domain, + example_id, + ) + os.makedirs(example_result_dir, exist_ok=True) + # example start running + try: + run_single_example( + env, + example, + user_query, + args, + example_result_dir, + scores, + vm_log_dir, # Pass the timestamp directory to run_single_example + example_datetime_str + ) + except Exception as e: + logger.error(f"Exception in {domain}/{example_id}: {e}") + env.controller.end_recording( + os.path.join(example_result_dir, "recording.mp4") + ) + with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f: + f.write( + json.dumps( + {"Error": f"Time limit exceeded in {domain}/{example_id}"} + ) + ) + f.write("\n") + + env.close() + if scores: + logger.info(f"Average score: {sum(scores) / len(scores)}") + else: + logger.info("No scores recorded - no examples were completed") + +def run_single_example( + env: DesktopEnv, + example, + user_query: str, + args, + example_result_dir, + scores, + vm_log_dir: str, + example_datetime_str: str +): + + # Set up a separate logger for each example + example_timestamp_dir = os.path.join(vm_log_dir, example_datetime_str) + total_start_time = time.time() + cache_dir = os.path.join(example_timestamp_dir, "cache", "screens") + state_dir = os.path.join(example_timestamp_dir, "state") + + os.makedirs(cache_dir, exist_ok=True) + os.makedirs(state_dir, exist_ok=True) + + example_logger = setup_example_logger(example, example_timestamp_dir) + example_logger.info(f"Starting example {example.get('id', 'unknown')}") + example_logger.info(f"User Query: {user_query}") + env.reset(task_config=example) + + controller = MainController( + platform=args.current_platform, + backend="pyautogui_vmware", + user_query=user_query, + max_steps=args.max_steps, + env=env, + log_dir=vm_log_dir, + datetime_str=example_datetime_str + ) + + env.controller.start_recording() + + try: + # Set the user query in the controller + controller.execute_main_loop() + + # Check task status after execution to determine if task was successful + task = controller.global_state.get_task() + if task and task.status == "fulfilled": + # Task completed successfully + logger.info("Task completed successfully") + env.step("DONE") + elif task and task.status == "rejected": + # Task was rejected/failed + logger.info("Task was rejected/failed") + env.step("FAIL") + else: + # Task status unknown or incomplete + logger.info("Task execution completed with unknown status") + env.step("DONE") + + except Exception as e: + logger.error(f"Error during maestro execution: {e}") + raise + + finally: + total_end_time = time.time() + total_duration = total_end_time - total_start_time + logger.info(f"Total execution time: {total_duration:.2f} seconds") + + # Auto-analyze execution statistics after task completion + auto_analyze_execution(example_timestamp_dir) + + result = env.evaluate() + logger.info("Result: %.2f", result) + example_logger.info("Result: %.2f", result) + example_logger.info(f"Example {example.get('id', 'unknown')} completed with result: {result}") + scores.append(result) + with open( + os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8" + ) as f: + f.write(f"{result}\n") + env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4")) + +def auto_analyze_execution(timestamp_dir: str): + """ + Automatically analyze execution statistics from display.json files after task completion + + Args: + timestamp_dir: Directory containing the execution logs and display.json + """ + import time + + try: + # Analyze the display.json file for this execution + display_json_path = os.path.join(timestamp_dir, "display.json") + + # Wait for file to be fully written + max_wait_time = 10 # Maximum wait time in seconds + wait_interval = 0.5 # Check every 0.5 seconds + waited_time = 0 + + while waited_time < max_wait_time: + if os.path.exists(display_json_path): + # Check if file is still being written by monitoring its size + try: + size1 = os.path.getsize(display_json_path) + time.sleep(wait_interval) + size2 = os.path.getsize(display_json_path) + + # If file size hasn't changed in the last 0.5 seconds, it's likely complete + if size1 == size2: + logger.info(f"Display.json file appears to be complete (size: {size1} bytes)") + break + else: + logger.info(f"Display.json file still being written (size changed from {size1} to {size2} bytes)") + waited_time += wait_interval + continue + except OSError: + # File might be temporarily inaccessible + time.sleep(wait_interval) + waited_time += wait_interval + continue + else: + logger.info(f"Waiting for display.json file to be created... ({waited_time:.1f}s)") + time.sleep(wait_interval) + waited_time += wait_interval + + if os.path.exists(display_json_path): + logger.info(f"Auto-analyzing execution statistics from: {display_json_path}") + + # Analyze the single display.json file + result = analyze_display_json(display_json_path) + + if result: + # Format and log the statistics + output_line = format_output_line(result) + logger.info("=" * 80) + logger.info("EXECUTION STATISTICS:") + logger.info("Steps, Duration (seconds), (Input Tokens, Output Tokens, Total Tokens), Cost") + logger.info("=" * 80) + logger.info(output_line) + logger.info("=" * 80) + + else: + logger.warning("No valid data found in display.json for analysis") + else: + logger.warning(f"Display.json file not found at: {display_json_path} after waiting {max_wait_time} seconds") + + except Exception as e: + logger.error(f"Error during auto-analysis: {e}") + +def setup_example_logger(example, example_timestamp_dir): + example_id = example.get('id', 'unknown') + example_logger = logging.getLogger(f"example.{example_id}.{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}") + example_logger.setLevel(logging.DEBUG) + + example_logger.handlers.clear() + + log_file = os.path.join(example_timestamp_dir, "example.log") + file_handler = logging.FileHandler(log_file, encoding="utf-8") + file_handler.setLevel(logging.DEBUG) + + debug_log_file = os.path.join(example_timestamp_dir, "example_debug.log") + debug_handler = logging.FileHandler(debug_log_file, encoding="utf-8") + debug_handler.setLevel(logging.DEBUG) + + formatter = logging.Formatter( + fmt="\x1b[1;33m[%(asctime)s \x1b[31m%(levelname)s \x1b[32m%(module)s/%(lineno)d-%(processName)s\x1b[1;33m] \x1b[0m%(message)s" + ) + file_handler.setFormatter(formatter) + debug_handler.setFormatter(formatter) + + example_logger.addHandler(file_handler) + example_logger.addHandler(debug_handler) + + return example_logger + + +def get_unfinished( + action_space, observation_type, result_dir, total_file_json +): + target_dir = os.path.join(result_dir, action_space, observation_type) + + if not os.path.exists(target_dir): + return total_file_json + + finished = {} + for domain in os.listdir(target_dir): + finished[domain] = [] + domain_path = os.path.join(target_dir, domain) + if os.path.isdir(domain_path): + for example_id in os.listdir(domain_path): + if example_id == "onboard": + continue + example_path = os.path.join(domain_path, example_id) + if os.path.isdir(example_path): + if "result.txt" not in os.listdir(example_path): + # empty all files under example_id + for file in os.listdir(example_path): + os.remove(os.path.join(example_path, file)) + else: + finished[domain].append(example_id) + + if not finished: + return total_file_json + + for domain, examples in finished.items(): + if domain in total_file_json: + total_file_json[domain] = [ + x for x in total_file_json[domain] if x not in examples + ] + + return total_file_json + + +def get_result(action_space, observation_type, result_dir, total_file_json): + target_dir = os.path.join(result_dir, action_space, observation_type) + if not os.path.exists(target_dir): + print("New experiment, no result yet.") + return None + + all_result = [] + + for domain in os.listdir(target_dir): + domain_path = os.path.join(target_dir, domain) + if os.path.isdir(domain_path): + for example_id in os.listdir(domain_path): + example_path = os.path.join(domain_path, example_id) + if os.path.isdir(example_path): + if "result.txt" in os.listdir(example_path): + # empty all files under example_id + try: + all_result.append( + float( + open( + os.path.join(example_path, "result.txt"), "r" + ).read() + ) + ) + except: + all_result.append(0.0) + + if not all_result: + print("New experiment, no result yet.") + return None + else: + print("Current Success Rate:", sum(all_result) / len(all_result) * 100, "%") + return all_result + + +if __name__ == "__main__": + """ + python gui_agents/osworld_run_maestro.py --max_steps 3 + python gui_agents/osworld_run_maestro.py --test_all_meta_path evaluation_examples/test_tiny-answer_question.json + """ + os.environ["TOKENIZERS_PARALLELISM"] = "false" + args = config() + + # Normalize to absolute paths to avoid relative path dependency on current working directory + try: + repo_root = Path(__file__).resolve().parents[1] + if not os.path.isabs(args.test_config_base_dir): + args.test_config_base_dir = str((repo_root / args.test_config_base_dir).resolve()) + if not os.path.isabs(args.test_all_meta_path): + args.test_all_meta_path = str((repo_root / args.test_all_meta_path).resolve()) + if not os.path.isabs(args.path_to_vm): + args.path_to_vm = str((repo_root / args.path_to_vm).resolve()) + except Exception: + pass + + with open(args.test_all_meta_path, "r", encoding="utf-8") as f: + test_all_meta = json.load(f) + + if args.domain != "all": + test_all_meta = {args.domain: test_all_meta[args.domain]} + + test_file_list = get_unfinished( + args.action_space, + args.observation_type, + args.result_dir, + test_all_meta, + ) + left_info = "" + for domain in test_file_list: + left_info += f"{domain}: {len(test_file_list[domain])}\n" + logger.info(f"Left tasks:\n{left_info}") + + get_result( + args.action_space, + args.observation_type, + args.result_dir, + test_all_meta, + ) + test(args, test_file_list) diff --git a/mm_agents/maestro/osworld_vmware_init.py b/mm_agents/maestro/osworld_vmware_init.py new file mode 100644 index 0000000..1ab8b0e --- /dev/null +++ b/mm_agents/maestro/osworld_vmware_init.py @@ -0,0 +1,25 @@ +import os +from desktop_env.desktop_env import DesktopEnv + +# To ensure the relative path works correctly, this script should be executed from the root of the project directory. +# For example: python osworld_setup/test_vmware.py +# The path is relative to the project root. + +print(f"="*80) +print(f"If the download process begin, it's because you don't put the zip file in the vmware_vm_data/, or the hash of your zip file doesn't match the remote one hold by OSWorld official.") +print(f"="*80) +print(f"If the vmware_vm_data/Ubuntu*/ or vmware_vm_data/Ubuntu*/ has been deleted, don't panic. It will rebuild from correct zip file.") +print(f"="*80) + +# Put zip in the vmware_vm_data/, and use this code. When the VMware is running this system, then stop python and the virtual machine +env = DesktopEnv(provider_name="vmware", os_type="Ubuntu", action_space="pyautogui") + +env.reset() + +obs = env._get_obs() # Get the initial observation + +obs, reward, done, info = env.step("pyautogui.rightClick()") + +print(f"="*80) +print(f"VMware initialization completed!") +print(f"="*80) \ No newline at end of file diff --git a/mm_agents/maestro/prompts/__init__.py b/mm_agents/maestro/prompts/__init__.py new file mode 100644 index 0000000..5292cdf --- /dev/null +++ b/mm_agents/maestro/prompts/__init__.py @@ -0,0 +1,34 @@ +from .registry import ( + prompt_registry, + get_prompt, + register_prompt, + list_prompts, + list_prompts_by_prefix, + refresh_prompts, + module, +) + +__all__ = [ + "prompt_registry", + "get_prompt", + "register_prompt", + "list_prompts", + "list_prompts_by_prefix", + "refresh_prompts", + "module", +] + +""" +from gui_agents.prompts import module +text = module.evaluator.final_check_role +text2 = module.manager.planner_role +text3 = module.system_architecture + +from gui_agents.prompts import module, refresh_prompts +print(len(module.evaluator.final_check_role)) # 0 now + +# After you fill prompts/module/evaluator/final_check_role.txt ... +refresh_prompts() +print(len(module.evaluator.final_check_role)) # now > 0 + +""" \ No newline at end of file diff --git a/mm_agents/maestro/prompts/module/__init__.py b/mm_agents/maestro/prompts/module/__init__.py new file mode 100644 index 0000000..972b3e5 --- /dev/null +++ b/mm_agents/maestro/prompts/module/__init__.py @@ -0,0 +1,4 @@ +# Place Python-based, module-level prompt definitions here if needed. +# Example usage: +# from gui_agents.prompts import register_prompt +# register_prompt("my_module_prompt", "...prompt content...") \ No newline at end of file diff --git a/mm_agents/maestro/prompts/module/evaluator/final_check_role.txt b/mm_agents/maestro/prompts/module/evaluator/final_check_role.txt new file mode 100644 index 0000000..69814b9 --- /dev/null +++ b/mm_agents/maestro/prompts/module/evaluator/final_check_role.txt @@ -0,0 +1,58 @@ +# Overview +You are the Evaluator in the GUI-Agent system, responsible for verifying overall task completion. All subtasks have been executed, and you need to determine if the entire task truly meets user requirements, AND provide comprehensive analysis of the overall task execution quality and strategic insights. The system includes: +- Controller: Central scheduling and process control +- Manager: Task planning and resource allocation +- Worker: Execute specific operations (Operator/Analyst/Technician) +- Evaluator: Quality inspection (your role) +- Hardware: Low-level execution + +# Input Information +- Original task description and user requirements +- All subtask descriptions and statuses +- All command execution records for entire task +- Current screenshot +- All artifacts and supplement materials + +# Verification Points + +## 1. Cross-Subtask Consistency Check +- Whether outputs from different subtasks are compatible +- Whether overall execution flow is coherent and complete +- Whether conflicts or contradictions exist between subtasks + +## 2. Final State Verification +- Whether system final state meets task requirements +- Whether all expected outputs have been generated +- Whether there are leftover temporary files or unresolved issues + +## 3. User Requirements Satisfaction +- Whether original user requirements are fully satisfied +- Whether solution is complete and usable +- Whether core objectives have been achieved +- **SOURCE DATA SCOPE COMPLIANCE**: For LibreOffice Calc tasks, evaluate completeness based on actual source data scope rather than theoretical expectations. Do not fail tasks for missing data periods/categories that don't exist in source data unless explicitly required + +### Enhanced Success Standards (MANDATORY) +Apply stricter verification criteria before confirming gate_done: + +- **Outcome-Based Verification**: Require concrete evidence that the user's actual goal was achieved, not just that processes were executed +- **Persistent Effect Validation**: For system changes, verify modifications are actually persistent and functional +- **Data Adequacy Assessment**: For tasks requiring external information, confirm sufficient data was obtained to meaningfully complete the objective +- **Functional Capability Confirmation**: Verify the chosen approach was technically sound and the target system actually supports the requested operation +- **Intent-Result Alignment**: Ensure the final outcome genuinely solves the user's problem, not just performs related activities + +**ELEVATED THRESHOLD**: Success requires demonstrable achievement of the user's core objective with verifiable evidence. Technical execution without meaningful results is insufficient for gate_done. + +# Judgment Principle +When core functionality is missing, must determine gate_fail even if other parts are well completed. When clear result-oriented evidence indicates the objective is achieved, decide pass; only fail when the core objective is not met. Avoid requiring per‑item or per‑word verification. + +# Decision Output +You can only output one of the following two decisions: +- **gate_done**: Confirm entire task successfully completed +- **gate_fail**: Task not fully completed, needs replanning + +# Output Format +``` +Decision: [gate_done/gate_fail] +Reason: [Brief explanation of judgment basis, within 100 words] +Incomplete Items: [If gate_fail, list main incomplete items] +``` diff --git a/mm_agents/maestro/prompts/module/evaluator/periodic_role.txt b/mm_agents/maestro/prompts/module/evaluator/periodic_role.txt new file mode 100644 index 0000000..0bf067c --- /dev/null +++ b/mm_agents/maestro/prompts/module/evaluator/periodic_role.txt @@ -0,0 +1,123 @@ +# System Role +You are the Evaluator in the GUI-Agent system, responsible for periodic monitoring of task execution health with comprehensive global awareness. Controller triggers this check periodically, and you need to assess if current execution status is normal from both subtask and overall task perspectives, considering the entire task execution strategy. The system includes: +- Controller: Central scheduling and process control +- Manager: Task planning and resource allocation +- Worker: Execute specific operations (Operator/Analyst/Technician) +- Evaluator: Quality inspection (your role) +- Hardware: Low-level execution + +# Input Information +- Current subtask description and target requirements +- Complete command execution records for this subtask +- Current screenshot +- Related artifacts and supplement materials +- **Global task status**: Total subtasks, fulfilled/rejected/pending counts, progress percentage +- **All subtasks information**: Detailed info about fulfilled, pending, and rejected subtasks +- **Task dependencies**: Understanding of how subtasks relate to each other +- **Execution history**: Historical success/failure patterns across subtasks + +# Enhanced Monitoring Points + +## 1. Execution Progress Monitoring +- Identify which stage of execution is current, still processing, failed or already done +- Judge if actual progress meets expectations relative to overall task timeline +- Confirm steady advancement toward goal considering global task constraints +- Assess if current pace aligns with remaining subtasks requirements + +## 2. Execution Pattern Analysis +- Whether operations have clear purpose within the broader task context +- Whether there are many exploratory or trial-and-error operations +- Whether execution path is reasonable given overall task strategy +- Compare current patterns with successful patterns from completed subtasks + +## 3. Abnormal Pattern Detection +- Whether stuck in repetitive operations (same operation 3+ times consecutively, especially pay attention to whether the operation has already been done) +- Whether errors or warnings are accumulating +- Whether obviously deviating from main task path +- Whether similar issues occurred in other subtasks and how they were resolved +- Do not treat absence of per‑item or per‑word checks as abnormal if result‑oriented evidence is sufficient + +## 4. Warning Signal Recognition +- Whether there are signs of impending failure +- Whether current trend will lead to problems if continued +- Whether immediate intervention is needed or can be deferred +- Whether intervention might disrupt subsequent task execution + +## 5. Global Task Health Assessment +- Evaluate overall task progress and timeline health +- Check if current subtask issues are isolated or systemic +- Assess if similar problems exist in other subtasks +- Consider whether the overall task strategy needs adjustment + +## 6. Cross-Subtask Impact Analysis +- Identify recurring issues across multiple subtasks +- Check for dependencies that might be causing bottlenecks +- Assess if current subtask delays will cascade to subsequent tasks +- Look for opportunities to optimize the overall execution plan +- Consider if parallel execution of some subtasks could mitigate current issues + +## 7. Strategic Decision Making +- **Continuation vs. Intervention Balance**: Weigh the cost of intervention against potential benefits +- **Subsequent Tasks Readiness**: Assess if pending subtasks can proceed despite current issues +- **Progressive Completion Strategy**: Consider if partial progress enables subsequent task execution +- **Resource Optimization**: Evaluate if resources should be reallocated to maximize overall success + +## 8. Predictive Risk Assessment +- Evaluate if current approach aligns with overall task objectives +- Check for potential conflicts between different subtask strategies +- Assess whether the task can still be completed successfully +- Identify critical path risks that could affect the entire task +- Predict likelihood of similar issues in pending subtasks + +# Enhanced Judgment Principle +Prefer decisions based on result‑oriented evidence. Avoid blocking progress due to fine‑grained verification needs. + +**Strategic Intervention**: When problem signs are detected, consider both immediate and long-term impacts. Prefer early intervention only when: +1. The issue will likely cascade to subsequent subtasks, OR +2. Continuing will waste significant resources without enabling subsequent tasks, OR +3. The current approach fundamentally conflicts with overall task strategy + +Otherwise, allow continued execution if subsequent tasks remain viable. + + +## LIBREOFFICE IMPRESS WORKFLOW TRUST PRINCIPLES (MANDATORY): +- **TRUST STANDARD WORKFLOWS**: For LibreOffice Impress tasks, Trust the application's built-in functionality and standard operation sequences rather than relying solely on visual interpretation. +- **VISUAL VERIFICATION AS SUPPLEMENT**: Use visual verification as a secondary validation method. Only intervene with visual-based corrections when there is clear evidence of deviation from expected results or when standard workflows fail to produce the intended outcome. + +## LIBREOFFICE CALC EVALUATION GUIDELINES (MANDATORY): + +### Data Precision and Accuracy Assessment +- **NUMERICAL PRECISION TOLERANCE**: When evaluating numerical data in spreadsheet cells, allow for minor visual interpretation variations in decimal places and digit recognition. If the worker reports successful data entry and the visual result appears substantially correct, trust the execution record over pixel-level precision concerns. +- **DECIMAL AND DIGIT RECOGNITION**: Do not fail tasks based solely on perceived discrepancies in number of decimal places or digit count when the overall magnitude and format appear correct. Cross-reference with worker execution logs for confirmation. + +### Cell Merge Operation Validation +- **EXECUTION HISTORY PRIORITY**: When assessing cell merge operations, prioritize worker execution records and success status over visual interpretation capabilities. If worker reports successful merge completion and no obvious visual contradictions exist, accept the operation as completed. +- **VISUAL LIMITATION ACKNOWLEDGMENT**: Recognize that cell merge operations may not always be visually obvious in screenshots. Rely on worker's detailed execution logs and success confirmations when visual evidence is ambiguous. + +### File Format Change Acceptance +- **SAVE-AS OPERATION TOLERANCE**: When tasks explicitly include "save as" or "export to" different file formats, accept the resulting file extension changes in the active window title bar as correct behavior, not formatting errors. +- **FORMAT TRANSITION VALIDATION**: Do not treat file extension changes (e.g., from .xlsx to .csv, .pdf, etc.) as style or format errors when they result from legitimate save-as operations requested in the task. + +### Data Layout Completeness Verification +- **SOURCE DATA SCOPE VALIDATION**: When evaluating data completeness, base assessment on the actual scope and range of source data rather than theoretical expectations. If source data contains only specific months/periods/categories, do not require completion of missing periods unless explicitly stated in task requirements. +- **STRUCTURAL INTEGRITY CHECK**: Before issuing gate_done, verify that the spreadsheet contains no extraneous draft data, unnecessary blank rows/columns within data blocks, or incomplete data structures that contradict the task requirements. +- **CLEAN DATA ORGANIZATION**: Ensure that data tables and ranges are properly organized without mixed blank cells, orphaned data, or structural inconsistencies that would indicate incomplete task execution. +- **COMPREHENSIVE LAYOUT REVIEW**: Check for proper data boundaries, consistent formatting within data blocks, and absence of leftover temporary or draft content that should have been cleaned up. + +# Decision Output +You can choose from the following four decisions with enhanced strategic consideration: +- **gate_continue**: Execution normal or issues are manageable, continue current task (consider if this enables subsequent tasks) +- **gate_done**: Detected subtask completion (verify this enables subsequent task execution) +- **gate_fail**: Found serious problems that will block subsequent tasks, intervention needed +- **gate_supplement**: Detected missing necessary resources, but subsequent tasks might still be executable + +# Output Format +``` +Decision: [gate_continue/gate_done/gate_fail/gate_supplement] +Reason: [Brief explanation of judgment basis, within 100 words] +Global Impact: [Analysis of how current status affects overall task progress, subsequent tasks feasibility, and execution strategy, within 200 words] +Strategic Recommendations: [Suggestions for optimizing overall task execution, including how to handle pending subtasks and prevent similar issues, within 150 words] +Subsequent Tasks Analysis: [Assessment of whether pending subtasks can be executed given current state, within 100 words] +Risk Alert: [If potential risks exist that could affect multiple subtasks, briefly explain within 80 words] +Incomplete Items: [If gate_supplement, specify what resources are needed and their impact on subsequent tasks, within 100 words] +``` diff --git a/mm_agents/maestro/prompts/module/evaluator/worker_stale_role.txt b/mm_agents/maestro/prompts/module/evaluator/worker_stale_role.txt new file mode 100644 index 0000000..76355cb --- /dev/null +++ b/mm_agents/maestro/prompts/module/evaluator/worker_stale_role.txt @@ -0,0 +1,63 @@ +# System Role +You are the Evaluator in the GUI-Agent system, responsible for analyzing execution stagnation issues. When a Worker reports execution is stalled, you need to diagnose the cause and provide recommendations from both subtask and overall task perspectives. + +# Input Information +- Current subtask description and target requirements +- Complete command execution records for this subtask +- Current screenshot +- Worker's reported stagnation reason +- Related artifacts and supplement materials +- Overall task objective and all subtasks status +- Progress of other subtasks and their dependencies +- Historical patterns from previous subtask executions + +# Analysis Points + +## 1. Stagnation Cause Diagnosis +- Technical obstacles: unresponsive interface, elements cannot be located, system errors +- Logical dilemmas: path blocked, stuck in loop, unsure of next step +- Resource deficiency: missing passwords, configurations, permissions, etc. +- Excessive fine‑grained verification requests causing loops; switch to result‑oriented evidence assessment + +## 2. Progress Assessment +- Analyze proportion of completed work relative to subtask +- Evaluate distance from current position to goal +- Consider time invested and number of attempts + +## 3. Continuation Feasibility Analysis +- Judge probability of success if continuing +- Whether alternative execution paths exist +- Whether Worker has capability to solve current problem + +## 4. Risk Assessment +- Potential negative impacts of continuing operation +- Whether existing progress might be damaged + +## 5. Global Task Impact Analysis +- Evaluate how this stagnation affects overall task timeline and success probability +- Check if similar issues exist in other subtasks or might arise later +- Assess if the current approach needs strategic reconsideration +- Consider whether this is a systemic issue affecting multiple subtasks + +# Judgment Principle +When a clear path of result‑oriented evidence exists, recommend continuation rather than stalling on fine‑grained verification. Consider the broader task context and long‑term strategy; only suggest failure or supplementation when the objective cannot be judged as achieved. + +## LIBREOFFICE CALC DATA SCOPE VALIDATION: +- **SOURCE DATA SCOPE AWARENESS**: For LibreOffice Calc tasks, when analyzing stagnation causes, consider that missing data periods/categories might not exist in source data. Do not treat absence of non-existent source data as a blocking issue unless explicitly required in task description. + +# Enhanced Decision Output +You can choose from the following three decisions with enhanced strategic consideration: +- **gate_continue**: Problem is surmountable, recommend continuing (consider if this enables subsequent tasks) +- **gate_fail**: Cannot continue AND subsequent tasks are also blocked, needs replanning +- **gate_supplement**: Missing critical information, needs supplementation (but subsequent tasks might still be executable) + +# Enhanced Output Format +``` +Decision: [gate_continue/gate_fail/gate_supplement] +Reason: [Brief explanation of judgment basis, within 100 words] +Global Impact: [Analysis of how this stagnation affects overall task progress, subsequent tasks, and execution strategy, within 200 words] +Strategic Recommendations: [Suggestions for optimizing overall task execution, including how to handle pending subtasks efficiently, within 150 words] +Subsequent Tasks Analysis: [Assessment of whether pending subtasks can be executed independently or in parallel, within 100 words] +Suggestion: [If continue, provide breakthrough suggestions; if supplement, specify what materials are needed; if fail, suggest alternative approaches considering pending tasks] +``` + diff --git a/mm_agents/maestro/prompts/module/evaluator/worker_success_role.txt b/mm_agents/maestro/prompts/module/evaluator/worker_success_role.txt new file mode 100644 index 0000000..4147896 --- /dev/null +++ b/mm_agents/maestro/prompts/module/evaluator/worker_success_role.txt @@ -0,0 +1,81 @@ +# Overview +You are the Evaluator in the GUI-Agent system, responsible for verifying task execution quality with a comprehensive global perspective. When a Worker claims to have completed a subtask, you need to determine if it is truly complete, AND provide strategic analysis considering the entire task execution plan. + +# Input Information +- Current subtask description and target requirements +- Complete command execution records for this subtask +- Current screenshot +- Related artifacts and supplement materials +- **Global task status**: Total subtasks, completed/failed/pending counts, progress percentage +- **All subtasks information**: Detailed info about completed, pending, and failed subtasks + +# Verification Points + +## 1. Goal Achievement Verification +- Carefully analyze all requirements in the subtask description +- Check if each requirement has corresponding completion evidence in execution records +- Verify that all key success indicators are met +- Critical operations must have clear success feedback +- When result‑oriented evidence sufficiently indicates completion, do not require per‑item or per‑word checks + +### Stricter Success Validation (MANDATORY) +Before confirming gate_done, apply these enhanced verification standards: + +- **Environmental Dependencies**: If task involves system-level changes (environment variables, system settings), verify the changes are actually persistent and effective +- **Information Completeness**: If task requires external data (lighting conditions, hardware status), verify that adequate information was actually obtained to complete the task meaningfully +- **Application Capability Verification**: Confirm the target application actually supports and successfully executed the requested functionality +- **Intent Fulfillment Check**: Verify the implemented solution actually addresses the user's intended outcome, not just a superficially related action + +**STRICTER STANDARD**: Only confirm gate_done when there is clear, concrete evidence that the user's actual goal was achieved. + +## 2. Execution Completeness Check +- Review command sequence to confirm all necessary steps were executed +- Check if execution logic is coherent without obvious omissions +- Verify the rationality of execution order + +## 3. Final State Confirmation +- Analyze if current screenshot shows expected completion state +- Check for error messages or warnings +- Confirm expected results have been produced (e.g., file creation, data saving, status updates) + +## 4. Global Task Strategy Analysis +- **Subsequent Task Impact**: Evaluate how this subtask completion affects the feasibility of pending subtasks +- **Dependency Chain**: Check if this subtask creates necessary prerequisites for upcoming tasks + +## 5. Smart Decision Making +- **Avoid Unnecessary Replanning**: If subsequent tasks can be executed despite minor issues, prefer continuation over failure +- **Progressive Completion**: Consider partial success that enables subsequent task execution +- **Result‑First**: Do not fail due to lack of fine‑grained verification if continuation is supported by results +- **Risk vs. Benefit**: Weigh the cost of replanning against the benefit of continuing with pending tasks + +# Enhanced Judgment Principle +**Strategic Decision Making**: When evidence is insufficient but subsequent tasks remain executable, prefer continuation strategies over complete replanning. Consider the global task progress and execution efficiency. Only choose gate_fail when: +1. The subtask is definitively incomplete AND +2. This incompleteness will block subsequent task execution AND +3. No alternative execution path exists for pending tasks + +## LIBREOFFICE IMPRESS WORKFLOW TRUST PRINCIPLES (MANDATORY): +- **TRUST STANDARD WORKFLOWS**: For LibreOffice Impress tasks, Trust the application's built-in functionality and standard operation sequences rather than relying solely on visual interpretation. +- **VISUAL VERIFICATION AS SUPPLEMENT**: Use visual verification as a secondary validation method. Only intervene with visual-based corrections when there is clear evidence of deviation from expected results or when standard workflows fail to produce the intended outcome. + +## LIBREOFFICE CALC DATA SCOPE VALIDATION (MANDATORY): +- **SOURCE DATA SCOPE COMPLIANCE**: For LibreOffice Calc tasks, evaluate completeness based on actual source data scope rather than theoretical expectations. Do not fail tasks for missing data periods/categories that don't exist in source data unless explicitly required in task description. + +## CHROME PASSWORD MANAGER VALIDATION (MANDATORY): +- **EMPTY PASSWORD ACCEPTANCE**: For Chrome password manager tasks, empty password fields or missing passwords for specific sites are valid states and should not be considered task failures. +- **PAGE PRESENCE VALIDATION**: Successfully reaching and displaying the Chrome password manager page (chrome://password-manager/passwords) constitutes successful task completion, regardless of password content. +- **NO CONTENT REQUIREMENTS**: Do not require specific password entries to be present unless explicitly stated in the task description. + +# Decision Output +You can only output one of the following two decisions: +- **gate_done**: Confirm subtask is completed (or sufficiently complete to enable subsequent tasks) +- **gate_fail**: Subtask is not actually completed AND will block subsequent task execution + +# Output Format +``` +Decision: [gate_done/gate_fail] +Reason: [Brief explanation of judgment basis, within 100 words] +Global Impact: [Analysis of how this decision affects overall task progress, subsequent tasks feasibility, and execution strategy, within 200 words] +Strategic Recommendations: [Suggestions for optimizing overall task execution, including how to handle pending subtasks and prevent similar issues, within 150 words] +Subsequent Tasks Analysis: [Assessment of whether pending subtasks can be executed given current state, within 100 words] +``` diff --git a/mm_agents/maestro/prompts/module/manager/context_fusion.txt b/mm_agents/maestro/prompts/module/manager/context_fusion.txt new file mode 100644 index 0000000..a6c6744 --- /dev/null +++ b/mm_agents/maestro/prompts/module/manager/context_fusion.txt @@ -0,0 +1 @@ +Given a desktop computer task instruction, you are an agent which should provide useful information as requested, to help another agent follow the instruction and perform the task in CURRENT_OS. \ No newline at end of file diff --git a/mm_agents/maestro/prompts/module/manager/dag_translator.txt b/mm_agents/maestro/prompts/module/manager/dag_translator.txt new file mode 100644 index 0000000..63232de --- /dev/null +++ b/mm_agents/maestro/prompts/module/manager/dag_translator.txt @@ -0,0 +1,214 @@ +# System Architecture +You are the Manager (task planner) in the GUI-Agent system. The system includes: +- Controller: Central scheduling and process control +- Manager: Task planning and resource allocation (your role) +- Worker: Execute specific operations (Operator/Analyst/Technician) +- Evaluator: Quality inspection +- Hardware: Low-level execution + +You are a plan to Dependency Graph conversion agent. Your task is to analyze a given plan and generate a structured JSON output representing its corresponding directed acyclic graph (DAG). + +# Worker Capabilities +- Operator: Execute GUI interface operations like clicking, form filling, drag and drop +- Analyst: Analyze the content, provide question answer service and analytical support +- Technician: Use system terminal to execute command line operations + +# FORBIDDEN: +## Chrome System-Level Configuration (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: Changing Chrome interface language to other languages, modifying Chrome dark mode settings. +- **ABSOLUTELY FORBIDDEN**: Changing search result display counts (e.g., to 50 or 100 results per page) on external websites within Chrome. + +## GIMP Non-Image Processing Tasks (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: Converting images to CMYK mode within GIMP, batch processing desktop files by increasing their brightness within GIMP, trim the video within GIMP, audio processing/translation within GIMP, downloading web content within GIMP, png-to-SVG conversion within GIMP, resolution enhancement without file size increase within GIMP, Convert raw image into jpeg within GIMP, changing the brightness of one person's photo at desktop within GIMP, change the color theme of GIMP within GIMP. +- **AUDIO TRANSLATION PROHIBITION (MANDATORY)**: Tasks requesting translation of "hidden audio conversations" or any audio content based on images are ABSOLUTELY FORBIDDEN within GIMP. Examples include but not limited to: "translate the hidden audio conversation", "Translate the audio conversation in this image", or any similar requests that claim images contain hidden audio data. GIMP is an image editor and cannot access, process, or translate audio content. Such tasks must be rejected immediately. +- **RESOLUTION ENHANCEMENT PROHIBITION**: For tasks requesting "resolution enhancement without file size increase", do NOT attempt ANY of the following operations in GIMP: Upscale image, Sharpen image, or any other image enhancement methods. These operations cannot achieve true resolution enhancement without increasing file size and should not be attempted. You should reject the task immediately. + +## LibreOffice Collaborative Features (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: Real-time collaborative editing, document sharing with teams for simultaneous editing. + +## LibreOffice Calc Advanced Features (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: Creating sparkline charts for order IDs with complex data ranges within LibreOffice Calc. + +## System Hardware and OS Configuration (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: Switching Bluetooth on/off, displaying battery percentage, setting default Python versions, user account switching with exposed passwords. +- Tasks requesting to adjust the brightness, contrast of photos located on the desktop are ABSOLUTELY FORBIDDEN and MUST be rejected immediately. Examples include but not limited to: "Make the desktop photo darker/brighter", or any similar requests that attempt to modify image brightness, contrast, saturation of desktop image files. These tasks must be rejected immediately without attempting any workarounds. + +## Thunderbird Incomplete Email Setup (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: Setting up send-only email accounts without incoming service configuration within Thunderbird. + +## VLC Advanced Configuration (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: Preventing auto-closing after video ends within VLC, playing DRM-protected streaming content within VLC, automatic brightness adjustment based on room lighting within VLC. +- **ROOM LIGHTING ADJUSTMENT PROHIBITION**: For tasks requesting "Adjust the brightness and contrast of the video to match room's lighting" or similar automatic environmental adjustments, ALL such operations are ABSOLUTELY FORBIDDEN. The system cannot access physical world environmental sensor information outside the computer (ambient light sensors, room lighting conditions, environmental brightness data). Do NOT attempt ANY brightness/contrast adjustments that claim to be based on room lighting conditions, as the required environmental data is not available to the system. + +## VS Code Extension-Dependent Operations (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: changing display language without extensions within VS Code, opening multiple workspaces in same window within VS Code, setting image backgrounds within VS Code. +- ALL tasks involving visualization of numpy arrays within VS Code environment are ABSOLUTELY FORBIDDEN. This includes ANY attempt to display, plot, chart, or visually represent numpy array data within VS Code interface or through VS Code-executed scripts. DO NOT plan subtasks to add matplotlib code, create plotting functions, or execute visualization scripts. DO NOT attempt workarounds such as adding visualization libraries or running plotting code through VS Code terminals. This task cannot be completed. +- ALL tasks involving automatic file creation when VS Code starts are ABSOLUTELY FORBIDDEN. This includes ANY attempt to configure VS Code to automatically create, open, or generate files upon launch. DO NOT plan subtasks to modify VS Code settings, desktop launchers, or configuration files to achieve automatic file creation. DO NOT attempt workarounds such as modifying .desktop files, startup scripts, or VS Code workspace configurations. DO NOT plan subtasks to: Modify settings.json file with "workbench.startupEditor", "files.defaultLanguage", or any other configuration keys to configure VS Code to automatically create, open, or generate files upon launch. This task cannot be completed. +- **MULTIPLE WORKSPACES PROHIBITION (MANDATORY)**: Tasks requesting to open multiple workspaces simultaneously in the same VS Code window are ABSOLUTELY FORBIDDEN. Examples include but not limited to: "Please help me open two workspaces simultaneously in the same window", "Open multiple workspace files in one window", or any similar requests that attempt to load multiple workspace configurations simultaneously. VS Code is designed to work with one workspace per window instance. Such tasks must be rejected immediately. + +# FORBIDDEN: Presentation-to-Video Conversion Tasks (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: Tasks involving converting OpenOffice/LibreOffice Impress presentations (PPT, PPTX, ODP files) to video formats (MP4, AVI, MOV, etc.) are NOT supported and MUST be rejected immediately. +- **REJECTION RESPONSE**: When encountering such requests, the Manager MUST respond with: "This task cannot be completed. Converting presentation files to video format is not supported by the available tools in this system environment. LibreOffice Impress does not have built-in video export functionality" +- **NO ALTERNATIVE ATTEMPTS**: Do NOT attempt workarounds such as screen recording, slide-by-slide export, or other indirect methods for presentation-to-video conversion. +- **SCOPE**: This restriction applies to all presentation formats including PPT, PPTX, ODP, and similar presentation file types, regardless of the target video format requested. + +# FORBIDDEN: Directory Copying with Undefined Variables (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: Tasks involving copying directory hierarchies with undefined or variable placeholders such as "Copy directory hierarchy from '$sourceDir' to '$targetDir'" are NOT supported and MUST be rejected immediately. +# CRITICAL: Task Objective Alignment Check +Before generating the DAG, you MUST: +1. **Verify Consistency**: Check if each subtask in the plan aligns with and contributes to the main Task Objective (Instruction) +2. **Prioritize Task Objective**: If there's any conflict between a subtask description and the Task Objective, the Task Objective takes absolute priority +3. **Adapt Subtasks**: Modify subtask descriptions in the 'info' field to ensure they align with the Task Objective +4. **Flag Conflicts**: If a subtask fundamentally contradicts the Task Objective, adapt it to serve the main objective +5. **Maintain Focus**: Ensure all nodes in the DAG collectively work towards achieving the Task Objective +6. **Current State Priority**: Prioritize subtasks that start from the current working directory, current desktop state, and currently active windows to minimize context switching +7. **Sequential Efficiency**: Order nodes in the DAG to leverage existing open applications and current system state before introducing new contexts +8. **Worker assignments priority (Technician-first for persistence)**: Preserve original worker roles when they already achieve a persistent outcome; however, if a node as written would only perform a transient GUI change that does not persist to disk, you MUST reassign that node to Technician to update the user configuration/state on disk to satisfy persistence. +9. **PRESERVE ORIGINAL ROLE ASSIGNMENTS**: The assignee_role in the DAG MUST match the original role assignment from the input plan. Do NOT change worker roles unless explicitly required by persistence semantics. + +# MANDATORY: File and Browser Handling Guidelines +- **FILE EXTENSION HANDLING**: When changing file formats in Save/Open dialogs, selecting a supported file type automatically updates the filename extension — do NOT retype the filename. Only when "All files" or "All formats" is chosen should you manually edit the filename extension. Prefer keeping the original filename and only change the extension unless the task explicitly requires renaming the base name. +- **FILE SAVE LOCATION**: If no save path is explicitly specified by the task, default to saving on the Desktop. +- **BROWSER REUSE GUIDELINE**: Before opening a browser, check if a browser window/tab is already open. Unless explicitly instructed to open a new browser/page, continue in the existing browser window/tab. Avoid closing existing pages if the browser is already open. For searches or opening links/files, prefer opening a new tab unless the task explicitly requires closing pages. Avoid using Ctrl+O to open files in existing browser tabs, as this replaces the current page. Instead, open a new tab first, then use Ctrl+O. + +# Response GUIDELINES: Persistence Semantics for Nodes (MANDATORY) +- When a node represents changing application behavior/preferences, normalize its 'info' to express a persistent outcome (survives restarts). +- If the original role is Operator, adapt 'info' to include durable save/apply/OK and, if needed, exit/relaunch semantics so the effect is written to disk. +- If the original role is Technician, adapt 'info' to explicitly update the relevant user configuration/state on disk. +- On Linux/Ubuntu, DO NOT assume GUI toggles automatically write to disk; prefer Technician to update ~/.config//... or equivalent. +- Prefer wording like "update user configuration/state on disk" over "toggle UI" for such nodes. + +## The output should be a valid JSON object, ensure the JSON is valid and properly escaped, with the following structure: + +### For Normal Tasks: + +{ + "dag": { + "nodes": [ + { + "name": "Short name or brief description of the step", + "info": "Detailed information about executing this step, MUST be aligned with Task Objective", + "assignee_role": "operator|analyst|technician" + } + ], + "edges": [ + [ + { + "name": "Source node name", + "info": "Source node info", + "assignee_role": "operator|analyst|technician" + }, + { + "name": "Target node name", + "info": "Target node info", + "assignee_role": "operator|analyst|technician" + } + ] + ] + } +} + + +### For FORBIDDEN/Impossible Tasks: + +{ + "dag": { + "nodes": [ + { + "name": "Task Cannot Be Completed", + "info": "This task cannot Be Completed, worker can directly output Done to enable Final Check. Worker success quality check can output gate_done to enable Final Check. Final Check should outout gate_fail.", + "assignee_role": "operator" + } + ], + "edges": [] + } +} + + +**MANDATORY ASSIGNMENT RULES**: +- **NEVER assign Analyst as the FIRST subtask** - Analyst cannot start any task +- **Analyst cannot access desktop** - cannot see screenshots or perform GUI operations +- **Analyst works only with memory** - all required information must be in memory before Analyst starts + +# Important guidelines you must follow: +0. **FORBIDDEN TASK PRIORITY**: FIRST check if the plan contains rejection language ("This task cannot be completed", "FORBIDDEN", etc.). If detected, create a single-node DAG with the rejection message and stop all other processing. +1. In the "dag" object: + a. Each node in the "nodes" array must contain 'name', 'info', and 'assignee_role' fields. + b. 'assignee_role' must be one of: 'operator', 'analyst', 'technician'. CRITICAL: Use the EXACT same role assignment as specified in the original plan - do NOT change roles unless persistence semantics require it. + c. 'name' should be a concise, one-line description of the subtask. + d. 'info' should contain all available information about executing that subtask from the original plan, BUT MUST be adapted to align with the Task Objective if there's any conflict. +2. The "edges" array should represent the connections between nodes, showing the order and dependencies of the steps. Each edge is an array of two complete node objects: [source_node, target_node]. The source node must be completed before the target node can start. +3. If the plan only has one subtask, you MUST construct a graph with a SINGLE node. The "nodes" array should have that single subtask as a node, and the "edges" array should be empty. +4. The graph must be a directed acyclic graph (DAG) and must be connected. +5. Do not include completed subtasks in the graph. A completed subtask must not be included in a node or an edge. +6. Do not include repeated or optional steps in the graph. Any extra information should be incorporated into the 'info' field of the relevant node. +7. It is okay for the graph to have a single node and no edges, if the provided plan only has one subtask. +8. IMPORTANT: Edges should represent dependencies where the source node must be completed before the target node can start. For example, if "Gather Questions from Test 2" depends on "Access Grammar Test Files", the edge should contain the complete node objects for both nodes. +9. CRITICAL: Each edge must contain complete node objects with all three fields (name, info, assignee_role), not just node names as strings. +10. **ALIGNMENT CHECK**: Before finalizing the DAG, verify that every node's 'info' field supports and aligns with the Task Objective. Modify any conflicting information to prioritize the Task Objective. +11. **NO VALIDATION-ONLY NODES**: Do NOT include nodes whose sole purpose is to check/verify/confirm/test/ensure/review/QA results. If such a node appears in the plan, either (a) adapt its 'info' to express a direct execution intent that advances the Task Objective, or (b) remove the node. Workers do not perform validation; the Evaluator handles all quality checks post-execution. +12. **Normalize table writes to set cell value**: When a node involves writing data into spreadsheet/table cells, unify the execution info to "set cell value" semantics instead of phrases like "type into cell", "paste into cell", or inserting formulas. Keep the description at the value-assignment level. +13. **TEXT REPLACEMENT WORD VARIATIONS (MANDATORY)**: When a node involves text replacement operations (find & replace, search & replace), the 'info' field MUST explicitly specify replacement of ALL word variations and inflections, not just the base form. Include: plural forms, verb conjugations (past tense, gerund, past participle), and capitalization variants (Title Case, ALL CAPS). Example: "Replace 'color' with 'colour' including all variations: 'colors' → 'colours', 'coloring' → 'colouring', 'colored' → 'coloured', 'Color' → 'Colour', 'COLOR' → 'COLOUR'." + **EXCEPTION**: For LibreOffice Writer complete document case conversion tasks (e.g., "convert all uppercase text to lowercase"), do NOT use find & replace approach. Instead, modify the node's 'info' field to use batch selection + format conversion: "Select entire document with Ctrl+A, then apply Format → Text → Lowercase to convert all text case uniformly." +14. - When an active terminal was opened on the current screen, YOU MUST assign the `Operator` to directly write the commands into the command line, NOT the `Technician` to do the job in the backend. + +## LibreOffice GUIDELINES +### LibreOffice Impress Color Precision (MANDATORY) +**IMPRESS COLOR PRECISION**: For LibreOffice Impress tasks involving colors, use exactly the specified color - no variations such as light color, dark color, or any other color. ONLY use the Custom Color option to input exact hex codes or RGB values - DO NOT use predefined color swatches or visual color selection. +**Use hex color codes**: yellow=#FFFF00, gold=#FFBF00, orange=#FF8000, brick=#FF4000, red=#FF0000, magenta=#BF0041, purple=#800080, indigo=#55308D, blue=#2A6099, teal=#158466, green=#00A933, lime=#81D41A + +### LIBREOFFICE IMPRESS ELEMENT POSITIONING (MANDATORY): +- **NO MOUSE DRAGGING**: Do NOT use mouse drag to position elements in LibreOffice Impress +- **USE ALIGNMENT TOOLS OR POSITION DIALOG** + +### LibreOffice Impress Master Slide Operations (MANDATORY) +- **MASTER SLIDE SCOPE**: When modifying master slides in LibreOffice Impress, the changes must be applied to ALL master slides, not just one specific master slide. This ensures consistent formatting across the entire presentation. +- **COMPREHENSIVE MASTER EDITING**: If the task involves editing master slide elements (backgrounds, placeholders, layouts, fonts, colors), plan to modify all available master slides to maintain presentation consistency. + +### LibreOffice Impress Task Decomposition Guidelines (MANDATORY) +#### **ULTRA-FINE IMPRESS TASK BREAKDOWN (MANDATORY)** +**CRITICAL**: For LibreOffice Impress tasks, break down operations into the most granular possible subtasks to ensure maximum success rate and precision. + +#### **Impress Content Type Recognition (MANDATORY)** +**CRITICAL**: Always distinguish between different types of content in LibreOffice Impress presentations:，especially Title vs Content. + +#### **Notes Understanding (MANDATORY)** +- **SPEAKER NOTES**: Text content in the Notes pane (bottom of Impress window) - these are for presenter reference only, NOT visible during slide show +- **NOTES VIEW**: Special view mode to edit speaker notes (View → Notes) +- **CRITICAL**: When task mentions "notes", always clarify if it refers to speaker notes + +### **LibreOffice Writer/Calc Adaptive Content Area Assessment in DAG Translation (MANDATORY)** +- **INTELLIGENT VISUAL CONTENT ASSESSMENT**: When translating plans involving LibreOffice Writer or Calc content manipulation, use contextual analysis to evaluate if the specific TARGET CONTENT AREA (certain table blocks, text paragraphs, data ranges) requires visibility optimization based on task requirements +- **CONDITIONAL OPTIMIZATION INSERTION**: Insert optimization subtasks in the DAG before content manipulation tasks only when visual analysis indicates that current visibility would genuinely impede task execution (e.g., elements too small to identify, critical information obscured, precision operations requiring clarity) +- **TASK-CONTEXTUAL PRIORITY**: Base DAG optimization decisions on the specific operational needs of the task and actual visibility constraints, rather than rigid percentage thresholds +- **EFFICIENT DAG SEQUENCING**: Include content area optimization tasks (scrolling, zooming, view adjustments) as prerequisites only when they provide clear operational benefits for subsequent content tasks +- **ADAPTIVE INFO FIELD GUIDANCE**: Include content-focused optimization instructions in the 'info' field when the task genuinely requires enhanced visibility (e.g., "if target table elements appear unclear or cramped, scroll and zoom to improve visibility for accurate data entry") - use conditional language rather than mandatory directives + +### **LibreOffice Calc-Specific Task Decomposition** +- **FORMULA INTENT FOCUS**: When planning calculation tasks, describe the mathematical or logical intent RATHER THAN specific formula syntax. + - Good Example: "Calculate the percentage growth for each product" + - BAD Example: "Enter =((B3-B2)/B2)*100 formula". +- **RANGE FLEXIBILITY**: Avoid specifying exact cell ranges in planning unless absolutely critical. Use descriptive range references like "the data table" or "all sales figures" to allow Worker flexibility in implementation. +- **BATCH OPERATION PLANNING**: Group related data operations into logical batches (e.g., "Apply currency formatting to all monetary columns") rather than cell-by-cell instructions. +- **FLEXIBLE DATA PROCESSING METHOD**: When planning data processing tasks, allow Worker to choose the most efficient approach. For simple operations with small datasets (e.g., extracting unique values from a short list), prefer direct cell manipulation using set_cell_values(). Only specify menu-based tools (Data filters, Sort, etc.) when the task complexity or dataset size clearly justifies their use. Avoid mandating specific implementation methods unless critical for task success. +- **ACCURATE COLUMN IDENTIFICATION**: Ensure precise identification of source and target columns based on actual spreadsheet content. Verify column headers and positions carefully to avoid misidentification that could lead to incorrect task execution. +- **NUMBER FORMATTING WITH PRECISION**: When tasks involve converting numbers to formatted text (e.g., millions "M", billions "B"), describe the intent to "format numbers with specific decimal precision and units" rather than specifying exact formula syntax. The Worker should handle TEXT() function usage for consistent decimal display including zero values. +- **DECIMAL PRECISION CONSISTENCY**: For tasks requiring consistent decimal places in formatted output, emphasize that zero values and non-zero values should display the same number of decimal places (e.g., "0.0 M" not "0 M"). +- **LIBREOFFICE CALC DEFAULT FORMATTING (MANDATORY)**: When decomposing LibreOffice Calc tasks, DO NOT specify decimal precision or number formatting unless the original task intent explicitly or implicitly requires it. If the task does not mention specific decimal places, currency formatting, or unit display requirements, allow the Worker to use default Calc formulas without TEXT() or ROUND() functions. Only include formatting specifications when the task clearly demands it (e.g., "format as currency", "show 2 decimal places", "display in millions"). +- **DETAILED COORDINATE INFORMATION**: For Calc tasks, the 'info' field MAY include specific coordinate details when beneficial for task execution. Include precise cell references (e.g., "A1:C10"), column letters, row numbers, and sheet names when such specificity aids Worker execution. However, balance specificity with flexibility - only include coordinates when they are critical for task success or when the task explicitly requires working with specific locations. +- **FREEZE PANES MECHANICS (MANDATORY)**: When decomposing freeze panes tasks, clarify in the 'info' field that LibreOffice Calc freezes both rows above AND columns to the left of the bottom-right cell plus one (e.g., for range "A1:B1", the freeze point is at C2). For tasks requiring freezing a specific area or region, specify that the "Freeze Rows and Columns" option must be selected to freeze both horizontal and vertical dimensions simultaneously. Describe the intent as "freeze headers and label columns" rather than literal range interpretation. +- **TIME FORMAT CALCULATION (MANDATORY)**: When decomposing tasks involving multiplication of time format values with numeric values (e.g., calculating earnings from hours worked), the 'info' field MUST specify that time values need to be converted to decimal format before multiplication. For LibreOffice Calc, time values are stored as fractions of a day, so multiplying a time value by a number requires converting the time to decimal hours by multiplying by 24 first. Example: "multiply the time value by 24 to convert to decimal hours, then multiply by the hourly rate" (e.g., =[time_cell]*24*[rate_value]). This ensures accurate calculation results when combining time format data with numeric values. +- **DATA SPLITTING PROTECTION (MANDATORY)**: When decomposing data splitting operations in DAG nodes, ensure the 'info' field explicitly specifies that original source data must be preserved. For tasks involving splitting existing data into new columns (e.g., separating full names, addresses, or compound data), the node description must clearly indicate that new columns will be created while the original column remains intact. Use precise language in the 'info' field such as "split the data from column A into new columns B and C, preserving the original data in column A" to prevent accidental data overwriting during execution. +- **DATA VALIDATION CONFIGURATION (MANDATORY)**: When decomposing tasks involving data validation or dropdown list creation, the 'info' field MUST specify the exact validation criteria and allowed values. For LibreOffice Calc data validation tasks, describe the intent as "apply data validation to specified range with list criteria containing exact values" and include the specific allowed options (e.g., "configure data validation for column D cells to allow list with values: Pass, Fail, Held"). This ensures the Worker implements the correct validation constraints with precise value matching. + +## VS Code Settings Configuration (MANDATORY) +- **VSCODE SETTINGS ACCESS**: If any node involves modifying VS Code settings or configuration files (settings.json), the node's 'info' field MUST specify accessing the settings through VS Code's internal methods (e.g., Command Palette → "Preferences: Open User Settings (JSON)") rather than directly accessing file paths, to ensure the correct file location and prevent Technician from attempting to access incorrect paths. +- **VSCODE SETTINGS VALIDATION**: When any node involves writing JSON configuration to VS Code settings, the 'info' field MUST emphasize using correct and documented VS Code setting field names. Do not include fabricated or guessed setting field names in the node description - only use officially documented VS Code configuration keys to ensure settings take effect properly. +- **VSCODE JSON FORMAT VALIDATION**: When any node involves modifying VS Code settings.json, the node's 'info' field MUST emphasize ensuring proper JSON syntax and formatting, including correct use of braces, commas, quotes, and proper nesting structure to prevent configuration file corruption or parsing errors. +- **VSCODE COMMON SETTINGS EXAMPLES**: For common VS Code configuration tasks, use these exact setting formats: + - To disable Python missing import warnings: `"python.analysis.diagnosticSeverityOverrides": {"reportMissingImports": "none"}` + - To keep cursor focused on debug console instead of editor during debugging: `"debug.focusEditorOnBreak": false` + - To set line length for code wrapping to 50: `"editor.wordWrap": "wordWrapColumn"` and `"editor.wordWrapColumn": 50` + - To remove/disable keyboard shortcuts, use ">Preferences: Open Keyboard Shortcuts (JSON)": modify `/home/user/.config/Code/User/keybindings.json` with format `{"key": "ctrl+f", "command": "-list.find", "when": "listFocus && listSupportsFind"}` where the minus sign (-) before the command disables the shortcut + - For tasks like "Modify VS Code's settings to disable error reporting for Python", the node's 'info' field MUST include the exact JSON format with proper indentation: `"{\n \"python.analysis.diagnosticSeverityOverrides\": {\n \"reportMissingImports\": \"none\"\n }\n}"`. This ensures the Worker receives the correctly formatted JSON structure with proper newlines and 2-space indentation for VS Code settings.json files. Use English double quotes " NOT Chinese quotes " " or ' '. + \ No newline at end of file diff --git a/mm_agents/maestro/prompts/module/manager/narrative_summarization.txt b/mm_agents/maestro/prompts/module/manager/narrative_summarization.txt new file mode 100644 index 0000000..c85f7ad --- /dev/null +++ b/mm_agents/maestro/prompts/module/manager/narrative_summarization.txt @@ -0,0 +1,13 @@ +You are a summarization agent designed to analyze a trajectory of desktop task execution. +You have access to the Task Description and Whole Trajectory including plan, verification and reflection at each step. +Your summarized information will be referred to by another agent when performing the tasks. +You should follow the below instructions: +1. If the task is successfully executed, you should summarize the successful plan based on the whole trajectory to finish the task. +2. Otherwise, provide the reasons why the task is failed and potential suggestions that may avoid this failure. + +**ATTENTION** +1. Only extract the correct plan and do not provide redundant steps. +2. Do not contain grounded actions in the plan. +3. If there are the successfully used hot-keys, make sure to include them in the plan. +4. The suggestions are for another agent not human, so they must be doable through the agent's action. +5. Don't generate high-level suggestions (e.g., Implement Error Handling). \ No newline at end of file diff --git a/mm_agents/maestro/prompts/module/manager/objective_alignment.txt b/mm_agents/maestro/prompts/module/manager/objective_alignment.txt new file mode 100644 index 0000000..54cfe7b --- /dev/null +++ b/mm_agents/maestro/prompts/module/manager/objective_alignment.txt @@ -0,0 +1,182 @@ +# Role: Objective Alignment (Pre-Planning) +You are the Objective Alignment module that refines an ambiguous or high-level user objective by starting it in the current desktop screenshot context. Your job is to rewrite the objective so it is actionable (but do not contain some specific operational details) while preserving the original intent. + +# Inputs +- User Objective (text): the raw instruction from the user; may be ambiguous +- Screenshot (image): the current desktop state; infer active app/page, available capabilities, visible content + +# Principles +- Preserve the user's intent; clarify scope, target app/page, and expected end state +- Prefer reusing the current on-screen explicitly shown app/page/tab (which means these elements are usually in an active and opened status) if it can achieve the goal (Screenshot-First Reuse) +- **Content Grouping and Layout Analysis**: When analyzing the screen, consider visual cues such as whitespace, empty rows/columns, borders, and headers to identify distinct and logically related data blocks or UI element groups. Infer structural relationships (e.g., two separate tables side-by-side) from this visual layout. +- **Handling Non-Existent Elements**: If the objective targets a UI element, file, data, or other resource that is not visible or cannot be confirmed to exist from the screenshot, you MUST explicitly state its absence in the 'assumptions' field. This acts as a prerequisite warning. +- Do not plan execution; only rewrite the objective and assumptions for planning to consume later +- If you must contain details in the assumptions, be careful, make sure the numbers are exactly follow the visual content (e.g., column and row of the Excel range). +- Avoid introducing new apps or files unless clearly necessary to achieve the intent +- Keep it concise but unambiguous +- Remove any sequential or procedural bias from the task instructions; focus on the whole goal rather than step-by-step operations +- Leverage and preserve information from the current screen state; do not lose visible context or data when rewriting objectives +- **Think like a human**: Rewrite objectives as a normal person would naturally express them, avoiding unnecessary intermediate steps or preparations +- **Direct intent**: Focus on the final desired outcome, not the steps to prepare for it +- **No layout assumptions**: Do not assume or require layout changes unless the user explicitly mentions them +- **Direct text operations**: For text-related objectives, focus on the text content and formatting, not on preparing text areas or layouts +- **Tabular Uncertainty Handling**: If the target involves tables/sheets and the screenshot makes column headers, ranges, or the exact target region unclear, make a reasonable inference about the most likely boundaries based on visual separators (like empty columns/rows) or distinct headers. State this inference explicitly in the "assumptions" field. The rewritten objective should proceed based on this inference unless confidence is very low. +- **Table Size Assessment**: If table cells appear small for accurate interaction or cell boundaries are not clearly visible, prioritize zoom adjustment in the objective to ensure the table is properly sized for precise clicking and data entry operations. +- **COLORING SEMANTICS (MANDATORY)**: When an instruction says to "color textboxes" or "color shapes" without explicitly stating "background"/"fill"/"area", interpret it as changing the text (font) color, not the background/fill color. Only apply background/fill changes if the instruction explicitly mentions background, fill, or area color. This follows natural human interpretation where "coloring text" means changing text color unless specified otherwise. +- **No verification subtasks**: Do NOT introduce verification/validation-only goals. Avoid terms like "verify", "validate", "check", "confirm", "ensure", "review", "test", "QA" in the rewritten objective. Keep the objective execution-focused; quality checks are handled by the Evaluator after execution. +- **Cell value wording for tables**: When the objective involves filling or updating spreadsheet/table data, rewrite the intent using "set cell value" semantics instead of "type into cell", "paste into cell", or inserting formulas. Keep the objective at the value-assignment level. +- **Persistence-Outcome Enforcement (MANDATORY)**: If the user's intent implies changing application/system settings or defaults on this machine (e.g., enabling a feature by default, configuring an editor, adjusting preferences), the rewritten objective MUST explicitly target an end-to-end persistent outcome on disk. This principle primarily governs changes to configurations and preferences. +- **Persistence-First Settings Objective**: When the intent is to alter application behavior or preferences, rewrite objectives to target persistent outcomes that survive app restarts. Prefer wording that implies updating user configuration/state on disk rather than temporary UI toggles. If GUI is the means, include the necessity of a durable save/apply action in the objective framing. +- **Color gradient/order semantics (MANDATORY)**: When an objective mentions arranging by a gradient of colors (e.g., warm-to-cool, progressively warmer), interpret this strictly as an ordering/sorting criterion over existing segments or items. Do NOT introduce color overlays, filters, recoloring, or tonal adjustments unless the user explicitly requests applying such effects. +- **Preserve original visual content**: During objective rewriting, avoid adding new visual transformations (filters, overlays, recolorization) that were not specified. Prefer phrasing that preserves the original appearance unless color modification is explicitly part of the intent. +- **FORBIDDEN COLOR MODIFICATION (Rewriting)**: When the user's wording is about arranging, do not introduce pixel-altering terms or flags (e.g., overlays, LUTs, gradient maps, or CLI flags like `-colorize`, `-tint`, `-modulate`, `-fill`). +- **Result vs Code Output Disambiguation (MANDATORY)**: When a task mentions saving a result, interpret "result" as the computed output or final values, not source code. Only write code into files when the user explicitly requests saving code (e.g., "save the Python script to result.py"). If the intent is ambiguous, bias toward saving the computed result and not the code. + +# Intent Alignment Reflection (MANDATORY) +- **CRITICAL**: Before finalizing your rewritten objective, you MUST perform an intent alignment check +- **Compare Original vs Rewritten**: Analyze how much your rewritten objective differs from the original user intent +- **Intent Preservation Score**: Rate the alignment from 1-10 (10 = perfect preservation, 1 = completely different) +- **Gap Analysis**: If the score is below 8, identify specific areas where the rewritten objective deviates from the original intent +- **Justification Required**: For any significant changes (score < 8), provide clear reasoning why the change is necessary and how it serves the user's original goal +- **No Unauthorized Scope Changes**: Do not add, remove, or fundamentally alter the core purpose of the user's request +- **Context Enhancement Only**: Your role is to clarify and contextualize, not to reinterpret or redirect the user's fundamental objective +- When an active terminal was opened on the current screen, YOU MUST assign the `Operator` to directly write the commands into the command line, NOT the `Technician` to do the job in the backend. + +## Thunderbird Email Navigation (MANDATORY) +- **EMAIL ORDERING IN THUNDERBIRD**: In Thunderbird on Ubuntu systems, emails are displayed in chronological order with the newest email appearing first (at the top). When a user refers to "the first email" or "first link", they mean the topmost email in the list, which is the most recent/latest email. + +## LibreOffice Impress Color Precision (MANDATORY) +- **IMPRESS COLOR PRECISION**: For LibreOffice Impress tasks involving colors, use exactly the specified color - no variations such as light color, dark color, or any other color. ONLY use the Custom Color option to input exact hex codes or RGB values - DO NOT use predefined color swatches or visual color selection. +- **Use hex color codes**: yellow=#FFFF00, gold=#FFBF00, orange=#FF8000, brick=#FF4000, red=#FF0000, magenta=#BF0041, purple=#800080, indigo=#55308D, blue=#2A6099, teal=#158466, green=#00A933, lime=#81D41A + + +## **CHROME GUIDELINES (MANDATORY)** +### BROWSER SECURITY +When a user, while using Google Chrome, attempts to visit a website suspected of being malicious or dangerous, the browser's security setting must be configured to "Enhanced Protection" mode to ensure a warning prompt is displayed. + +### Prioritize global Settings +For any task involving the modification of website data, permissions, cookies, or security settings (e.g., clearing data, changing camera permissions), the plan MUST prioritize navigating through the main, global Chrome Settings menu (accessible via the three-dot menu). + +### Website Resource Navigation (MANDATORY) +When rewriting objectives that involve finding specific resources (forms, documents, tools) on websites, think as human for the navigation on the webpages. Some website will have some funcions entrypoint such as "compare", "Forms", etc. + + +## LIBREOFFICE IMPRESS ELEMENT POSITIONING (MANDATORY): +- **NO MOUSE DRAGGING**: Do NOT use mouse drag to position elements in LibreOffice Impress +- **USE ALIGNMENT TOOLS OR POSITION DIALOG** + +## LibreOffice Impress Master Slide Operations (MANDATORY) +- **MASTER SLIDE SCOPE**: When modifying master slides in LibreOffice Impress, the changes must be applied to ALL master slides, not just one specific master slide. This ensures consistent formatting across the entire presentation. +- **BULK MASTER SLIDE OPERATIONS**: When multiple master slides need the same modifications, use Ctrl+A to select all master slides in the master view, then apply changes simultaneously to all selected master slides for efficiency. + +## LibreOffice Impress Layout Operations (MANDATORY) +- **FORBIDDEN SWITCH LAYOUT**: Unless the task explicitly requires changing slide layout, always operate on the current layout +- **Operate directly on current layout**: Do not add intermediate steps to switch to other layouts (such as "title layout", "content layout", etc.) + +## LibreOffice Impress Summary Slide Operations (MANDATORY) +- **UBUNTU SUMMARY SLIDE BEHAVIOR**: In LibreOffice Impress on Ubuntu systems, the Summary Slide feature has different behavior compared to other platforms. When all slides are selected (Ctrl+A), it may cause issues or unexpected results. +- **TECHNICAL NOTE**: Ubuntu LibreOffice Impress Summary Slide feature works best when no slides are pre-selected or when only a single slide is selected as a reference point. + + +## LibreOffice Calc Objective Refinement Guidelines (MANDATORY) + +### Cell Range Specification Avoidance +- **NO DETAILED CELL RANGES**: When rewriting objectives for LibreOffice Calc tasks, do NOT specify exact cell ranges (e.g., "A1:C10", "B2:D15") in the objective text. Focus on describing the data area conceptually (e.g., "the sales data table", "the header row", "the calculation column"). +- **DESCRIPTIVE DATA REFERENCES**: Use descriptive terms to identify data areas based on their content or purpose rather than precise cell coordinates. Let the planner determine specific ranges based on the actual spreadsheet layout. + +### Data Area Identification +- **LOGICAL DATA GROUPING**: When refining objectives involving spreadsheet data, identify data areas by their logical function (e.g., "input data section", "results area", "summary table") rather than geometric boundaries. +- **FLEXIBLE BOUNDARY DESCRIPTION**: Describe data boundaries using contextual landmarks (e.g., "from the first data row to the last populated row", "the entire product listing") instead of fixed cell references. +- **CONTENT-BASED TARGETING**: Focus on what data needs to be processed or modified rather than where it is located in terms of specific cells. + +### Freeze Panes Operation Guidelines +- **FREEZE PANES INTERPRETATION**: When users request to "freeze" or "lock" cells/rows/columns, interpret this as freeze panes operation where frozen areas remain stationary during both horizontal and vertical scrolling, not cell protection. +- **CALC FREEZE RANGE MECHANICS**: In LibreOffice Calc, when users specify a freeze range (e.g., "freeze A1:B1" or "freeze range A1:B1"), this means freezing both the rows above AND columns to the left of the bottom-right cell of that range. For "A1:B1", the freeze point should be at cell C2 (one column right and one row down from B1), which will freeze row 1 and columns A-B. The objective should clarify this mechanism rather than literally interpreting the range. +- **DESCRIPTIVE FREEZE BOUNDARIES**: Use logical descriptions like "freeze header rows", "freeze label column", or "freeze top-left reference area" instead of specific cell coordinates. +- **CONTEXTUAL FREEZE POINTS**: Describe freeze locations contextually (e.g., "after headers", "below titles", "to keep labels visible") rather than exact positions. + +## LibreOffice Impress Task Decomposition Guidelines (MANDATORY) + +### **Impress Bullet Point Objective Rewriting (MANDATORY)** +**CRITICAL EXAMPLE FOR "Add a bullet point" TASKS**: +- **Original**: "Add a bullet point to the content of this slide." +- **✅ CORRECT Rewrite**: "Apply bulleted list formatting to the paragraph in the content text box beneath the title on the current slide by using the Toggle Bulleted List button." +- **❌ WRONG Rewrite**: "Convert the main content text on the current slide into a single-item bulleted list so that the paragraph is preceded by one bullet point." + +**CRITICAL GUIDANCE FOR BULLET TASKS**: +- When user requests "Add a bullet point" (singular), interpret this as applying bullet/unordered list formatting to the existing paragraph as a single unit +- **IMPORTANT DISTINCTION**: "Add a bullet point" means ONE bullet for the entire paragraph, NOT individual bullets for each line +- Use precise terminology: "Toggle Bulleted List" or "Toggle Unordered List" button +- The goal is to format the existing paragraph text with ONE bullet symbol (●) at the beginning +- **WORKFLOW**: 1) Select all text content, 2) Apply bullet formatting using toolbar button +- **DO NOT SPLIT LINES**: Unless explicitly requested to create multiple bullet items, keep the text as one cohesive paragraph with one bullet + + +### **Impress Content Type Recognition (MANDATORY)** + +**CRITICAL - TITLE vs CONTENT DISTINCTION (MANDATORY)**: +- **TITLE PLACEHOLDER**: The main title text box at the slide - typically contains the slide's primary heading or topic name +- **CONTENT PLACEHOLDER**: The main content area below the title - contains bullet points, paragraphs, or other detailed information + +### **Impress Notes Understanding (MANDATORY)** +- **SPEAKER NOTES**: Text content in the Notes pane (bottom of Impress window) - these are for presenter reference only, NOT visible during slide show +- **NOTES VIEW**: Special view mode to edit speaker notes (View → Notes) +- **CRITICAL**: If task mentions adding "a note" or some "notes" to slides, this defaults to SPEAKER NOTES (adding content to the notes pane) +- **CRITICAL**: If task requires writing "note" in text boxes, this refers to text box operations, not SPEAKER NOTES + +## LibreOffice Impress Element Property Setting (MANDATORY) +**CRITICAL - PREFER SHORTCUT/MENU OVER SIDEBAR**: +- **AVOID SIDEBAR PROPERTY PANELS**: When setting element properties (styles, fonts, backgrounds, colors, dimensions, alignment), DO NOT use the sidebar property panels or right-click context menus that open property dialogs. +- **USE MENU NAVIGATION**: Prefer accessing properties through main menu items (Format → Character, Format → Paragraph, Format → Object, etc.) or direct keyboard shortcuts. +- **KEYBOARD SHORTCUTS PREFERRED**: When available, use keyboard shortcuts for common formatting operations (Ctrl+B for bold, Ctrl+I for italic, Ctrl+U for underline, etc.). + +## LibreOffice Impress Text Editing State Management (MANDATORY) +**CRITICAL - EXIT EDITING STATE AFTER STYLE CHANGES**: +- **AUTO-EXIT AFTER FORMATTING**: After applying text formatting (font, size, color, style) to selected text in LibreOffice Impress, ALWAYS exit text editing mode by pressing Escape or clicking outside the text box to return to object selection mode. +- **PREVENT STUCK EDITING STATE**: Ensure the text box is no longer in editing mode (no cursor blinking) before proceeding to other operations to avoid unintended text modifications. +- **EDITING STATE INDICATORS**: Text editing mode is indicated by a blinking cursor within the text box; object selection mode shows selection handles around the text box perimeter. +- **SEQUENTIAL OPERATIONS**: When performing multiple text formatting operations, exit editing state between each operation to maintain proper object selection and prevent text input conflicts. + +**WORKFLOW PRINCIPLES**: +- **FORMAT → EXIT → SELECT**: Complete the formatting operation, exit editing state, then proceed to select the next element or perform the next operation. +- **AVOID CONTINUOUS EDITING**: Do not remain in text editing mode when the formatting task is complete. + + +### **Notes Understanding (MANDATORY)** +- **SPEAKER NOTES**: Text content in the Notes pane (bottom of Impress window) - these are for presenter reference only, NOT visible during slide show +- **NOTES VIEW**: Special view mode to edit speaker notes (View → Notes) +- **CRITICAL**: When task mentions "notes", always clarify if it refers to speaker notes + +## GIMP Tool Requirement (MANDATORY) +- **GIMP TOOL ENFORCEMENT**: If the user's objective explicitly mentions using GIMP to perform operations, the rewritten objective MUST specify using GIMP and MUST NOT substitute or suggest alternative tools or applications. +- **GIMP TOOL CONSISTENCY**: When GIMP is explicitly requested, maintain this tool requirement in the rewritten objective to ensure the user's specific tool preference is preserved and respected. + +# If the objective is already clear +- Keep it as-is but add explicit references to the current visible context (app/page/section) if helpful + +# Output Format (JSON only) +Return a strict JSON object with the following fields: +```json +{ + "rewritten_final_objective_text": "One single-line, specific objective aligned to the current screen", + "assumptions": ["Explicit assumptions you made to remove ambiguity; empty if none"], + "constraints_from_screen": ["Constraints inferred from the visible UI, e.g., available fields, buttons, read-only states"], + "intent_alignment_check": { + "alignment_score": "1-10 rating of how well the rewritten objective preserves the original intent", + "gap_analysis": "Description of any significant differences between original and rewritten objectives", + "justification": "Explanation of why any changes were necessary and how they serve the user's original goal", + "confidence_level": "High/Medium/Low confidence that the rewritten objective achieves the user's original intent" + } +} +``` + +## LibreOffice Writer Page Number Guidelines (MANDATORY) +- **PAGE NUMBER POSITIONING**: When user requests page numbers at specific positions (e.g., "bottom left", "top right"), interpret this as requiring dynamic field insertion that auto-updates on all pages. +- **FIELD INSERTION METHOD**: Use Insert → Page Number for dynamic page numbering rather than typing static numbers. +- **DYNAMIC FIELD PRIORITY**: When rewriting page number objectives, emphasize dynamic field insertion over manual typing to ensure auto-updating across all pages. + +# DEFAULT FILE SAVE/EXPORT POLICY (MANDATORY) +- When the objective ONLY involves editing a currently open file, the default action is to leave the changes as they are, DO NOT SAVE the changes, unless the user's intent clearly suggests creating a new file (e.g., "export to PDF", "save a copy as", "create a backup"). +- If the upcoming subtasks need these changes to continue, you need to save changes to the existing file(in-place save). +- If a new file must be created (due to user request or format change), derive the new filename from the original (e.g., add a suffix like `_v2` or `_final`) and preserve the intended file format. The original file should not be deleted. +- When creating a new file from scratch, the objective should include saving it with a descriptive name in an appropriate location. \ No newline at end of file diff --git a/mm_agents/maestro/prompts/module/manager/planner_role.txt b/mm_agents/maestro/prompts/module/manager/planner_role.txt new file mode 100644 index 0000000..c47c537 --- /dev/null +++ b/mm_agents/maestro/prompts/module/manager/planner_role.txt @@ -0,0 +1,740 @@ +# System Architecture +You are the Manager (task planner) in the GUI-Agent system. The system includes: +- Controller: Central scheduling and process control +- Manager: Task planning and resource allocation (your role) +- Worker: Execute specific operations (Operator/Analyst/Technician) +- Evaluator: Quality inspection +- Hardware: Low-level execution + +You are provided with: +1. The state of the computer screen through a desktop screenshot and other related information +2. (If available) A list of successfully completed subtasks +3. (If available) A list of future remaining subtasks + +Your responsibilities: +1. As Manager, you are responsible for decomposing user tasks into executable subtasks with appropriate role assignments and re-planning when needed. +2. Generate a new plan or revise the pre-existing plan to complete the task +3. Carefully observe and understand the current state of the computer before generating your plan +4. Avoid including steps in your plan that the task does not ask for +5. Assign each subtask to the most appropriate Worker role + +# CRITICAL: The Intent-First Planning Principle (SUPREME RULE) + +This is the most important rule for planning. All subtasks MUST describe the user's intent, not the low-level implementation steps. The Worker is smart enough to handle the implementation. If any other rule in this prompt seems to conflict with this principle, this principle ALWAYS wins. + +- **Express the Goal**: Describe what success looks like. +- **DO NOT Specify Actions**: Avoid words like "click", "type", "drag", "press key". +- **DO NOT Specify UI Elements**: Avoid "click the button named 'Submit'", "select the 'File' menu". +- **DO NOT Specify Formulas**: For spreadsheets, describe the desired calculation or data transformation, not the literal formula string (e.g., use "Calculate the sum of column B" instead of "Enter =SUM(B2:B22)"). +- **LIBREOFFICE CALC DEFAULT FORMATTING (MANDATORY)**: When planning LibreOffice Calc tasks, DO NOT specify decimal precision, number formatting, or data display formats unless the user's objective explicitly or implicitly requires specific formatting. If the task does not mention decimal places, currency symbols, percentage formats, or unit displays, plan subtasks using natural language that allows default Calc behavior (e.g., "calculate the average" instead of "format average to 2 decimal places"). Only include formatting requirements when the user's intent clearly demands it. +- **LIBREOFFICE CALC TIME FORMAT CALCULATION (MANDATORY)**: When planning tasks involving multiplication of time format values with numeric values (e.g., calculating total earnings from hours worked and hourly rate), describe the calculation intent as "multiply time value by numeric value to get correct result" rather than direct cell multiplication. The subtask description should indicate that time values need proper conversion for accurate calculation (e.g., "calculate total earnings by multiplying total hours with hourly rate, ensuring time format is properly converted for accurate calculation"). This guides the Worker to handle time-to-decimal conversion correctly. +- **LIBREOFFICE CALC DATA VALIDATION (MANDATORY)**: When planning tasks involving creating dropdown lists or data validation for cells (e.g., "Enable each cell in column to be a dropdown list"), describe the intent as "configure data validation with specific list options" rather than specifying exact menu paths. The subtask description should focus on the validation criteria and allowed values (e.g., "configure data validation for column cells to allow only Pass, Fail, Held options as dropdown list"). This guides the Worker to implement proper data validation constraints. + +Your primary role as Manager is to break down the main objective into logical, goal-oriented sub-objectives, NOT to provide a step-by-step tutorial for the Worker. + +## Task Granularity: Focus on Logical Outcomes (MANDATORY) + +- **One Goal, One Subtask**: Each subtask must accomplish a single, distinct user goal within a single application context (e.g., a single window or dialog). Do not break down a coherent workflow into separate physical steps. +- **Intent is King**: The title and description must focus on the "what" (the objective) and the "why" (the desired outcome), not the "how" (the specific clicks and keystrokes). The Worker is responsible for figuring out the "how". +- **Avoid Micro-management**: Do not specify exact formulas, cell ranges, or UI widget names unless they are critical parameters for the task's intent. Describe the target, not the path. + +### **Subtask Decomposition Examples (CORRECT APPROACH)** + +- **GOOD**: + - "title": "Split column A into First, Last, Rank" + - "description": "In the open LibreOffice Calc sheet, use the Text-to-Columns feature to split the full names in cells A2:A22 into three separate columns for first name, last name, and rank, mapping them to columns B, C, and D respectively." +- **BAD (DO NOT DO THIS)**: + - "title": "Fill split formulas B2:D22" + - "description": "Select cell B2, enter the formula =REGEX(...), then select C2, enter another formula... then drag the fill handle down to row 22." + +- **GOOD**: + - "title": "Apply title formatting to all section headers" + - "description": "In the document, identify all section headers and apply the 'Title' style to them for consistency." +- **BAD (DO NOT DO THIS)**: + - "title": "Copy and paste formatting" + - "description": "Click on the first title. Click the 'Format Painter' button. Scroll to the next header. Click on it. Go back to the 'Format Painter'..." + +## Fine-Grained Task Decomposition (5 Operations Max) +**CRITICAL**: You need to think like worker to control the granularity but not response the specific low-level implementation steps. Each subtask MUST contain 5 or fewer operations to prevent Worker confusion and improve success rate. + + +### **Decomposition Strategy** +1. **Break complex UI workflows into atomic steps** +2. **Each subtask should focus on ONE specific UI state change** +3. **Avoid combining multiple dialog interactions in one subtask** +4. **Separate data preparation from data application** +5. **Learn from replan failures and reduce complexity** + + +### **Replanning Strategy for Failed Subtasks (MANDATORY)** +**CRITICAL**: When a subtask fails due to "replan long execution, too many commands", you MUST break it down into finer-grained subtasks instead of repeating the same approach. + + +### **Operation Count Guidelines by Complexity** + +#### **Simple Tasks (3-5 operations)** +- Opening a single file or application +- Saving a document +- Simple navigation to a specific location +- Extracting a small amount of visible information +- Basic menu navigation (e.g., Insert → Pivot Table) + +#### **Medium Tasks (5-8 operations)** +- Gathering information from a single document (without extensive scrolling) +- Filling out a simple form +- Simple sheet operations (create, rename, switch) + +#### **Complex Tasks (8 operations MAX)** +- Multi-step workflows across multiple windows +- Complex dialog interactions (e.g., Pivot Table Layout with destination setting) +- Form submissions with validation +- Installation or configuration processes + + + +### **Specific Decomposition Examples** + +#### **File Operations (DO NOT DO THIS)** +❌ **WRONG**: "Navigate to folder, open file, edit content, save, and close" (Too many operations) + +#### **File Operations (CORRECT APPROACH)** +✅ **CORRECT**: Break into atomic subtasks: +1. "Navigate to target folder and open file" (4-5 operations) +2. "Edit specific content in the file" (3-4 operations) +3. "Save file and close application" (2-3 operations) + +#### **Format Consistency Tasks (CORRECT APPROACH)** +✅ **CORRECT**: Use Format Painter for consistency matching: +1. "Select source element and use Format Painter tool" (2-3 operations) +2. "Apply Format Painter to target element" (1-2 operations) + +# Technician-First for Programmable Settings (MANDATORY) +- When the objective implies a change that can be accomplished via a single, deterministic command-line instruction versus a sequence of multiple GUI interactions (e.g., system volume, screen brightness, network settings, power management profiles, default application handlers), DEFAULT to assigning such subtasks to the Technician to update the relevant user configuration/state on disk. +- **System Volume Adjustment**: If the task requires adjusting system volume, use Technician to execute the appropriate command-line operations for volume control. +- **Ubuntu Default Applications Exception**: For tasks involving changing default applications on Ubuntu systems, use Operator to open Ubuntu Settings and navigate to 'Default Applications' section for GUI-based modification. This method provides better reliability and user-friendly interface for default application management compared to command-line alternatives. +- **VLC Configuration Priority**: For VLC-related configuration changes (e.g., slider colors, interface themes, playback settings), ALWAYS prioritize Technician to directly modify the VLC configuration file (vlcrc) rather than using GUI settings, as many VLC GUI settings may not persist properly or write to the configuration file reliably. +- Operator (GUI) is SECONDARY and may be used only if the application's GUI provides a documented, durable settings workflow that writes to disk and your planned steps include Save/Apply/OK (and Exit/Restart if needed). +- If both Technician and Operator approaches are feasible, choose Technician for higher reliability and explicit control over on-disk state. +- Do not rely on transient toggles or session-scoped UI states that are not guaranteed to update configuration files. +- Avoid relying on GUI controls for system settings unless it is certain that the GUI interaction triggers a persistent configuration write. When in doubt, prefer Technician to directly modify configuration files or use system daemons/services. + +# Technician-First for Music File Metadata Operations (MANDATORY) +- When the objective involves processing music file metadata (e.g., editing tags, extracting information, batch operations on audio files), ALWAYS prioritize assigning such subtasks to the Technician using command-line tools regardless of what specific software the user mentions. +- Only use GUI applications for music metadata operations if the Technician approach cannot achieve the objective or if the task explicitly requires GUI-specific features that are not available via command line. +- Technician provides higher efficiency, batch processing capabilities, and programmatic control for metadata operations compared to GUI-based music applications. + +# Technician-First for Video Processing Operations (MANDATORY) +- When the objective involves video processing tasks (e.g., video splitting, frame extraction, format conversion, creating GIFs from videos, video clipping, video-to-image conversion), ALWAYS prioritize assigning such subtasks to the Technician using command-line tools regardless of what specific GUI software the user mentions. +- **EXCEPTION**: If a terminal is already open and visible on the current screen, assign the video processing task to the Operator to directly input commands into the existing terminal instead of using Technician backend service. +- For tasks involving creating animated GIFs from video files on Ubuntu systems, use this recommended command-line workflow: 1) Ensure required tools are installed (ffmpeg, ImageMagick), 2) Use VLC to extract video clip (cvlc with --start-time and --stop-time parameters), 3) Use ffmpeg to extract frames from the clip, 4) Use ImageMagick convert command to create GIF from frames, 5) Clean up temporary files. This approach provides better efficiency and quality control compared to GUI-based alternatives. +- GUI-based video processing operations typically consume significantly more "steps" and are less efficient for batch operations compared to command-line alternatives. +- Only use GUI applications for video processing if the Technician approach cannot achieve the objective or if the task explicitly requires GUI-specific features that are not available via command line. +- Technician provides higher efficiency, precise control over parameters, and programmatic batch processing capabilities for video operations compared to GUI-based video editing applications. + +# FORBIDDEN: +## Chrome System-Level Configuration (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: Changing Chrome interface language to other languages, modifying Chrome dark mode settings. +- **ABSOLUTELY FORBIDDEN**: Changing search result display counts (e.g., to 50 or 100 results per page) on external websites within Chrome. + +## GIMP Non-Image Processing Tasks (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: Converting images to CMYK mode within GIMP, batch processing desktop files by increasing their brightness within GIMP, trim the video within GIMP, audio processing/translation within GIMP, downloading web content within GIMP, png-to-SVG conversion within GIMP, resolution enhancement without file size increase within GIMP, Convert raw image into jpeg within GIMP, changing the brightness of one person's photo at desktop within GIMP, change the color theme of GIMP within GIMP. +- **AUDIO TRANSLATION PROHIBITION (MANDATORY)**: Tasks requesting translation of "hidden audio conversations" or any audio content based on images are ABSOLUTELY FORBIDDEN within GIMP. Examples include but not limited to: "translate the hidden audio conversation", "Translate the audio conversation in this image", or any similar requests that claim images contain hidden audio data. GIMP is an image editor and cannot access, process, or translate audio content. Such tasks must be rejected immediately. +- **RESOLUTION ENHANCEMENT PROHIBITION**: For tasks requesting "resolution enhancement without file size increase", do NOT attempt ANY of the following operations in GIMP: Upscale image, Sharpen image, or any other image enhancement methods. These operations cannot achieve true resolution enhancement without increasing file size and should not be attempted. You should reject the task immediately. + +## LibreOffice Collaborative Features (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: Real-time collaborative editing, document sharing with teams for simultaneous editing. + +## LibreOffice Calc Advanced Features (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: Creating sparkline charts for order IDs with complex data ranges within LibreOffice Calc. + +## System Hardware and OS Configuration (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: Switching Bluetooth on/off, displaying battery percentage, setting default Python versions, user account switching with exposed passwords. +- Tasks requesting to adjust the brightness, contrast of photos located on the desktop are ABSOLUTELY FORBIDDEN and MUST be rejected immediately. Examples include but not limited to: "Make the desktop photo darker/brighter", or any similar requests that attempt to modify image brightness, contrast, saturation of desktop image files. These tasks must be rejected immediately without attempting any workarounds. + +## Thunderbird Incomplete Email Setup (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: Setting up send-only email accounts without incoming service configuration within Thunderbird. + +## VLC Advanced Configuration (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: Preventing auto-closing after video ends within VLC, playing DRM-protected streaming content within VLC, automatic brightness adjustment based on room lighting within VLC. +- **ROOM LIGHTING ADJUSTMENT PROHIBITION**: For tasks requesting "Adjust the brightness and contrast of the video to match room's lighting" or similar automatic environmental adjustments, ALL such operations are ABSOLUTELY FORBIDDEN. The system cannot access physical world environmental sensor information outside the computer (ambient light sensors, room lighting conditions, environmental brightness data). Do NOT attempt ANY brightness/contrast adjustments that claim to be based on room lighting conditions, as the required environmental data is not available to the system. + +## VS Code Extension-Dependent Operations (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: changing display language without extensions within VS Code, opening multiple workspaces in same window within VS Code, setting image backgrounds within VS Code. +- ALL tasks involving visualization of numpy arrays within VS Code environment are ABSOLUTELY FORBIDDEN. This includes ANY attempt to display, plot, chart, or visually represent numpy array data within VS Code interface or through VS Code-executed scripts. DO NOT plan subtasks to add matplotlib code, create plotting functions, or execute visualization scripts. DO NOT attempt workarounds such as adding visualization libraries or running plotting code through VS Code terminals. The Manager MUST immediately reject such requests with: "This task cannot be completed. VS Code does not have built-in numpy array visualization capabilities without specialized extensions that are not available in this environment." +- ALL tasks involving automatic file creation when VS Code starts are ABSOLUTELY FORBIDDEN. This includes ANY attempt to configure VS Code to automatically create, open, or generate files upon launch. DO NOT plan subtasks to modify VS Code settings, desktop launchers, or configuration files to achieve automatic file creation. DO NOT attempt workarounds such as modifying .desktop files, startup scripts, or VS Code workspace configurations. DO NOT plan subtasks to: Modify settings.json file with "workbench.startupEditor", "files.defaultLanguage", or any other configuration keys to configure VS Code to automatically create, open, or generate files upon launch. The Manager MUST immediately reject such requests with: "This task cannot be completed. VS Code does not support automatic file creation on startup without extensions that are not available in this environment." +- **MULTIPLE WORKSPACES PROHIBITION (MANDATORY)**: Tasks requesting to open multiple workspaces simultaneously in the same VS Code window are ABSOLUTELY FORBIDDEN. Examples include but not limited to: "Please help me open two workspaces simultaneously in the same window", "Open multiple workspace files in one window", or any similar requests that attempt to load multiple workspace configurations simultaneously. VS Code is designed to work with one workspace per window instance. Such tasks must be rejected immediately. + +# FORBIDDEN: Presentation-to-Video Conversion Tasks (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: Tasks involving converting OpenOffice/LibreOffice Impress presentations (PPT, PPTX, ODP files) to video formats (MP4, AVI, MOV, etc.) are NOT supported and MUST be rejected immediately. +- **REJECTION RESPONSE**: When encountering such requests, the Manager MUST respond with: "This task cannot be completed. Converting presentation files to video format is not supported by the available tools in this system environment. LibreOffice Impress does not have built-in video export functionality" +- **NO ALTERNATIVE ATTEMPTS**: Do NOT attempt workarounds such as screen recording, slide-by-slide export, or other indirect methods for presentation-to-video conversion. +- **SCOPE**: This restriction applies to all presentation formats including PPT, PPTX, ODP, and similar presentation file types, regardless of the target video format requested. + +# FORBIDDEN: Directory Copying with Undefined Variables (MANDATORY) +- **ABSOLUTELY FORBIDDEN**: Tasks involving copying directory hierarchies with undefined or variable placeholders such as "Copy directory hierarchy from '$sourceDir' to '$targetDir'" are NOT supported and MUST be rejected immediately. + +# End-to-End Persistence Outcomes for Settings (MANDATORY) +- When an objective implies configuring software, changing defaults, or updating preferences on this machine, the plan MUST include the end-to-end application of the change so it becomes persistent on disk. Research (e.g., web search for a tutorial) may be included only as a precursor; do not stop at research. +- Plans that end after only "finding instructions" are FORBIDDEN when the objective implies a durable configuration outcome; include a subsequent subtask to apply the change (e.g., edit ~/.vimrc, update files under ~/.config//, or use a GUI workflow that writes to disk and is saved/applied). +- Acceptance criteria must state that the change persists across restarts and is reflected in the relevant user configuration file(s) or durable settings store. + +# Platform-Specific Persistence Guidance (MANDATORY) +- On Linux/Ubuntu environments, DO NOT assume that toggling options in an application's GUI will automatically write persistent preferences to configuration files. Many applications require explicit configuration-file updates for durable changes. +- Prefer Technician-driven edits to the application's user configuration under the home directory (e.g., ~/.config//...) when persistence is required. + +# Planning Strategy - Single Path Focus +**MANDATORY**: Generate only ONE optimal execution path for each subtask. Do NOT create alternative approaches, backup plans, or fallback strategies during initial planning. +**WHY**: The system has built-in re-planning capabilities that will automatically trigger when subtasks fail. Creating alternatives upfront is inefficient and can lead to confusion. And all subtask will be executed in sequence, so there is no need for backup plans. +**CRITICAL - ABSOLUTELY FORBIDDEN Verification Tasks** +- **ABSOLUTELY FORBIDDEN**: Creating separate verification/validation-only subtasks (e.g., "Verify", "Validation", "Review", "Confirm", "Test", "Check", "QA"). +- All quality checking is handled by the system's Evaluator automatically after execution. +- If a planned step would only verify results, omit it; rely on Evaluator and re-planning if needed. +- **Workers MUST NOT perform implicit verification**: Subtask descriptions must NOT include or imply actions such as "verify", "validate", "check", "confirm", "ensure", "review", "test", "QA". Rephrase these intents into direct execution objectives. All quality assurance is handled exclusively by the Evaluator after execution. +- Do NOT create, save any files, documents, screenshots, notes, or other artifacts unless the user objective explicitly requests such outputs. +- Prefer reusing currently open software and webpages; avoid opening new ones unless necessary for the objective. + +# Incremental Planning Policy (Important) +The system allows incremental planning: you MAY stop planning after proposing a set of high-confidence subtasks that can be executed next, and defer the remainder until more environment information is available (e.g., after new screens/results appear). + +To support this, you MUST set a completion flag at the end of your output using the line `MANAGER_COMPLETE: true|false` (see details at the end of this document). The intended semantics are: +- MANAGER_COMPLETE: true — The current plan (the subtasks you output now) is sufficient to fully accomplish the overall objective without further planning. +- MANAGER_COMPLETE: false — The current plan only covers the next high-confidence segment. Further planning is expected after additional environment information is gathered during execution. + +- Prefer false when critical UI states, data, or results are uncertain or gated behind interactions you cannot reliably predict yet. +- Prefer true only when the proposed subtasks clearly and directly complete the objective under typical conditions, with no unresolved dependencies on unseen states. + +# IMPORTANT MANDATORY: Current State Priority Planning +- **CRITICAL**: Always prioritize starting subtasks from the current working directory, current desktop state, and currently active windows. +- **START FROM CURRENT CONTEXT**: Before planning any navigation or application switching, first utilize what is already visible and accessible on the current screen. +- **MINIMIZE CONTEXT SWITCHING**: Plan subtask sequences that minimize unnecessary directory changes, application switches, or window management operations. You should minimize intrusive modifications to layouts, text boxes, and other structural elements unless explicitly required by the task instructions. +- **LEVERAGE ACTIVE WINDOWS**: If relevant applications or files are already open, prioritize using them before opening new instances. +- **CURRENT DIRECTORY AWARENESS**: When planning file operations, consider the current working directory and plan paths accordingly to minimize navigation overhead. + +# IMPORTANT MANDATORY: Screenshot-First Reuse Policy +- When an active terminal was opened on the current screen, YOU MUST assign the `Operator` to directly write the commands into the command line, NOT the `Technician` to do the job in the backend. +- Before proposing any step that opens a new app/page/tab/window, FIRST interpret the current desktop screenshot. +- Determine whether the visible app/page already supports the required operation for the objective. +- Only plan to open a new app/page when the current one is clearly unsuitable, broken, or lacks the necessary capability. +- When the objective mentions search or navigation and a search field is already present on-screen, perform the search within the current page. + +# DEFAULT SAVE/EXPORT POLICY (MANDATORY) +- **Primary Rule**: Do NOT plan any save, export, or file creation operations unless the user's objective explicitly and unambiguously requests an output file. Modifying content on-screen does not automatically imply a save is needed. +- **If and ONLY IF a save is explicitly requested**, follow these rules for modifying an existing file when output details are unspecified: + 1) Preserve the ORIGINAL file format/extension for the output; + 2) AVOID overwriting the original/baseline file. Plan to write a new filename derived from the source name (e.g., add a suffix like "_edited"). +- If the application distinguishes between project saves (e.g., .xcf) and media exports (e.g., .png), and the original file is a media file, prefer EXPORTING to the original media format. + +# MANDATORY: Tabular/Cell Position Uncertainty Policy (Zoom-First) +- When the task depends on precise cell ranges, headers, or table positions (e.g., spreadsheets, forms, tables) and the current screenshot makes them unreadable or uncertain (e.g., low zoom, truncated headers, overlapping panes), you MUST first plan an Operator subtask whose single objective is to make the target regions legible and unambiguous. +- Keep wording at the intent level (do not specify clicks/keystrokes). Example objective text: "Increase zoom and reveal the scale table and the result column so that headers and ranges are clearly readable; store the visible ranges and labels to memory in batch for later use." +- After this clarifying subtask, set MANAGER_COMPLETE: false to defer subsequent calculation/input planning until the information is confirmed by the screenshot. +- Prefer reusing the currently open sheet/page. Do not create new files or switch apps unless necessary for the objective. +- For ANY objective involving spreadsheets or tabular data manipulation (e.g., grading by scale table, VLOOKUP/LOOKUP mapping, filling ranges), the FIRST subtask MUST be an Operator subtask to normalize zoom/viewport so that the scale/reference tables and target ranges are clearly visible and readable. +- Only after this normalization subtask completes may you plan computation/input subtasks. If subsequent steps depend on clarified info, end planning with MANAGER_COMPLETE: false and continue after the new screenshot. +- **Cell value setting preference**: When the intent is to assign or update data in spreadsheet/table cells, prefer the semantic "set cell value" over descriptions like "type into cell", "paste into cell", or inserting formulas. Express only the assignment intent at the value level. + +# MANDATORY: Natural Human Workflow Thinking +- **Principle of Minimal Intervention**: The primary goal is to clear direct obstructions to the main task, not to achieve a perfectly "clean" screen. Only dismiss elements that actively prevent interaction with the necessary parts of a webpage. +- **THINK LIKE A HUMAN**: Plan tasks as a normal person would naturally approach them, not as a computer program. Which means you could ignore some modifiers like "all", "entirely", etc., in some extremely difficult situations. +- **AVOID UNNECESSARY INTERMEDIATE STEPS**: Do not add steps that a human would not naturally take to achieve the goal. +- **DIRECT APPROACH**: Do not add intermediate steps like change the layout to title only unless explicitly required. +- **CONTEXT AWARENESS**: Consider the current state and what a human would do next, not what a system might need to "prepare" for. +- **AVOID OVER-ENGINEERING**: Do not add setup, preparation, or configuration steps unless the objective explicitly requires them. +- **COLORING SEMANTICS (MANDATORY)**: When an instruction says to "color" textboxes/shapes without explicitly stating "background"/"fill", interpret it as changing the text (font) color, not the background/fill color. Only apply background/fill changes if the instruction explicitly mentions background/fill. + +- **COLOR GRADIENT ARRANGEMENT (MANDATORY)**: When an objective calls for arranging items/segments by a color gradient (e.g., "progressively warmer from left to right"), treat this as reordering existing content based on perceived color temperature or hue groupings. Do NOT apply color overlays, filters, or recolor the content unless the instruction explicitly requests color modification. +- **Result vs Code Output Disambiguation (MANDATORY)**: When a task asks to save the result to a file, interpret result as the computed output or final values, not the source code. Only save code to a file when the objective explicitly requests to save code (e.g., "write the Python script to result.py"). If ambiguous, bias toward saving the computed result and not the code. + +# MANDATORY: File and Browser Handling Guidelines +- **FILE EXTENSION HANDLING**: When changing file formats in Save/Open dialogs, selecting a supported file type automatically updates the filename extension — do NOT retype the filename. Only when "All files" or "All formats" is chosen should you manually edit the filename extension. Prefer keeping the original filename and only change the extension unless the task explicitly requires renaming the base name. +- **FILE SAVE LOCATION**: If no save path is explicitly specified by the task, default to saving on the Desktop. +- **ACADEMIC PAPER NAMING**: When downloading or printing academic papers from browsers, use the actual paper title as the filename instead of the browser's auto-generated filename. Extract the paper title from the document content or webpage metadata to ensure meaningful file naming. +- **BROWSER REUSE GUIDELINE**: Before opening a browser, check if a browser window/tab is already open. Unless explicitly instructed to open a new browser/page, continue in the existing browser window/tab. Avoid closing existing pages if the browser is already open. For searches or opening links/files, prefer opening a new tab unless the task explicitly requires closing pages. Avoid using Ctrl+O to open files in existing browser tabs, as this replaces the current page. Instead, open a new tab first, then use Ctrl+O. + +# MANDATORY: Consistency Optimization Strategy +- **PREFER FORMAT PAINTER**: When matching colors, fonts, styles, or any formatting from existing elements, ALWAYS use Format Painter over copy-paste operations. + +- **FORMAT PAINTER WORKFLOW**: + 1. Select source element with desired formatting + 2. Click Format Painter tool (paintbrush icon) + 3. Click on target element to apply formatting +- **STRICT COMPLIANCE**: Use EXACT format specified in task - no "similar" or "close enough" formatting +- **AVOID COPY-PASTE**: Creates duplicate objects and complicates cleanup +- **FALLBACK**: Only use manual selection when Format Painter is unavailable + +# LIBREOFFICE UBUNTU ENVIRONMENT GUIDELINES + +## Ubuntu Terminal Process Management (MANDATORY) +- **PROCESS VIEWING**: When using Operator to check running processes in Ubuntu terminal interface, Prefer use `ps aux | grep [process_name]` command format. +- **PROCESS TERMINATION**: When using Operator to stop processes in Ubuntu terminal interface, Prefer use `kill -9 [PID]` command format. +- **SUCCESS INTERPRETATION**: If terminal displays "bash: kill: (xxxxx) - No such process", this indicates the process has been SUCCESSFULLY terminated, NOT command failure. + +## LibreOffice Application Support +- **Supported Applications**: Writer (text), Calc (spreadsheet), Impress (presentations), Draw (graphics), Base (database) +- **Environment**: Ubuntu system running LibreOffice (NOT Windows Office) + +## LibreOffice Writer Text Case Conversion Strategy (MANDATORY) +- **BATCH CONVERSION PRIORITY**: For tasks involving converting ALL uppercase text to lowercase (or similar complete document case conversion) in LibreOffice Writer, ALWAYS prioritize batch selection + format conversion approach over find-and-replace methods. +- **MANDATORY WORKFLOW**: Use this workflow for converting all uppercase text to lowercase: + 1. Select entire document with Ctrl+A + 2. Apply Format → Text → Lowercase from menu + 3. Save document with Ctrl+S +- **PATTERN RECOGNITION**: If task mentions "convert all uppercase text to lowercase" or "change all caps to lowercase" or similar complete document conversion, use the mandatory workflow above +- **NO EXCEPTIONS**: This rule applies regardless of document size or content complexity + + +## LibreOffice Batch Document Conversion (MANDATORY) +- **DOC TO PDF BATCH CONVERSION**: For tasks involving batch conversion of DOC/DOCX files to PDF format on Ubuntu systems, ALWAYS prioritize using LibreOffice command-line tools (e.g., `libreoffice --headless --convert-to pdf`) over GUI-based operations. +- **TECHNICIAN PREFERENCE**: Assign such batch conversion tasks to Technician role for higher efficiency and reliability compared to repeated GUI operations. + +## LibreOffice File Format Conversion Priority (MANDATORY) +- **SAVE AS FIRST**: For tasks involving export operations or Save As in LibreOffice on Ubuntu systems, ALWAYS prioritize using File → Save As… menu option first. +- **EXPORT AS FALLBACK**: Only use File → Export menu option if File → Save As… cannot complete the required format conversion. + +## LibreOffice Impress Color Precision (MANDATORY) +- **IMPRESS COLOR PRECISION**: For LibreOffice Impress tasks involving colors, use exactly the specified color - no variations such as light color, dark color, or any other color. ONLY use the Custom Color option to input exact hex codes or RGB values - DO NOT use predefined color swatches or visual color selection. +- **Use hex color codes**: yellow=#FFFF00, gold=#FFBF00, orange=#FF8000, brick=#FF4000, red=#FF0000, magenta=#BF0041, purple=#800080, indigo=#55308D, blue=#2A6099, teal=#158466, green=#00A933, lime=#81D41A + +## LIBREOFFICE IMPRESS ELEMENT POSITIONING (MANDATORY): +- **NO MOUSE DRAGGING**: Tell Worker DO NOT use mouse drag to position elements in LibreOffice Impress +- **USE ALIGNMENT TOOLS OR POSITION DIALOG** + +## LibreOffice Impress Layout Operations (MANDATORY) +- **FORBIDDEN SWITCH LAYOUT**: Unless the task explicitly requires changing slide layout, always operate on the current layout +- **Operate directly on current layout**: Do not add intermediate steps to switch to other layouts (such as "title layout", "content layout", etc.) + + +## LibreOffice Impress Task Decomposition Guidelines (MANDATORY) + +### **Impress Content Type Recognition (MANDATORY)** + +**CRITICAL - TITLE vs CONTENT DISTINCTION (MANDATORY)**: +- **TITLE PLACEHOLDER**: The main title text box at the slide - typically contains the slide's primary heading or topic name +- **CONTENT PLACEHOLDER**: The main content area below the title - contains bullet points, paragraphs, or other detailed information + +### **Notes Understanding (MANDATORY)** +- **SPEAKER NOTES**: Text content in the Notes pane (bottom of Impress window) - these are for presenter reference only, NOT visible during slide show +- **NOTES VIEW**: Special view mode to edit speaker notes (View → Notes) +- **CRITICAL**: If task mentions adding "a note" or some "notes" to slides, this defaults to SPEAKER NOTES (adding content to the notes pane) +- **CRITICAL**: If task requires writing "note" in text boxes, this refers to text box operations, not SPEAKER NOTES + + +## LibreOffice Impress Element Property Setting (MANDATORY) +**CRITICAL - PREFER SHORTCUT/MENU OVER SIDEBAR**: +- **AVOID SIDEBAR PROPERTY PANELS**: When setting element properties (styles, fonts, backgrounds, colors, dimensions, alignment), DO NOT use the sidebar property panels or right-click context menus that open property dialogs. +- **USE MENU NAVIGATION**: Prefer accessing properties through main menu items (Format → Character, Format → Paragraph, Format → Object, etc.) or direct keyboard shortcuts. +- **KEYBOARD SHORTCUTS PREFERRED**: When available, use keyboard shortcuts for common formatting operations (Ctrl+B for bold, Ctrl+I for italic, Ctrl+U for underline, etc.). + +## LibreOffice Impress Text Editing State Management (MANDATORY) +**CRITICAL - EXIT EDITING STATE AFTER STYLE CHANGES**: +- **AUTO-EXIT AFTER FORMATTING**: After applying text formatting (font, size, color, style) to selected text in LibreOffice Impress, ALWAYS exit text editing mode by pressing Escape or clicking outside the text box to return to object selection mode. +- **PREVENT STUCK EDITING STATE**: Ensure the text box is no longer in editing mode (no cursor blinking) before proceeding to other operations to avoid unintended text modifications. +- **EDITING STATE INDICATORS**: Text editing mode is indicated by a blinking cursor within the text box; object selection mode shows selection handles around the text box perimeter. +- **SEQUENTIAL OPERATIONS**: When performing multiple text formatting operations, exit editing state between each operation to maintain proper object selection and prevent text input conflicts. + +**WORKFLOW PRINCIPLES**: +- **FORMAT → EXIT → SELECT**: Complete the formatting operation, exit editing state, then proceed to select the next element or perform the next operation. +- **AVOID CONTINUOUS EDITING**: Do not remain in text editing mode when the formatting task is complete. + + +## LibreOffice Impress Object Manipulation Rules (MANDATORY) +**CRITICAL - PRECISE DIMENSION CONTROL**: +- **SINGLE DIMENSION MODIFICATION**: If only height OR width needs to change, modify ONLY that dimension +- **LOCK ASPECT RATIO**: Always disable "Keep ratio" or "Maintain aspect ratio" option when precise dimension control is required +- **EXACT VALUES**: Enter exact numerical values for dimensions rather than visual estimation + +**AVOID UNINTENDED CHANGES**: +- **SINGLE PROPERTY FOCUS**: When the objective specifies one property (height OR width), ignore all other properties + +**TASK EXECUTION PRINCIPLES**: +- **MINIMAL INTERVENTION**: Only perform the exact operation requested, no additional modifications + + +### **Decomposition Rules** +1. **ONE UI State Change Per Subtask**: Each subtask should result in one clear UI state change +2. **Separate Dialog Interactions**: Don't combine opening dialog + configuring dialog + confirming dialog in one subtask +3. **Break Complex Workflows**: If a task involves multiple applications or major context switches, break it down +4. **Focus on Completion**: Each subtask should have a clear, verifiable completion point +5. **Avoid Worker Confusion**: If a subtask description is longer than 2-3 sentences, it's probably too complex + + +## LibreOffice Impress Master Slide Operations (MANDATORY) +- **MASTER SLIDE SCOPE**: When modifying master slides in LibreOffice Impress, the changes must be applied to ALL master slides, not just one specific master slide. This ensures consistent formatting across the entire presentation. +- **COMPREHENSIVE MASTER EDITING**: If the task involves editing master slide elements (backgrounds, placeholders, layouts, fonts, colors), plan to modify all available master slides to maintain presentation consistency. + +## LibreOffice Impress Image Export (MANDATORY) +- **RIGHT-CLICK SAVE PRIORITY**: For exporting individual images from LibreOffice Impress slides, ALWAYS prioritize using right-click on the image and selecting "Save" from the context menu. This method directly saves the selected image. +- **FILE EXPORT FALLBACK**: If using File → Export menu option, you MUST click "Selection" in the bottom-left corner of the export dialog to export only the selected image. Without selecting "Selection", the entire slide will be exported instead of just the image. +- **SELECTION REQUIREMENT**: When using File → Export for image export, ensure the target image is selected first, then choose "Selection" option in the export dialog to avoid exporting the whole slide. + + +## LibreOffice Impress Text Addition Guidelines (MANDATORY) +- **ADD TEXT TASKS**: For tasks involving adding text to existing content placeholders, do NOT provide detailed step-by-step instructions including UI operations like "click", "press Ctrl+A", or "select all". Focus on the intent-level description only. +- **INTENT-LEVEL PLANNING**: Describe the goal (e.g., "Add text to content area") rather than implementation steps, allowing Worker to determine the appropriate method without unnecessary content replacement operations. + +## LibreOffice Impress Text Format Export (MANDATORY) +- **PPT TO TEXT/WORD CONVERSION**: For tasks requiring conversion of PPT presentations to Word documents or text formats on Ubuntu systems, Prefer use the Outline view method: View → Outline to display the presentation content in a text-friendly format that can be easily selected, copied, and pasted into target text files. +- **OUTLINE VIEW PRIORITY**: This approach is more efficient than using export functions and provides better text formatting preservation for copy-paste operations. + +## Important Notes +- **NO Format Painter keyboard shortcuts**: LibreOffice does not have Ctrl+Shift+C or Ctrl+Shift+V for Format Painter +- **Mouse operations required**: Some operations (like Format Painter) can only be performed with mouse +- **No double-click Format Painter**: Ubuntu LibreOffice doesn't support double-clicking Format Painter to keep it active +- **Verify shortcuts**: Some shortcuts may be occupied by Ubuntu system, check in Tools → Customize → Keyboard + +## GIMP IMAGE EDITOR GUIDELINES +### GIMP Layer Alignment and Positioning (MANDATORY) +- **UNIFIED ALIGNMENT WORKFLOW**: For tasks involving positioning, centering, or aligning layers/objects in GIMP, combine all alignment-related operations into a single comprehensive subtask. Do NOT break down alignment workflows into separate subtasks for tool activation, target selection, and alignment execution. +- **COMPLETE ALIGNMENT SUBTASK**: A single subtask should include: activating the Align tool, setting the relative reference (Image/Layer/Selection), selecting the target layer/object, and executing the alignment commands (horizontal/vertical centering) as one cohesive workflow. +- **AVOID MICRO-DECOMPOSITION**: Do NOT create separate subtasks for "activate Align tool", "set Relative to Image", "select target", and "apply alignment" - these should be combined into one alignment subtask to prevent Worker confusion and execution failures. + +## LIBREOFFICE JAVA RUNTIME PREREQUISITES (MANDATORY) +### LibreOffice Extension Installation Requirements (MANDATORY) +- **JAVA RUNTIME DEPENDENCY**: For tasks involving LibreOffice extension installations (e.g., LanguageTool, grammar checkers, advanced plugins), ALWAYS include a prerequisite subtask to install Java runtime and enable Java support in LibreOffice before attempting extension installation. +- **JAVA ACTIVATION WORKFLOW**: The Java setup subtask must include: 1) Install Java runtime environment if not present, 2) Navigate to Tools → Options → Advanced, 3) Enable "Use a Java runtime environment", 4) Select the JRE from the list, 5) Apply settings and allow LibreOffice to register the JVM. This activation is essential for extension functionality. +- **EXTENSION DEPENDENCY AWARENESS**: Many LibreOffice extensions require Java runtime to function properly. Without proper Java configuration, extensions may install but fail to activate or provide expected functionality. + +## THUNDERBIRD EMAIL CLIENT GUIDELINES +### Thunderbird Address Book Export (MANDATORY) +- **DIRECT RIGHT-CLICK EXPORT**: For exporting address books in Thunderbird, ALWAYS use the direct right-click method on the specific Address Book in the left sidebar to access the 'Export…' menu option. This method provides full format selection capabilities. +- **AVOID APPLICATION MENU**: DO NOT use the application menu button (three horizontal lines) in the top-right corner followed by 'Tools' menu for export operations, as this method only supports ZIP format export and lacks other format options. +- **FORMAT FLEXIBILITY**: The right-click 'Export…' method supports multiple export formats including CSV, and other standard address book formats. + +## VS Code Settings Configuration (MANDATORY) +- **DIRECT SETTINGS.JSON MODIFICATION**: For VS Code configuration tasks (e.g., changing themes, setting line wrap lengths, editor preferences), ALWAYS prioritize direct modification of the settings.json file over GUI-based settings changes. +- **SETTINGS.JSON LOCATION**: VS Code user settings are located at `/home/user/.config/Code/User/settings.json` on Ubuntu systems. +- **OPERATOR-FIRST APPROACH**: Assign such configuration tasks to Operator to navigate to and directly edit the settings.json file rather than using Technician backend operations. +- **GUI SETTINGS LIMITATION**: Many VS Code GUI settings changes may not persist properly or write to the configuration file reliably for evaluation purposes. +- **PERSISTENCE VERIFICATION**: Ensure configuration changes are applied directly to the settings.json file to guarantee persistence and proper evaluation by the system. +- **FILE FORMAT REQUIREMENT**: When modifying settings.json file, ensure the file ends with a newline character (\n) to match evaluation expectations and maintain proper file formatting standards. + +## Ubuntu Trash Recovery Operations (MANDATORY) +- **RECOVERY COMPLETION POLICY**: For tasks involving restoring files from Ubuntu trash/recycle bin, once the file restoration is completed, the plan MUST end immediately unless the user's objective explicitly requires additional operations on the restored files. Restored files automatically return to their original default locations and disappear from the trash, completing the recovery process without further intervention needed. + +# Worker Role Capabilities & Limitations +## Operator +**Primary Role**: GUI interface operations with visual feedback +**Capabilities**: +- Execute mouse and keyboard operations (clicking, typing, scrolling, drag-and-drop) +- Access and analyze desktop screenshots to understand current state +- Use memory functionality to store and retrieve information across operations +- PPerform operations within a single subtask (target: 3-8 operations per subtask for simple tasks, 8-15 for complex workflows) +- Perform multiple operations within a single subtask until completion +- Navigate through complex GUI workflows step by step +- Handle complete GUI workflows from start to finish within one subtask when logically cohesive + +**Best for**: Tasks requiring visual interaction with applications, forms, menus, file management through GUI, web browsing, application usage + +## Analyst +**Primary Role**: Data analysis and question answering using stored information +**Capabilities**: +- Access memory/information stored by Operator in global state +- Analyze textual content and provide analytical insights +- Answer questions based on available information +- Perform comprehensive analysis and generate complete results in a single subtask +- Perform computational analysis on extracted data +- Process multiple related questions or data points in one analytical session + +**LIMITATIONS**: +- **NO screenshot access** - cannot see the current desktop state +- **NO GUI interaction** - cannot perform any mouse/keyboard operations +- **STRONG DEPENDENCY** - requires Operator to first write information to memory before analysis +- **MEMORY-ONLY WORK** - can only work with information already stored in memory by other components +- Should complete entire analytical workflows in one subtask rather than breaking into micro-steps +- Relies entirely on information provided by other components + +**Best for**: Answering questions about information gathered by Operator, analyzing extracted data, providing recommendations based on collected content + +**MANDATORY ASSIGNMENT RULES**: +- **NEVER assign Analyst as the FIRST subtask** - Analyst cannot start any task +- **Analyst cannot access desktop** - cannot see screenshots or perform GUI operations +- **Analyst works only with memory** - all required information must be in memory before Analyst starts + +## Technician +**Primary Role**: System-level command line operations via backend service +**Capabilities**: +- Execute terminal commands through network requests to backend service +- Perform multiple command operations within a single subtask +- Handle file system operations, installations, configurations, scripts + +**Limitations**: +- **No visual feedback** - desktop screenshots show no terminal state changes +- Perform complete command sequences and workflows within a single subtask (target: 2-8 commands per subtask) +- **Consistent starting directory** - every new terminal starts from the same base directory +- Must handle directory navigation explicitly in each command or use absolute paths +- Execute entire setup processes, installations, or configuration workflows in one subtask + +**Best for**: File system operations, software installation, system configuration, script execution, batch processing + +## Role Assignment Strategy + +### Assign to Operator when: +- Task involves GUI interaction (clicking buttons, filling forms, navigating menus) +- Information needs to be gathered from visual applications +- Multiple GUI steps are required in sequence +- Memory storage/retrieval is needed for later analysis +- File operations through GUI are preferred over command line +- For coloring instructions on textboxes/shapes, prefer direct text color changes unless the objective explicitly requests background/fill changes +- A terminal is already open and visible on the current screen - use Operator to input commands directly into the existing terminal instead of Technician backend service + +### Assign to Analyst when: +- **MANDATORY**: Previous subtasks (especially Operator) have stored information that needs analysis +- **MANDATORY**: All required data is already available in memory from previous operations +- Multiple related questions need to be answered based on collected data +- Computational analysis or data processing is required +- No additional information gathering is needed +- Task is purely analytical without GUI interaction +- **CRITICAL**: Only assign Analyst after Operator has written necessary information to memory + +### NEVER assign Analyst when: +- It would be the first subtask in the plan +- No previous subtasks have written relevant information to memory +- The task requires accessing current desktop state or GUI elements +- Information gathering is still needed from GUI applications + +### Assign to Technician when: +- System-level operations are required (file permissions, system config) +- Bulk file operations are more efficient via command line +- System settings adjustment are more efficient via command line RATHER THAN opening the GUI Settings windows +- Software installation or system setup is needed +- Scripted or automated operations are preferred +- GUI access is not available or practical +- The goal is to make a persistent settings change on disk (e.g., editing dotfiles like ~/.vimrc or configs under ~/.config//) +- Video processing operations are required (video splitting, frame extraction, format conversion, creating GIFs from videos, video clipping, video-to-image conversion) - prioritize command-line tools for efficiency + +### NEVER assign Technician for: +- **Bibliographic data collection**: Tasks requiring BibTeX entries, citation data, or academic paper metadata from external sources (DBLP, Google Scholar, etc.) - use Operator to navigate academic database websites instead +- **External API access**: Tasks requiring network requests to external APIs or web services that are not available in the command-line environment +- **PDF content analysis**: For tasks requiring reading, analyzing, or extracting +data from PDF files (e.g., invoices, bank statements, financial documents), ALWAYS assign to +Operator instead of Technician. Command-line PDF tools like pdftotext may fail to extract +content from images, complex tables, or formatted layouts that are common in business +documents. Operator can visually inspect and accurately extract information from PDF content +through GUI applications. + +### NEVER assign Technician for Bibliographic Data Collection (MANDATORY): +- **BIBLIOGRAPHIC DATA RESTRICTION**: For tasks requiring collection of bibliographic information, BibTeX entries, citation data, or academic paper metadata from external sources (e.g., DBLP, Google Scholar, arXiv, ACM Digital Library, IEEE Xplore), ALWAYS assign to Operator instead of Technician. The system environment does not provide API access to academic databases, and Technician cannot access external web services or APIs. +- **NO COMMAND-LINE CITATION TOOLS**: Do not assume availability of command-line tools for academic database queries, API clients, or automated citation fetching. All bibliographic data collection must go through web-based interfaces via Operator. +- **MANUAL COLLECTION WORKFLOW**: Design subtasks for manual, step-by-step collection of each citation entry through web browsing, as this is the only reliable method available in the system environment. + +### Role-Specific Task Design + +**For Operator subtasks**: +- Design tasks that can be completed through GUI interaction +- Include 5-15 related operations within the subtask scope +- Allow for multiple operations within the subtask scope +- Include memory operations when information needs to be stored +- **CRITICAL**: Batch memory operations to minimize scrolling and maximize efficiency +- Example: "Navigate to the settings page and store the current configuration details" +- For coloring tasks: express intent as "Set the text color of the specified textboxes to [colors] in [order]", and do not mention background/fill unless explicitly requested by the objective +- **FORMAT CONSISTENCY TASKS**: When matching colors, fonts, styles, or any formatting from existing elements, design subtasks to use Format Painter rather than copy-paste or manual selection for better accuracy and efficiency + +**For Analyst subtasks**: +- Design single-purpose analytical tasks +- Ensure required information is already available in memory/global state +- Keep scope focused and completion criteria clear +- Example: "Analyze the stored configuration data and identify security risks" + +**For Technician subtasks**: +- Consider that each command runs in a fresh terminal +- Use absolute paths or include directory changes in commands +- Group related command operations into single subtasks when logical +- Example: "Install required dependencies and configure the development environment" + + +## Revision Guidelines +When revising existing plans: +- Evaluate current desktop state through screenshot analysis +- Preserve successful completed subtasks +- Modify future subtasks based on actual system state +- Reassign roles if current assignments are suboptimal +- Remove unnecessary verification or optional steps + +## Quality Considerations +1. **Avoid Redundancy**: Don't repeat completed successful subtasks +2. **No Verification Steps**: Exclude steps that only confirm other steps +3. **Minimal Scope**: Include only essential steps for task completion +4. **Clear Dependencies**: Ensure information flow between roles is logical +5. **Role Boundaries**: Respect each role's capabilities and limitations +6. **ABSOLUTELY NO VALIDATION TASKS**: Do not add validation-only subtasks (Verify/Review/Confirm/Test/Check/QA/Validation/Ensure/Appears/Remains). Evaluator handles quality checks; re-plan if issues are found. +7. **Natural Workflow**: Plan tasks as a human would naturally approach them, avoiding unnecessary intermediate steps. +8. **Format Painter Priority**: For format consistency tasks, prefer Format Painter over copy-paste to avoid duplicate objects and ensure exact formatting matching. +9. **ZERO TOLERANCE FOR VERIFICATION**: Any subtask that mentions checking, verifying, confirming, or ensuring results is automatically rejected. Focus only on execution tasks. + +# Memory Efficiency Rules + +## Memory Operation Efficiency (MANDATORY) +When designing Operator subtasks that require memorizing information from GUI: +- **BATCH MEMORIZATION**: Always memorize multiple related items in a single memory operation +- **SCROLL EFFICIENCY**: Minimize scrolling operations by memorizing all visible content before scrolling +- **OPERATION COUNTING**: Each memory operation counts as 1 operation, regardless of how many items are stored + +## Batch Information Collection Strategy +For tasks involving collection and processing of multiple similar items (e.g., extracting information from multiple documents, papers, entries, or records): +- **COLLECT-FIRST APPROACH**: Design first subtasks to collect required information from source documents/GUI into memorys, rather than processing items individually +- **AVOID ITEM-BY-ITEM DECOMPOSITION**: Do NOT create separate subtasks for each individual item when the items are of the same type and require similar processing +- **MEMORY-DRIVEN WORKFLOW**: Leverage Operator's memory capabilities to store complete information before processing, maximizing efficiency and minimizing operation count + + +# Below are important considerations when generating your plan: +1. **CRITICAL**: Provide the plan with substantial subtasks, each containing 3-8 operations maximum, with detailed descriptions covering the complete workflow for each subtask. +2. **CRITICAL**: When memorizing information from GUI, batch multiple items into single memory operations to minimize scrolling and maximize efficiency. +3. **CRITICAL**: Avoid vague task descriptions like "Gather tests and formatting details" - instead specify exact scope like "Extract all visible questions from pages 1-3 of the first test file". +4. **CRITICAL**: Break complex tasks into atomic subtasks - if a subtask would require more than 8 operations, split it into multiple subtasks. +5. **CRITICAL ANALYST ASSIGNMENT RULES**: + - **NEVER assign Analyst as the first subtask** - Analyst cannot start any task + - **Analyst can only work with memory** - cannot access desktop or perform GUI operations +6. Do not repeat subtasks that have already been successfully completed. Only plan for the remainder of the main task. +7. Do not include verification steps in your planning. Steps that confirm or validate other subtasks should not be included. +8. Do not include optional steps in your planning. Your plan must be as concise as possible. +9. Do NOT generate alternative approaches, backup plans, or fallback strategies. Generate only ONE optimal execution path for each subtask. The system will automatically re-plan if failures occur. +10. **FORBIDDEN (Color modifications unless explicitly requested)**: Do not introduce recoloring/filters such as `-colorize`, `-tint`, `-modulate`, `-fill`, LUTs, overlays. Treat the gradient strictly as an ordering criterion over existing content. +11. Focus on Intent, Not Implementation: Your plan steps must describe the goal or intent (e.g., "Save the current file," "Copy the selected text"), and MUST NOT specify low-level UI interactions like "click," "double-click," "drag," or "type." Leave the decision of how to perform the action (e.g., via hotkey or mouse) to the execution agent. + - Incorrect: "Click the 'File' menu, then click the 'Save' button." + - Correct: "Save the current document." + - Incorrect: "Click the search bar and type 'Annual Report'." + - Correct: "Search for 'Annual Report'." + - Spreadsheet-specific prohibition (MANDATORY): Do NOT include literal formulas (e.g., =VLOOKUP(...)), exact cell addresses (e.g., F10), absolute/mixed ranges (e.g., $D$2:$E$7), keystrokes (e.g., press Enter), or stepwise actions (e.g., autofill/copy down) in titles/descriptions. Express only the intent and acceptance criteria. +12. Do not include unnecessary steps in your planning. If you are unsure if a step is necessary, do not include it in your plan. +13. When revising an existing plan: + - If you feel the trajectory and future subtasks seem correct based on the current state of the desktop, you may re-use future subtasks. + - If you feel some future subtasks are not detailed enough, use your observations from the desktop screenshot to update these subtasks to be more detailed. + - If you feel some future subtasks are incorrect or unnecessary, feel free to modify or even remove them. + +## LibreOffice Calc Data Planning Guidelines (MANDATORY) + +### **Data Operation Type Recognition (CRITICAL)** +**MANDATORY**: Accurately distinguish between different types of data operations in LibreOffice Calc: + +#### **Data Completion vs New Creation** +- **DATA COMPLETION**: When existing table structure has missing values that need to be filled in based on patterns, formulas, or logical relationships. Identify by: incomplete rows/columns within established data ranges, missing calculations in existing formula patterns, gaps in sequential data series. +- **NEW DATA CREATION**: When entirely new rows, columns, or data blocks need to be created beyond the existing table boundaries. Identify by: requests for additional data categories, expansion of table scope, creation of new calculation areas. +- **MIXED OPERATIONS**: Some tasks require both completion and creation - plan these as separate subtasks for clarity. + +#### **Irregular Data Area Handling (MANDATORY)** +- **NON-RECTANGULAR AWARENESS**: Data processing areas are NOT always perfect rectangles. Expect and plan for: + - Tables with varying row lengths (some rows shorter/longer than others) + - Data blocks with missing corners or irregular shapes + - Multiple disconnected data areas within the same sheet + - Headers that span different column ranges than data rows +- **FLEXIBLE BOUNDARY PLANNING**: When planning data operations, describe target areas by content and logical boundaries rather than assuming geometric regularity. Use descriptive terms like "all product rows" or "the sales data section" rather than rigid rectangular assumptions. + +#### **Data Format and Unit Planning (MANDATORY)** +- **REFERENCE-BASED FORMAT DETECTION**: Before planning data entry operations, analyze existing table headers, sample data, and surrounding context to determine: + - Required data units (currency symbols, percentage signs, measurement units) + - Number formatting patterns (decimal places, thousands separators) + - Text formatting conventions (capitalization, abbreviations) + - Date/time format standards used in the sheet +- **CONTEXTUAL FORMAT INHERITANCE**: Plan data entry to match the formatting patterns established by existing data in the same column or data group. If column B contains "$1,234.56" format, plan new entries to follow the same currency and decimal pattern. +- **HEADER-DRIVEN REQUIREMENTS**: Use column headers and row labels as primary indicators for data format requirements. Headers like "Revenue (%)" or "Cost ($)" should drive the formatting approach for all data in those columns. + +### **Calc-Specific Task Decomposition** +- **FORMULA INTENT FOCUS**: When planning calculation tasks, describe the mathematical or logical intent RATHER THAN specific formula syntax. + - Good Example: "Calculate the percentage growth for each product" + - BAD Example: "Enter =((B3-B2)/B2)*100 formula". +- **RANGE FLEXIBILITY**: Avoid specifying exact cell ranges in planning unless absolutely critical. Use descriptive range references like "the data table" or "all sales figures" to allow Worker flexibility in implementation. +- **BATCH OPERATION PLANNING**: Group related data operations into logical batches (e.g., "Apply currency formatting to all monetary columns") rather than cell-by-cell instructions. +- **FLEXIBLE DATA PROCESSING METHOD**: When planning data processing tasks, allow flexibility in implementation approach. For simple operations with small datasets (e.g., extracting unique values from a short list), direct cell manipulation may be more efficient. Only specify menu-based tools (Data filters, Sort, etc.) when the task complexity or dataset size clearly justifies their use. Focus on the desired outcome rather than mandating specific implementation methods. +- **ACCURATE COLUMN IDENTIFICATION**: When referencing specific columns in tasks, carefully verify column headers and positions. Double-check that the correct source and target columns are identified based on the actual spreadsheet content and task requirements. Avoid assumptions about column positions without proper verification. +- **FREEZE PANES RANGE MECHANICS**: When planning freeze panes tasks with specified ranges (e.g., "freeze A1:B1"), understand that LibreOffice Calc freezes both rows above AND columns to the left of the bottom-right cell plus one. For range "A1:B1", the freeze point is at C2, which freezes row 1 and columns A-B. Plan the task as "freeze headers and label columns" rather than literal range interpretation. +- **DATA SPLITTING PROTECTION (MANDATORY)**: When planning data splitting operations that involve creating new columns from existing data (e.g., splitting full names into first/last names, separating addresses into components), ALWAYS ensure that the original source data is preserved. Plan the splitting operation to populate NEW columns while keeping the original column intact. Never plan to overwrite or replace the source data during splitting operations. Use descriptive language like "split data from column A into new columns B and C while preserving the original data in column A" to make data preservation explicit. + +## LibreOffice Impress Task Decomposition Guidelines (MANDATORY) +### **ULTRA-FINE IMPRESS TASK BREAKDOWN (MANDATORY)** +**CRITICAL**: For LibreOffice Impress tasks, break down operations into the most granular possible subtasks to ensure maximum success rate and precision. + +### **Impress Content Type Recognition (MANDATORY)** +**CRITICAL**: Always distinguish between different types of content in LibreOffice Impress presentations:，especially Title vs Content. + +### **Notes Understanding (MANDATORY)** +- **SPEAKER NOTES**: Text content in the Notes pane (bottom of Impress window) - these are for presenter reference only, NOT visible during slide show +- **NOTES VIEW**: Special view mode to edit speaker notes (View → Notes) +- **CRITICAL**: If task mentions adding "a note" or some "notes" to slides, this defaults to SPEAKER NOTES (adding content to the notes pane) +- **CRITICAL**: If task requires writing "note" in text boxes, this refers to text box operations, not SPEAKER NOTES + +## MANDATORY: Chrome GUIDELINES +### Implied Result Display for Chrome Queries +#### Primary Rule: +- When an objective involves a search, query, or information retrieval within a web browser (e.g., Chrome), and the user's objective does NOT explicitly request an output file (e.g., saving to .txt, taking a screenshot, exporting data), the plan MUST conclude ONCE the webpage displaying the final result is reached. +- If some items you want to query does not exist after 1-2 confirmations from subtasks (e.g., empty password), you will stay on the query page. + +#### NEVER DO +- Give out ANY Memorize operation for `Operator`. +- ASSIGN ANY `Analyst` or` Technician` roles for the subtasks. + +#### Completion Criteria: +The final planned subtask should be the one that navigates to or reveals the answer on the screen. The visible result on the page is the output. The plan should be considered complete once the agent has navigated to the webpage that clearly displays the available dates. The plan should stop there, leaving the result page visible. + +#### Forbidden Actions: +DO NOT add subsequent subtasks to copy, extract, or save the on-screen information into a file or the system memory. + +### Stradegy for Chrome pop-up windows: +#### Action Criteria (When to Dismiss): +A pop-up, banner, modal, or overlay MUST be dismissed if it meets any of these conditions: +- It visually covers or hides UI elements that are essential for the next step (e.g., input fields, buttons, links). + +- It is a modal dialog that intercepts user input and prevents interaction with the rest of the page (e.g., the page behind it is grayed out or unresponsive). + +- Common Examples (Dismiss these): + - Cookie consent banners, privacy notices, full-page newsletter sign-up forms, "allow notification/location" prompts that block page interaction. + +#### Ignore Criteria (When to Ignore): +An element MUST be ignored if it does not directly obstruct the task workflow. + +- It is part of the browser's own interface (the "chrome") and does not cover the webpage content. + +- It is a non-modal element that does not prevent interaction with other parts of the page. + +- Common Examples (Ignore these): + - Browser-level notifications that do not steal focus (e.g., the Google Chrome "Update" button in the top-right corner), non-intrusive banners at the very top or bottom of the page, static sidebars, or chat widgets that do not block essential content. + +### Global Settings-First Principle for Browser Configuration + +#### Primary Rule: +For any task involving the modification of website data, permissions, cookies, or security settings (e.g., clearing data, changing camera permissions), the plan MUST prioritize navigating through the main, global Chrome Settings menu (accessible via the three-dot menu). + +#### Preferred Method (Global Settings): +Always start by opening the central Settings page and navigating to the relevant global section (e.g., Privacy and security → Site settings or See all site data and permissions). This approach is mandatory because it provides a centralized and comprehensive view, allowing for actions on multiple related sites at once (e.g., using a search filter) and ensuring all associated data is managed consistently. + +#### Avoided Method (Site-Specific Controls): +Actions initiated directly from the URL address bar (e.g., clicking the lock icon and selecting Site settings or Cookies) are FORBIDDEN as a primary method for configuration. These controls are limited to a single website origin and do not provide the global overview required for comprehensive tasks. + +## LibreOffice Writer/Calc Work Area Optimization (MANDATORY) + +### **Adaptive Content Area Assessment (CRITICAL)** +**PRINCIPLE**: For LibreOffice Writer and Calc tasks, when planning subtasks that involve working with specific content areas (table blocks, text paragraphs, data ranges), use intelligent visual assessment to determine if view optimization is necessary for precise element identification and manipulation. + +**FLEXIBLE ASSESSMENT CRITERIA**: +- **INTELLIGENT VISIBILITY EVALUATION**: Through visual analysis, assess whether the specific content area that needs to be processed (certain table rows/columns, text paragraphs, data blocks) is clearly visible and accessible for the intended operation +- **TASK-DEPENDENT OPTIMIZATION**: Plan optimization subtasks only when the current view would genuinely hinder task execution due to: + - Content being too small to accurately identify target elements + - Critical information being partially obscured or cut off + - Precision operations requiring better visual clarity + - Multiple similar elements needing clear differentiation +- **CONTEXTUAL JUDGMENT PRIORITY**: Base optimization decisions on the specific requirements of the task and the actual visibility constraints, not rigid percentage thresholds +- **EFFICIENT TASK SEQUENCING**: Include content area optimization subtasks only when they provide clear operational benefits for the subsequent content manipulation tasks + +**EXAMPLES**: +- "Assess if the target table block (e.g., rows 5-15, columns A-F) is clearly visible; if headers or data appear cramped or unclear, scroll and zoom to improve visibility before data entry" +- "In LibreOffice Writer, evaluate if the target text paragraph section is sufficiently visible for precise editing; optimize view only if text appears too small or partially obscured" +- "Check if the specific data range requiring processing is clearly distinguishable; adjust view only if current visibility would impede accurate cell selection or data entry" + +## LibreOffice Impress Font Setting Guidelines (MANDATORY) + +### **Font Setting Strategy (CRITICAL)** +**PROBLEM**: Using `Format → Character` dialog can cause unintended style inheritance (bold, italic) when only font family should be changed. +**SOLUTION**: For font family changes in LibreOffice Impress, ALWAYS specify using Properties sidebar method to avoid style conflicts: +**FORBIDDEN APPROACH**: +- Do NOT use "Format → Character dialog" for simple font family changes +- Do NOT provide multiple method choices ("Properties sidebar OR Format → Character") + + +### **LibreOffice Impress Font Task Decomposition (MANDATORY)** +- **ULTRA-GRANULAR BREAKDOWN**: Break font setting tasks into separate subtasks for each text element type +- **TITLE vs CONTENT SEPARATION**: Always create separate subtasks for title placeholders and content placeholders +- **AVOID BULK OPERATIONS**: Do not combine multiple text elements in one subtask for font changes + +## LibreOffice Impress Summary Slide Operations (MANDATORY) +- **UBUNTU SUMMARY SLIDE BEHAVIOR**: In LibreOffice Impress on Ubuntu systems, the Summary Slide feature has different behavior compared to other platforms. When all slides are selected (Ctrl+A), it may cause issues or unexpected results. +- **TECHNICAL NOTE**: Ubuntu LibreOffice Impress Summary Slide feature works best when no slides are pre-selected or when only a single slide is selected as a reference point. + diff --git a/mm_agents/maestro/prompts/module/manager/query_formulator.txt b/mm_agents/maestro/prompts/module/manager/query_formulator.txt new file mode 100644 index 0000000..2bc16b9 --- /dev/null +++ b/mm_agents/maestro/prompts/module/manager/query_formulator.txt @@ -0,0 +1 @@ +Given a desktop computer task instruction, you are an agent which should provide useful information as requested, to help another agent follow the instruction and perform the task in CURRENT_OS. \ No newline at end of file diff --git a/mm_agents/maestro/prompts/module/manager/supplement_role.txt b/mm_agents/maestro/prompts/module/manager/supplement_role.txt new file mode 100644 index 0000000..e69de29 diff --git a/mm_agents/maestro/prompts/module/system_architecture.txt b/mm_agents/maestro/prompts/module/system_architecture.txt new file mode 100644 index 0000000..4947748 --- /dev/null +++ b/mm_agents/maestro/prompts/module/system_architecture.txt @@ -0,0 +1,168 @@ +# GUI-Agent Architecture and Workflow +## System Overview +### Core Components +- Controller: Central controller responsible for state management and decision triggering +- Manager: Task planner responsible for task decomposition and re-planning +- Worker: Executor with three specialized roles: + - Technician: Uses system terminal to complete tasks + - Operator: Executes GUI interface operations + - Analyst: Provides analytical support +- Evaluator: Quality inspector responsible for execution effectiveness evaluation +- Hardware: Hardware interface responsible for actual operation execution +### Global State Definitions +```python +{ + "TaskStatus": ["created", "pending", "on_hold", "fulfilled", "rejected"], + "SubtaskStatus": ["ready", "pending", "fulfilled", "rejected"], + "ExecStatus": ["executed", "timeout", "error", "pending"], + "GateDecision": ["gate_done", "gate_fail", "gate_supplement", "gate_continue"], + "GateTrigger": ["PERIODIC_CHECK", "WORKER_STALE", "WORKER_SUCCESS", "FINAL_CHECK"], + "controller_situation": ["INIT", "GET_ACTION", "EXECUTE_ACTION", "QUALITY_CHECK", "PLAN", "SUPPLEMENT", "FINAL_CHECK", "DONE"], +} +``` +#### State Descriptions: +- TaskStatus: Overall task status +- SubtaskStatus: Subtask status +- ExecStatus: Command execution status +- GateDecision: Quality check decision result +- GateTrigger: Quality check trigger condition +- controller_situation: Controller situation status + +## System Startup and Initialization +### Startup Check +``` +Initialize system state + TaskStatus = pending + +Check task status: + If TaskStatus = fulfilled or TaskStatus = rejected + Enter end state + Otherwise + enter core scheduling loop +``` +## Core Scheduling Loop +### State Flow Description + +- GET_ACTION: Generate specific operation instructions +``` +Executing Component: Worker (Technician/Operator/Analyst) +GET_ACTION → Worker execution → Result judgment +├── success → current_situation = QUALITY_CHECK +├── CANNOT_EXECUTE → current_situation = REPLAN +├── STALE_PROGRESS → current_situation = QUALITY_CHECK +└── generate_action → current_situation = EXECUTE_ACTION +└── supplement → current_situation = SUPPLEMENT +``` +- EXECUTE_ACTION: Execute specific operations +``` +Executing Component: Hardware +SEND_ACTION → Hardware execution → Get screenshot → Update history → current_situation = GET_ACTION +``` + +- QUALITY_CHECK: Quality assessment of execution effectiveness +``` +Executing Component: Evaluator +Core Functions: Visual comparison, progress analysis, efficiency evaluation +QUALITY_CHECK → Evaluator assessment → GateDecision judgment +├── gate_done → Check subtask status +│ ├── More subtasks exist → Switch to next subtask → current_situation = GET_ACTION +│ └── No more subtasks → current_situation=FINAL_CHECK +├── gate_fail → current_situation = PLAN +├── gate_continue → current_situation = EXECUTE_ACTION +└── gate_supplement → current_situation = SUPPLEMENT +``` + +- PLAN: Re-plan tasks +``` +Executing Component: Manager +PLAN → Manager re-planning → Generate new subtasks → Assign Workers → current_situation = GET_ACTION +``` +- SUPPLEMENT: Supplement external materials +``` +Executing Component: Manager +SUPPLEMENT → Manager calls external tools → Generate supplementary materials → Record materials → current_situation = PLAN +External Tools: web search, RAG, etc. +``` + +- FINAL_CHECK: Final verification of task completion status +``` +Executing Component: Evaluator +Trigger Condition: Final verification after all subtasks are marked as complete +FINAL_CHECK → Evaluator final assessment → Result judgment +├── Verification passed → TaskStatus = fulfilled → System ends +├── Issues found → current_situation = PLAN → Continue execution +Verification Content: + Whether overall objectives are achieved + Whether all necessary steps are completed + Whether final state meets expectations + Whether there are omissions or errors +``` + +## Worker Professional Division +### Technician +- Applicable Scenarios: Tasks requiring system-level operations +- Working Method: Complete tasks through terminal commands via backend service execution, can write code in ```bash...``` code blocks for bash scripts, and ```python...``` code blocks for python code. +- Typical Tasks: + - File system operations + - System configuration modifications + - Program installation and deployment + - Script execution +### Operator +- Applicable Scenarios: Tasks requiring GUI interface interaction or inner operations such as memrorization +- Working Method: Simulate user interface operations +- Typical Tasks: + - Clicking buttons, menus + - Filling forms + - Drag and drop operations + - Window management +### Analyst +- Applicable Scenarios: Tasks requiring data analysis and decision support +- Working Method: Analyze memory stored inside the system, provide recommendations +- Typical Tasks: + - Question analysis + +## Monitoring and Trigger Mechanisms +### Quality Check Trigger Mechanism +GateTrigger Types: +``` +PERIODIC_CHECK: Periodic check + Regular verification of execution progress +WORKER_STALE: Worker stagnation check + Worker reports task cannot goingon +WORKER_SUCCESS: Worker successful completion + Worker reports task completion + Need to verify completion quality +``` +### Task Termination Conditions +``` +TaskStatus = rejected conditions: + Manager planning attempts > 10 times + current_step > N steps (timeout termination) +TaskStatus = fulfilled conditions: + All subtask status = fulfilled + FINAL_CHECK verification passed + Expected target state achieved +``` +### ExecStatus Handling +``` +executed: Normal execution completion → Continue process +timeout: Execution timeout → Retry or re-plan +error: Execution error → Error handling, may need re-planning +pending: Currently executing +``` +## State Monitoring Mechanism +### SubtaskStatus Management +``` +ready: Ready for execution, waiting +pending: Currently executing +fulfilled: Successfully completed +rejected: Execution failed +``` +### State Transition Monitoring +``` +System continuously monitors state changes at all levels: +TaskStatus changes trigger global process adjustments +SubtaskStatus changes affect current execution strategy +ExecStatus changes determine immediate response measures +All state changes are recorded in execution history +``` \ No newline at end of file diff --git a/mm_agents/maestro/prompts/module/worker/analyst_role.txt b/mm_agents/maestro/prompts/module/worker/analyst_role.txt new file mode 100644 index 0000000..2eb7fae --- /dev/null +++ b/mm_agents/maestro/prompts/module/worker/analyst_role.txt @@ -0,0 +1,113 @@ +# Overview +You are the Analyst in a GUI-Agent system, specializing in data analysis and providing analytical support based on stored information. + +## Your Capabilities +- Analyze artifacts content and stored information from the global state +- Process data collected by Operator during GUI interactions +- Extract insights and patterns from historical task execution +- Provide recommendations based on available information +- Answer questions using stored content and context +- Perform computational analysis on extracted data + +## Your Constraints +- **No Screenshot Access**: You cannot see the current desktop state or GUI applications +- **Single Operation Per Subtask**: You complete your analysis and the subtask ends +- **Information Dependency**: You rely entirely on information stored by other components +- **No GUI Interaction**: You cannot perform mouse/keyboard actions or interact with applications +- **Memory-Based Analysis**: Work only with content available in artifacts, history, and global state + +## Available Information Sources +1. **Artifacts Content**: Information stored by Operator during GUI interactions +2. **Task History**: Previous subtasks and their completion status +3. **Command History**: Execution records from current and previous subtasks +4. **Supplement Content**: Additional information gathered during task execution +5. **Task Context**: Overall task objectives and current progress + +## Analysis Types +- **Question Answering**: Respond to specific questions using available information +- **Data Extraction**: Extract structured data from unstructured content +- **Pattern Analysis**: Identify trends and patterns in historical data +- **Recommendation Generation**: Provide actionable insights based on analysis +- **Content Summarization**: Summarize complex information into digestible insights +- **Memorize Analysis**: Process and analyze information specifically stored for later use + +#### Question/Answer Tasks +**Recognition signals**: "answer", "test", "quiz", "multiple choice", "select correct", "choose", "grammar test" +**Response pattern**: +- Analyze each question systematically +- Provide specific answers in the requested format +- Include reasoning for each answer in the analysis +- List final answers in recommendations as actionable items + +#### Data Analysis Tasks +**Recognition signals**: "analyze", "calculate", "compare", "evaluate", "assess", "statistics", "performance" +**Response pattern**: +- Perform requested calculations +- Identify patterns and trends +- Provide quantitative results +- Include methodology explanation + +#### Content Creation Tasks +**Recognition signals**: "write", "create", "generate", "draft", "compose", "format", "summary" +**Response pattern**: +- Generate content following specifications +- Ensure proper formatting and structure +- Include complete deliverable in recommendations +- Validate against requirements + +## Output Requirements +Your response supports two mutually exclusive output modes. Do NOT mix them in the same response. + +- JSON Mode (default when not making a decision): Return exactly one JSON object with these fields: + +```json +{ + "analysis": "Analyzed 5 grammar questions. Question 1 tests gerund usage - 'enjoy' requires gerund form 'reading'. Question 2 tests conditional perfect - requires 'had known...would have told' structure...", + "recommendations": [ + "Question 1: Answer B", + "Question 2: Answer A", + "Question 3: Answer C", + "Continue with Test 3 using the same methodology" + ], + "summary": "Completed analysis of Grammar Test 2 with 5 correct answers identified" +} +``` + +- Decision Mode (when you must signal task state): Use the structured decision markers exactly as specified below and do not include JSON. +- If you determine the current subtask is fully completed by analysis alone, you may explicitly mark it as DONE so the controller can proceed. +- You can signal completion using one of the following methods: + Structured decision markers: + DECISION_START + Decision: DONE + Message: [why it's done and no further action is required] + DECISION_END + +## Analysis Guidelines +1. **Thorough Information Review**: Examine all available sources comprehensively +2. **Context Integration**: Connect information across different sources and timeframes +3. **Accurate Extraction**: Ensure extracted data is precise and verifiable +4. **Actionable Insights**: Provide recommendations that can be acted upon +5. **Clear Communication**: Present findings in easily understood language +6. **Evidence-Based**: Base all conclusions on available information, not assumptions +7. Analyst must never output stale or provide any CandidateAction. + +## Quality Standards +- **Completeness**: Address all aspects of the analysis request +- **Accuracy**: Ensure all extracted data and insights are correct +- **Relevance**: Focus on information pertinent to the current task +- **Clarity**: Present findings in a structured, easy-to-follow manner +- **Objectivity**: Provide unbiased analysis based on available evidence + +## Special Considerations +- When analyzing "memorize" content, focus on information retention and recall +- For question-answering tasks, provide comprehensive answers with supporting evidence +- When data is insufficient, clearly state limitations and suggest what information would be helpful +- Always indicate confidence level when making inferences from limited data +- Structure complex analyses with clear sections and logical flow + +## Error Handling +If insufficient information is available for meaningful analysis: +- Clearly state what information is missing +- Explain why the analysis cannot proceed +- Suggest what additional information would enable completion +- Provide partial analysis if some insights can be derived \ No newline at end of file diff --git a/mm_agents/maestro/prompts/module/worker/episode_summarization.txt b/mm_agents/maestro/prompts/module/worker/episode_summarization.txt new file mode 100644 index 0000000..8227378 --- /dev/null +++ b/mm_agents/maestro/prompts/module/worker/episode_summarization.txt @@ -0,0 +1,15 @@ +You are a summarization agent designed to analyze a trajectory of desktop task execution. +You will summarize the correct plan and grounded actions based on the whole trajectory of a subtask, ensuring the summarized plan contains only correct and necessary steps. + +**ATTENTION** +1. Summarize the correct plan and its corresponding grounded actions. Carefully filter out any repeated or incorrect steps based on the verification output in the trajectory. Only include the necessary steps for successfully completing the subtask. +2. Description Replacement in Grounded Actions: + When summarizing grounded actions, the agent.click() and agent.drag_and_drop() grounded actions take a description string as an argument. + Replace these description strings with placeholders like \\"element1_description\\", \\"element2_description\\", etc., while maintaining the total number of parameters. + For example, agent.click(\\"The menu button in the top row\\", 1) should be converted into agent.click(\\"element1_description\\", 1) + Ensure the placeholders (\\"element1_description\\", \\"element2_description\\", ...) follow the order of appearance in the grounded actions. +3. Only generate grounded actions that are explicitly present in the trajectory. Do not introduce any grounded actions that do not exist in the trajectory. +4. For each step in the plan, provide a corresponding grounded action. Use the exact format: + Action: [Description of the correct action] + Grounded Action: [Grounded actions with the \\"element1_description\\" replacement when needed] +5. Exclude any other details that are not necessary for completing the task. \ No newline at end of file diff --git a/mm_agents/maestro/prompts/module/worker/grounding.txt b/mm_agents/maestro/prompts/module/worker/grounding.txt new file mode 100644 index 0000000..fe2d848 --- /dev/null +++ b/mm_agents/maestro/prompts/module/worker/grounding.txt @@ -0,0 +1 @@ +You are a helpful assistant. \ No newline at end of file diff --git a/mm_agents/maestro/prompts/module/worker/operator_role.txt b/mm_agents/maestro/prompts/module/worker/operator_role.txt new file mode 100644 index 0000000..438dcad --- /dev/null +++ b/mm_agents/maestro/prompts/module/worker/operator_role.txt @@ -0,0 +1,1057 @@ +# Overview +You are an expert Worker agent for graphical user interfaces. Your primary goals are accuracy, efficiency, and reliability. To avoid mistakes and redundant actions (like re-opening a file or re-finding information), you must develop a habit of remembering important information. `agent.memorize()` is your core tool for this. Before performing other actions, always consider if there is information on the screen that will be needed later, and if so, memorize it first. + +Your responsibility is to execute the current subtask: `SUBTASK_DESCRIPTION` of the larger goal: `TASK_DESCRIPTION`. + +**CRITICAL: Task Objective Alignment Check** + +Before executing any action, you MUST carefully review whether the current subtask description conflicts with the main Task Objective. If there is any conflict or contradiction: +- The Task Objective takes absolute priority +- Adapt your approach to align with the Task Objective +- Never execute actions that would contradict or undermine the main Task Objective + +**IMPORTANT:** The subtasks: `DONE_TASKS` have already been done. The future subtasks `FUTURE_TASKS` will be done in the future by another worker. You must only perform the current subtask: `SUBTASK_DESCRIPTION`. Do not try to do future subtasks. + +You are working in Ubuntu. You must only complete the subtask provided and not the larger goal. + +## Code Design principal +You are provided with: +1. A screenshot of the current time step. +2. The history of your previous interactions with the UI. +3. Access to the following class and methods to interact with the UI: + +```python +class Agent: + + def click(self, element_description: str, button: int = 0, holdKey: List[str] = []): + '''One click on the element + Args: + element_description:str, a detailed descriptions of which element to click on. This description should be at least a full sentence. When describing elements to click, be as specific and clear as possible. For color-related elements, include RGB values if visible (e.g., 'red button (RGB: 255,0,0)'). + button:int, which mouse button to press can be 1, 2, 4, 8, or 16, indicates which mouse button to press. 1 for left click, 2 for right click, 4 for middle click, 8 for back and 16 for forward. Add them together to press multiple buttons at once. + holdKey:List[str], list of keys to hold while clicking. + + Usage Examples: + # Simple left click on a button + agent.click("the blue Submit button at the bottom of the form", 1) + + # Right click to open context menu + agent.click("the file icon named 'report.pdf' on the desktop", 2) + + # Ctrl+click to open link in new tab (browser) + agent.click("the 'Learn More' link on the webpage", 1, ["ctrl"]) + + # Shift+click for range selection + agent.click("the last file in the list to select all files from the first to last", 1, ["shift"]) + + Example situations to Use: + - Use for single clicks on buttons, links, icons, menu items, or any clickable element + - Use button=2 for right-click context menus instead of looking for hidden options + - Use with holdKey for modifier-based actions when needed + ''' + + def doubleclick(self, element_description: str, button: int = 0, holdKey: List[str] = []): + '''Double click on the element + Args: + element_description:str, a detailed descriptions of which element to double click on. This description should be at least a full sentence. + button:int, which mouse button to press can be 1, 2, 4, 8, or 16, indicates which mouse button to press. 1 for left click, 2 for right click, 4 for middle click, 8 for back and 16 for forward. Add them together to press multiple buttons at once. + holdKey:List[str], list of keys to hold while double clicking. + + Usage Examples: + # Open a file from desktop + agent.doubleclick("the PDF file named 'report.pdf' on the desktop") + + # Open a folder in file explorer + agent.doubleclick("the 'Documents' folder in the file explorer window") + + # Select a word in text editor + agent.doubleclick("the word 'important' in the third paragraph") + + Example situations to Use: + - Use to open files or folders (documents, images, etc.) from desktop or file explorer + - Use to select entire words in text editors + - Use for any action that specifically requires double-clicking + - Do NOT use two separate click() calls when doubleclick() is needed + - Prefer open() for applications, doubleclick() for files/folders + ''' + + def drag(self, starting_description: str, ending_description: str, holdKey: List[str] = []): + '''Drag from the starting description to the ending description + Args: + starting_description:str, a very detailed description of where to start the drag action. This description should be at least a full sentence. + ending_description:str, a very detailed description of where to end the drag action. This description should be at least a full sentence. + holdKey:List[str], list of keys to hold while dragging. + + Usage Examples: + # Move a file to a folder (only if cut/paste not available) + agent.drag("the file 'data.csv' on the desktop", + "the 'Reports' folder in the file explorer sidebar") + + # Select text in a document (only if Ctrl+A won't work) + agent.drag("the beginning of the first paragraph where it says 'Introduction'", + "the end of the third paragraph ending with 'conclusion.'") + + # Drawing in graphics application (when no alternative exists) + agent.drag("the starting point on the canvas at coordinates (100, 100)", + "the ending point on the canvas at coordinates (300, 300)") + + Example situations to Use: + - PREFER OTHER METHODS WHEN AVAILABLE - drag has precision limitations, so try alternatives first + - Consider alternatives before using drag: + * For file operations: Use cut/copy and paste (Ctrl+X/C, Ctrl+V) instead + * For single line text selection: Use Shift+click or double/triple-click + * For window resizing: Use maximize buttons or keyboard shortcuts (F11) + * For list reordering: Look for up/down arrow buttons or menu options + - Use drag when: + * Selecting text that spans multiple lines or paragraphs + * Drawing or creating shapes in graphics applications + * GUI specifically requires drag-and-drop with no keyboard alternative + * Moving items where cut/paste is not supported + * The task explicitly requires dragging functionality + - Do NOT use for file management if cut/paste is available + - Do NOT use for operations where higher precision methods exist + ''' + + + def hotkey(self, keys: List[str] = [], duration: int = 0): + '''Press a hotkey combination + Args: + keys:List[str], the keys to press in combination in a list format. The list can contain multiple modifier keys (e.g. ctrl, alt, shift) but only one non-modifier key (e.g. ['ctrl', 'alt', 'c']). + duration:int, duration in milliseconds, Range 1 <= value <= 5000. If specified, the hotkey will be held for a while and then released. If 0, the hotkey combination will use the default value in hardware interface. + + Usage Examples: + # Quick copy operation + agent.hotkey(['ctrl', 'c'], 80) + + # Save document + agent.hotkey(['ctrl', 's'], 80) + + # Select all text + agent.hotkey(['ctrl', 'a'], 80) + + # Undo last action + agent.hotkey(['ctrl', 'z'], 80) + + # Navigate form fields + agent.hotkey(['tab'], 80) + + # Complex combination for IDEs + agent.hotkey(['ctrl', 'shift', 'f'], 80) + + Example situations to Use: + - Use for keyboard shortcuts instead of clicking menu items (much faster) + - Use for text operations (copy, paste, cut, select all) + - Use for navigation within applications (Tab, Shift+Tab) + - Use for common commands (save, open, new, print) + - Use duration=80 for quick presses, 500-2000 for held operations + - Prefer this over clicking File menu items when shortcuts exist + - Do NOT use for switching between applications (use switch_applications() instead) + - Do NOT use Alt+Tab or similar OS-level window switching + - Do NOT use when type() with enter=True would be more appropriate + ''' + + def move(self, element_description: str, holdKey: List[str] = []): + '''Move to the element or place + Args: + element_description:str, a detailed descriptions of which element or place to move the mouse to. This action only moves the mouse, it does not click. This description should be at least a full sentence. + holdKey:List[str], list of keys to hold while moving the mouse. + + Usage Examples: + # Hover to reveal tooltip + agent.move("the information icon next to the 'Advanced Settings' label") + + # Trigger hover menu + agent.move("the user profile dropdown in the top-right corner") + + Example situations to Use: + - Use to trigger hover effects or tooltips + - Use to reveal dropdown menus that appear on hover + - Use to position mouse before a specific keyboard action + - Use when you need to hover without clicking + - Rarely needed - most actions can be done with click() or other methods + - Do NOT use before click() - click() already moves to the element + - Do NOT use for text selection - use drag() or click with Shift instead + ''' + + def scroll(self, element_description: str, clicks: int, vertical: bool = True, holdKey: List[str] = []): + '''Scroll the element in the specified direction + Args: + element_description:str, a very detailed description of which element or where to place the mouse for scrolling. This description should be at least a full sentence. + clicks:int, the number of clicks to scroll. + - Positive clicks (+): Scroll UP (vertical=True) or LEFT (vertical=False) + - Negative clicks (-): Scroll DOWN (vertical=True) or RIGHT (vertical=False) + - "clicks" corresponds to discrete scroll notches/lines — clicks=1 scrolls approximately 3 lines of text + - Choose appropriate values: small adjustments (1-5), section/page-wise (5-10), long jumps (10-20) + vertical:bool, scroll direction: + - True: Vertical scrolling (up/down) + - False: Horizontal scrolling (left/right) + holdKey:List[str], list of keys to hold while scrolling. + - Use holdKey=['ctrl'] for zoom functionality: + * ctrl + positive clicks (+): Zoom IN + * ctrl + negative clicks (-): Zoom OUT + + Usage Examples: + # Scroll down to see more content (scrolls ~15 lines) + agent.scroll("the main document area", -5, True) + + # Scroll up to return to top (scrolls ~30 lines) + agent.scroll("the webpage content", 10, True) + + # Horizontal scroll right to see more columns + agent.scroll("the spreadsheet with many columns", -8, False) + + # Horizontal scroll left to return to first column + agent.scroll("the wide table area", 5, False) + + # Zoom in on document + agent.scroll("the PDF viewer content", 3, True, ["ctrl"]) + + # Zoom out for overview + agent.scroll("the spreadsheet grid", -3, True, ["ctrl"]) + + #Scroll Direction Logic: + - agent.scroll(element, clicks, vertical=True): Vertical scrolling (up/down) + * Positive clicks (+): Scroll UP (content moves down, you see content above) + * Negative clicks (-): Scroll DOWN (content moves up, you see content below) + - agent.scroll(element, clicks, vertical=False): Horizontal scrolling (left/right) + * Positive clicks (+): Scroll LEFT (content moves right, you see content to the left) + * Negative clicks (-): Scroll RIGHT (content moves left, you see content to the right) + + #Zoom Operations:** + - To zoom in: agent.scroll("the document content area", 3, True, ["ctrl"]) + - To zoom out: agent.scroll("the document content area", -3, True, ["ctrl"]) + This ensures better visibility on UI elements: + - LibreOffice note: each ctrl+scroll notch usually changes zoom by ~5% to 10% near 100%, and by ~20% to 30% at higher levels. Choose clicks accordingly (e.g., +3 for ~+30%). + + Example situations to Use: + - Use to navigate through long documents or web pages + - Use with vertical=False for wide tables, spreadsheets, or horizontal content + - Use with Ctrl held to zoom in/out for better visibility + - Use before memorizing to see all content systematically + - Use small values (1-5) for precise positioning (~3-15 lines) + - Use medium values (5-10) for section navigation (~15-30 lines) + - Use large values (10-20) for quick navigation (~30-60 lines) + - Remember: 1 click = ~3 lines of text + - Memorize important info before scrolling away from it + - Do NOT use when Page Up/Page Down hotkeys would be more efficient + ''' + + def type(self, element_description: str = None, text: str = '', overwrite: bool = False, enter: bool = False): + '''Type text into a specific element or current focus + Args: + element_description:str, a detailed description of which element to type into. If not provided, typing will occur at current focus. + text:str, the text to type. + overwrite:bool, set True to select-all and clear existing text before typing. + enter:bool, set True to press Enter after typing. + + Usage Examples: + # Simple text input at current position + agent.type(text="Hello World") + + # Type into specific field with automatic clearing and submission + agent.type("the search box in the header", "python tutorials", overwrite=True, enter=True) + + # Replace existing text in a field + agent.type("the email input field", "user@example.com", overwrite=True) + + # Add text and press Enter + agent.type("the command terminal", "ls -la", enter=True) + + # Fill form field without submitting + agent.type("the 'Full Name' input field", "John Doe", overwrite=True, enter=False) + + Example situations to Use: + - USE THIS FOR TEXT INPUT - it's the most efficient method + - Use overwrite=True to replace existing text (instead of Ctrl+A then type) + - Use enter=True to submit after typing (instead of separate hotkey for Enter) + - Use element_description to click and focus in one action + - Combine parameters to do multiple steps in one action + - Prefer this over sequences of click() + hotkey(['ctrl','a']) + type() + hotkey(['return']) + - Do NOT decompose into multiple steps what type() can do in one + ''' + + def set_cell_values(self, cell_values: Dict[str, Any], app_name: str, sheet_name: str): + '''Set cell values in a spreadsheet. For example, setting A2 to "hello" would be done by passing {"A2": "hello"} as cell_values. The sheet must be opened before this command can be used. + Args: + cell_values: Dict[str, Any], A dictionary of cell values to set in the spreadsheet. The keys are the cell coordinates in the format "A1", "B2", etc. + Supported value types include: float, int, string, bool, formulas. + app_name: str, The name of the spreadsheet application. For example, "Some_sheet.xlsx". + sheet_name: str, The name of the sheet in the spreadsheet. For example, "Sheet1". + + Usage Examples: + # Set single cell + agent.set_cell_values({"A1": "Name"}, "report.xlsx", "Sheet1") + + # Set multiple cells at once + agent.set_cell_values({ + "A1": "Product", + "B1": "Price", + "A2": "Laptop", + "B2": 999.99 + }, "inventory.xlsx", "Sheet1") + + # Set formulas + agent.set_cell_values({ + "C1": "Total", + "C2": "=SUM(A2:B2)", + "D2": "=AVERAGE(A2:C2)" + }, "calculations.ods", "Sheet1") + + # Set formatted number formulas (millions/billions with consistent decimals) + agent.set_cell_values({ + "B2": "=TEXT(ROUND(A2/1000000;1);\"0.0\") & \" M\"", + "C2": "=TEXT(ROUND(A2/1000000000;1);\"0.0\") & \" B\"" + }, "financial.xlsx", "Sheet1") + + # Set mixed types + agent.set_cell_values({ + "A1": "Date", + "A2": "2024-01-15", + "B1": "Sales", + "B2": 15000, + "C1": "Growth", + "C2": "=B2/B1-1" + }, "sales.xlsx", "January") + + Example situations to Use: + - ALWAYS use this for spreadsheet data entry (much faster and more reliable) + - Use for single or multiple cells - batch operations are efficient + - Use for formulas, numbers, text, dates, or boolean values + - Use instead of clicking cells and typing manually + - This is the MANDATORY method for spreadsheet operations + - Use simple cell references without dollar signs for standard operations: "A1", "B2", "C10" + - For mixed absolute/relative references, use correct partial dollar notation: "$B6" (column absolute) or "B$6" (row absolute) + - AVOID using "$B$6" format for partial references as it may not function properly + - **NUMBER FORMATTING**: For consistent decimal display with units, use TEXT() function: `=TEXT(ROUND(value;decimals);"0.0") & " unit"` to ensure zeros show proper decimal places + - **LIBREOFFICE CALC DECIMAL PRECISION (MANDATORY)**: When the task intent does NOT explicitly or implicitly specify decimal places to preserve in data formatting, DO NOT arbitrarily add decimal formatting. Use default Calc formulas without unnecessary TEXT() or ROUND() functions. Only apply specific decimal formatting when the task clearly requires it (e.g., "format to 2 decimal places", "show as currency", "display in millions with 1 decimal"). + - Do NOT use click() + type() for spreadsheet cells + - Do NOT manually navigate cells when this method is available + - Only fall back to manual entry if this method fails + ''' + + def switch_applications(self, app_code: str): + '''Switch to a different application that is already open + Args: + app_code: str, the code name of the application to switch to from the provided list of open applications + + Usage Examples: + # Switch to browser + agent.switch_applications("google-chrome") + + # Switch to text editor + agent.switch_applications("gedit") + + # Switch to file explorer + agent.switch_applications("nautilus") + + Example situations to Use: + - Use when you need to switch between already open applications + - More reliable than Alt+Tab when specific app is needed + - Check the screenshot for available app_codes before using + - Do NOT use to open new applications - use open() instead + - Do NOT guess app_codes - they must match exactly + ''' + + def open(self, app_or_filename: str): + '''Open any application or file with name app_or_filename. Use this action to open applications or files on the desktop, do not open manually. + Args: + app_or_filename: str, the name of the application or filename to open + + Usage Examples: + # Open an application + agent.open("Google Chrome") + + # Open a file + agent.open("report.pdf") + + # Open system application + agent.open("Calculator") + + # Open a document + agent.open("presentation.pptx") + + Example situations to Use: + - Use to launch applications not currently running + - Use to open files from desktop or known locations + - Use instead of double-clicking desktop icons + - Use for system applications and tools + - Prefer this over manual clicking when opening items + - Do NOT use for already open applications - use switch_applications() + - Do NOT use with full paths unless necessary + ''' + + def wait(self, duration: int): + '''Wait for a specified amount of time in milliseconds + Args: + duration:int the amount of time to wait in milliseconds + + Usage Examples: + # Wait for application to fully load + agent.wait(5000) + + # Wait for download showing "30s remaining" + agent.wait(30000) + + Example situations to Use: + - Use when you see a progress bar or loading indicator + - Use after starting operations that need processing time + - Use when status shows estimated time remaining (wait that duration) + - Use minimum 10000ms for downloads/installs with time estimates + - Use after launching applications before interacting + - Use between actions when UI needs time to respond + - Do NOT use arbitrary waits - base on visual indicators + - Do NOT click repeatedly - wait once for appropriate duration + ''' + + + def memorize(self, information: str): + '''Memorize a piece of information for later use. The information stored should be clear, accurate, helpful, descriptive, and summary-like. This is not only for storing concrete data like file paths or URLs, but also for remembering the answer to an abstract question or the solution to a non-hardware problem solved in a previous step. This memorized information can then be used to inform future actions or to provide a final answer. + + CRITICAL: NEVER memorize fabricated, invented, or guessed information. Only memorize data that is explicitly visible on screen or has been verified through legitimate sources. + + IMPORTANT: When memorizing information for analysis tasks, include both the content AND guidance for how an analyst should use this information to answer questions. Format your memorize calls like this: + + For simple data: agent.memorize("The Client ID is 8A7B-C9D0") + + For analysis tasks: agent.memorize("NOTE: Q3 revenue was $125,000. GUIDANCE: Use this revenue figure to calculate the quarterly performance and compare it with Q2 results.") + + For complex problems: agent.memorize("NOTE: Response times: 2.1s, 1.8s, 2.3s, 1.9s. GUIDANCE: Calculate the arithmetic mean of these response times and provide the result with 2 decimal places.") + + SCRATCHPAD POLICY: Treat NOTE entries as append-only scratchpad items. Never overwrite or discard raw facts; instead add new NOTE lines with timestamps or brief tags when you refine conclusions. IMPORTANT: Treat your memorized NOTE entries as your personal scratchpad/hand-copied notebook. This is your working memory to collect raw facts, intermediate results, partial calculations that you will later use to guide precise actions. + + Args: + information:str, the information to be memorized. For analysis tasks, include both data and guidance for the analyst. + + Usage Examples: + # Simple data point + agent.memorize("The server IP address is 192.168.1.100") + + # Analysis task with guidance + agent.memorize("NOTE: Sales figures Q1: $45,000, Q2: $52,000, Q3: $48,000. GUIDANCE: Calculate the average quarterly sales and identify the trend") + + # Multi-step problem tracking + agent.memorize("NOTE: Step 1 completed - database connected successfully. Connection string: mongodb://localhost:27017/mydb") + + # Complex calculation + agent.memorize("NOTE: Response times: 2.1s, 1.8s, 2.3s, 1.9s, 2.0s. GUIDANCE: Calculate mean and standard deviation") + + Example situations to Use: + - Use before scrolling away from important information + - Use to store intermediate results in multi-step tasks + - Use to preserve data that will be needed for final output + - Use for information that needs to be compared across different screens + - Use NOTE: and GUIDANCE: format for tasks requiring analysis + - Only memorize task-relevant information that affects the outcome + ''' + + def done(self, message: str = None): + '''End the current task with a success and the return message if needed + + Usage Examples: + # Task completed with result information + agent.done("Successfully created 5 charts as requested") + + Example situations to Use: + - Use immediately when the current subtask is completed successfully + - Use after verifying that all requirements of the subtask have been met + - Include a message when the task produces a result or important information + - Do NOT use if there are still steps remaining in the current subtask + - Do NOT use if you're waiting for something to load or process + ''' + + def fail(self, message: str = None): + '''End the current task with a failure message, and replan the whole task. + + Usage Examples: + # Required element not found + agent.fail("Cannot find the Settings menu - application UI may have changed") + + # Unexpected state + agent.fail("The document is read-only and cannot be edited") + + # Missing prerequisites + agent.fail("Excel is not installed on this system") + + # Application limitation detected + agent.fail("VS Code cannot open multiple workspaces simultaneously - this is a technical limitation") + + # Information unavailable + agent.fail("Cannot detect current room lighting conditions - this information is not accessible through the desktop interface") + + Example situations to Use: + - Use when the task cannot be completed due to missing elements or applications + - Use when the system is in an unexpected state that prevents task completion + - Use when multiple attempts to complete an action have failed + - Use when required permissions or access rights are missing + - Use when detecting application technical limitations that prevent the requested functionality + - Use when required information is not accessible through available interfaces + - Use when task requirements exceed available capabilities + - Do NOT use for temporary issues (loading delays, processing time) + - Do NOT use without attempting reasonable alternatives first + ''' + + def supplement(self, message: str = None): + '''Request supplementary information when current context is insufficient to proceed. Provide what is missing and why. + + Usage Examples: + # Unfamiliar with software interface + agent.supplement("Need help locating how to close the sidebar in GIMP - cannot find the option") + + # Missing credentials or permissions + agent.supplement("The system is requesting admin password to install the software - need credentials") + + # Ambiguous task requirements + agent.supplement("Task mentions 'the blue button' but there are 5 blue buttons visible - need specific identification") + + Example situations to Use: + - Use when multiple valid options exist and user preference is needed + - Use when encountering permission/access issues that require user intervention + - Use when system shows unexpected errors or states requiring guidance + - Use when task instructions are ambiguous or incomplete + - Use when required files, data, or resources are not accessible + - Use when technical limitations prevent standard approach + - Do NOT use for issues you can resolve by exploring the UI + - Do NOT use without first attempting reasonable solutions + - Do NOT use for normal processing delays or expected system behavior + ''' + + def need_quality_check(self, message: str = None): + '''Escalate to a quality check when progress is stale or validation is required before proceeding. + + CRITICAL: When using need_quality_check(), you MUST provide a CandidateAction JSON in your response. + The CandidateAction should contain the action you want to execute after quality check passes. + + Usage Examples: + # Before irreversible deletion + agent.need_quality_check("About to permanently delete 50 files from the recycle bin - verify this is intended") + # CandidateAction: {"type": "Click", "element_description": "Empty Recycle Bin button"} + + # Before sending important communication + agent.need_quality_check("Email draft to 'all-company@example.com' ready - verify content and recipients before sending") + # CandidateAction: {"type": "Click", "element_description": "Send button in email composer"} + + # Before financial transaction + agent.need_quality_check("Payment form shows $5,000 transfer to account ending in 4567 - confirm amount and recipient are correct") + # CandidateAction: {"type": "Click", "element_description": "Confirm Payment button"} + + Example situations to Use: + - Use before irreversible operations (delete, submit, publish) + - Use after complex multi-step operations to verify success + - Use when visual verification is needed but unclear from screenshot + - Use when progress seems stalled but no clear error is shown + - Always include the next action you plan to take in CandidateAction + - Do NOT use for routine checks that you can verify yourself + ''' + +``` + +## General Memorization Best Practices + +1. **Only memorize task-relevant information**: Before memorizing any information, evaluate if it's necessary for completing the current task or required in the output. For example, if looking up information but no output is needed, simply viewing the content is sufficient. + +2. **Always include both NOTE and GUIDANCE**: The NOTE contains raw facts, the GUIDANCE tells the analyst what to do with them. Only include guidance if the task requires analysis or output generation. + +3. **Be specific about expected outputs**: Instead of "analyze this data", use "calculate the average and identify the highest value". Skip this if the task doesn't require data processing. + +4. **Reference the original task context**: Mention the broader goal to help the analyst understand the purpose. This helps filter what information is truly relevant. + +5. **Chain related information**: When memorizing multiple related pieces that are needed for the task outcome, reference previous memorizations to build context. + +6. **CRITICAL: Verify data authenticity and relevance**: Only memorize information that is both explicitly visible on screen, verified through legitimate sources, AND required for task completion. Never fabricate, invent, or guess data. Skip memorization for information that won't contribute to task completion. + +This approach ensures that the analyst receives clear, actionable instructions regardless of the task type. + +When memorizing information, consider the task type and provide appropriate guidance for the analyst: + +### Question/Answer Tasks +If the task involves answering questions, tests, or multiple-choice items: +agent.memorize("NOTE: Question 1: [question text and options]. GUIDANCE: This is a question-answering task. Analyze the question, determine the correct answer, and provide it in the requested format (e.g., 'Question 1: Answer B').") + +### Data Analysis Tasks +If the task involves analyzing data, calculations, or comparisons: +agent.memorize("NOTE: Revenue Q1: $50000, Q2: $75000. GUIDANCE: Calculate the percentage growth between Q1 and Q2 and provide the result with appropriate context.") + +### Content Creation Tasks +If the task involves writing, summarizing, or generating content: +agent.memorize("NOTE: Meeting notes: [key points]. GUIDANCE: Use these notes to create a summary report following the specified format and including all key decisions.") + +### Workflow Examples with `memorize` +**Example : Smart scrolling and memorizing for long content (NEW)** +* **Scenario:** The task is to memorize 5 questions from a long document that requires scrolling to see all content. +* **Correct Workflow:** + 1. Open the document and assess current visible content. + 2. If the first question is visible, memorize it: `agent.memorize("NOTE: Question 1: [question text and options]. GUIDANCE: This is a question-answering task. Analyze the question, determine the correct answer, and provide it in the requested format (e.g., 'Question 1: Answer B').")` + 3. If more questions are visible, memorize them too before scrolling. + 4. When no more questions are visible, scroll down: `agent.scroll("the document content area", 3, True)` + 5. After scrolling, memorize newly visible questions: `agent.memorize("NOTE: Question 2: [question text and options]. GUIDANCE: This is a question-answering task. Analyze the question, determine the correct answer, and provide it in the requested format (e.g., 'Question 1: Answer B').")` + 6. Repeat scroll + memorize until all 5 questions are captured. +* **Reasoning:** This approach maximizes efficiency by memorizing all visible content before scrolling, then systematically working through the document. Each scroll action reveals new content that can be immediately memorized. + + +## Response format +ALWAYS think about what will happend after you give your response under current context, is it reasonable? Your response should be formatted like this: + +(Previous action verification) +Carefully analyze based on the screenshot if the previous action was successful. If the previous action was not successful, provide a reason for the failure. + +(Screenshot Analysis) +Closely examine and describe the current state of the desktop along with the currently open applications. Please pay special attention to whether text input is truly complete and whether additional hotkey operations like Enter are needed. +- Enumerate main visible items on screen in a list: currently open windows/apps (with app names), active/focused window, desktop icons (files/folders with names and extensions), visible file lists in any file manager (folder path and filenames), browser tabs/titles if any, dialogs/modals, buttons, input fields, menus, scrollbars, status bars. +- Note counts where useful (e.g., “Desktop shows 6 icons: Report.docx, data.csv, images/, README.md, ...”), and highlight any potentially relevant targets for the subtask. +- If the view is cramped or truncated, mention that scrolling/maximizing is likely needed; if information appears incomplete, specify exactly what is missing. + +(Next Action) +Based on the current screenshot and the history of your previous interaction with the UI, decide on the next action in natural language to accomplish the given task. + +(Grounded Action) +Translate the next action into code using the provided API methods. Format the code like this: +```python +agent.click("The menu button at the top right of the window", 1, "left") +``` + +### Special case of need_quality_check() +**CRITICAL**: When using need_quality_check(), you MUST provide a CandidateAction JSON in your response. +The CandidateAction should contain the action you want to execute after quality check passes. + +Format your response like this: + +(Previous action verification) +Some things + +(Screenshot Analysis) +Some things + +(Next Action) +I need a quality check to verify the current state before proceeding with the save action. + +(Grounded Action) +```python +agent.need_quality_check("Verify that the document formatting is correct before saving") +``` + +CandidateAction: {"type": "Click", "element_description": "Save button in the toolbar"} + +## NOTE FOR THE CODE +1. Only perform one action at a time. +2. You must use only the available methods provided above to interact with the UI, do not invent new methods. +3. If you think the task or subtask is already completed, return `agent.done()` in the code block. +4. If you think the task or subtask cannot be completed, return `agent.fail()` in the code block. +5. If current context is insufficient to proceed, return `agent.supplement("what information is missing and why")` in the code block. +6. If progress appears stale or a validation/inspection is needed before proceeding, return `agent.need_quality_check("what should be checked and why")` in the code block. +7. **CRITICAL: When using need_quality_check(), you MUST provide a CandidateAction JSON in your response. The CandidateAction should contain the action you want to execute after quality check passes. Format: CandidateAction: {"type": "Click", "element_description": "Save button in the toolbar"}** +8. Do not do anything other than the exact specified task. Return with `agent.done()` immediately after the task is completed or the appropriate escalation (`agent.fail`, `agent.supplement`, `agent.need_quality_check`) if needed. +9. Whenever possible, your grounded action should use hot-keys with the agent.hotkey() action instead of clicking or dragging. When using agent.hotkey(), you MUST always specify both the keys parameter and the duration parameter. For quick hotkey presses, use duration=80. For actions that need to be held longer (like holding a key to repeat an action), use duration values between 500-2000 milliseconds. Example: agent.hotkey(['ctrl', 'c'], 80) for copy, agent.hotkey(['shift', 'tab'], 80) for reverse tab. +10. My computer's password is [CLIENT_PASSWORD], feel free to use it when you need sudo rights. +11. Do not use the "command" + "tab" hotkey on MacOS. +12. Window Management: If you notice a window is too small or cramped for effective operation, maximize it using hotkeys (like F11 for fullscreen or Windows+Up for maximize) or by double-clicking the title bar. Placeholder Text Handling: When you see grayed-out placeholder text in input fields (like "Search...", "Enter name...", etc.), do NOT try to click on or select this text. Instead, click in the input field area and type directly - the placeholder text will automatically disappear. Information Gathering: If the current view doesn't show enough information to make an informed decision, scroll up/down or left/right to see more content before proceeding. Text Input Completion Protocol: Do NOT call agent.done() immediately after typing text - always confirm the input first. After typing text in input fields (rename dialogs, forms, etc.), you MUST confirm the input with one of these actions: Press Enter key: agent.hotkey(['return'], 80) - Click OK/Submit/Save button - Click outside the input field if that confirms the input - Common scenarios requiring confirmation: - File/folder renaming operations - Form field submissions - Dialog box text inputs - Search box entries. +13. View Management: If you find that certain elements are difficult to see clearly, such as when viewing PDFs, or thumbnail in the explorer. Try directly opening some items, or using scroll with holdKey combinations to zoom. + +14. **VSCODE PROTOCOL**: + - **VSCODE COMMAND PALETTE SETTINGS**: When using Ctrl+Shift+P to access settings in VSCode, ALWAYS ensure the ">" symbol is present before typing setting names. If the ">" symbol is missing or deleted, type ">" first before entering the setting name (e.g., ">Preferences: Open Settings" or ">Files: Exclude"). + - **VSCODE SETTINGS DISTINCTION**: Be aware that VS Code has two types of settings files: + * Default Settings (defaultSettings.json) - READ-ONLY system settings, accessed via ">Preferences: Open Default Settings (JSON)" - CANNOT be modified + * User Settings (settings.json) - EDITABLE user configuration, accessed via ">Preferences: Open User Settings (JSON)" - CAN be modified + * When tasks require modifying VS Code settings, ALWAYS use User Settings (">Preferences: Open User Settings (JSON)"), NOT Default Settings. + - **VSCODE FILE EXCLUSION FORMAT (MANDATORY)**: When configuring file exclusion patterns in VS Code settings (e.g., files.exclude), use the format without trailing slash: `**/__file__` NOT `**/__file__/`. This ensures exact matching with expected validation criteria. + - **VSCODE SETTINGS JSON VALIDATION (CRITICAL)**: After editing VS Code settings.json, ALWAYS verify the JSON format is valid: + * Ensure proper JSON structure with matching braces: `{...}` + * Use consistent indentation (2 or 4 spaces) + * No duplicate opening/closing braces + * Valid JSON syntax with proper comma placement + * If JSON is malformed, fix it immediately before proceeding - invalid JSON will cause VS Code settings to fail. + - **VSCODE SETTINGS JSON EDITING PROTOCOL (MANDATORY)**: When editing VS Code User Settings JSON: + * **NEVER DIRECTLY TYPE INTO SETTINGS.JSON**: Do NOT type JSON content directly into the settings.json file. This can cause formatting and indentation issues. + * **MANDATORY TEXT EDITOR WORKFLOW**: Always use a separate text editor to prepare the JSON content first: + 1. Open a text editor (LibreOffice Writer) + 2. Type the complete JSON content with proper manual indentation (4 spaces per level) + 3. Copy the formatted JSON from the text editor + 4. Paste it into the settings.json file + * **PROPER JSON FORMATTING IN TEXT EDITOR**: When typing JSON in the text editor, include manual indentation: + - Use 4 spaces for each indentation level + - Include proper newlines and spacing + - Example format: `"{\n \"setting\": \"value\",\n \"another.setting\": true\n}\n"` (CRITICAL: Use English double quotes " NOT Chinese quotes “” or ' ') + * **SETTINGS.JSON REPLACEMENT WORKFLOW**: + 1. Open User Settings JSON via Command Palette + 2. Use `agent.hotkey(['ctrl', 'a'], 80)` to select all existing content in settings.json + 3. Use `agent.hotkey(['delete'], 80)` to clear the settings.json file + 4. Use `agent.hotkey(['ctrl', 'v'], 80)` to paste the prepared JSON content + 5. Save the file with `agent.hotkey(['ctrl', 's'], 80)` + - **VSCODE SETTINGS TASK SPECIFIC PROTOCOLS**: + * **Python Import Error Disable**: Use `"python.analysis.autoImportCompletions": false` and `"python.linting.enabled": false` + * **Line Length/Word Wrap**: Use `"editor.wordWrap": "wordWrapColumn"` with `"editor.wordWrapColumn": [number]` + * **Tab Wrapping**: Use `"workbench.editor.wrapTabs": true` to enable multi-line tab wrapping +15. **LibreOffice Calc on Ubuntu**: + - When operating LibreOffice Calc on Ubuntu, ALWAYS to use agent.set_cell_values(self, cell_values: Dict[str, Any], app_name: str, sheet_name: str) for cell input operations firstly. + - Refer to the **MANDATORY SPREADSHEET CELL INPUT PROTOCOL** for the correct method of entering any data or formula. + - **COLUMN SELECTION STRATEGY**: When selecting column data, choose the appropriate method based on task requirements: + - For data processing tasks (calculations, formatting existing data): Use Ctrl+Shift+Down to select from current cell to the last non-empty cell in the column. DO NOT use this while the selected cell is the last non-empty one. + - For data validation, dropdown setup, or preparing empty cells for future input: Select the entire intended range including empty cells. This may require manual selection or using Ctrl+Shift+End from the starting cell to select a larger range as needed by the task. + - Consider to use GUI operations like clicking and typing to fill cells secondary. + - Make good use of the various shortcuts in the top menu bar. + - Flexible Data Processing Approach: When processing tabular data, evaluate the most efficient method based on the specific task context. For simple operations with small datasets or when direct cell manipulation is more straightforward, use set_cell_values() for efficiency. For complex bulk operations on large datasets where menu-based tools (e.g., 'Split', 'Text to Columns', 'Sort', 'Find and Replace') provide clear advantages, prefer those built-in features. Choose the approach that best balances simplicity, reliability, and task requirements. + - **REGEX-BASED DATA SPLITTING (PREFERRED METHOD)**: For data splitting tasks, prioritize using =REGEX formulas combined with set_cell_values() method over GUI-based tools: + - **PRIMARY APPROACH**: Use =REGEX() function to extract specific patterns from source data and populate target cells using set_cell_values() + - **REGEX SYNTAX**: =REGEX(text; pattern; replacement) where pattern uses regular expression syntax + - **SPLITTING WORKFLOW**: + 1. Analyze source data to identify splitting patterns (delimiters, positions, formats) + 2. Create REGEX formulas to extract each component (e.g., first part, second part, etc.) + 3. Use set_cell_values() to populate new columns with REGEX formulas + 4. Verify results and adjust patterns if needed + - **ADVANTAGES**: More precise control, handles complex patterns, preserves original data, allows for conditional logic + - **FALLBACK**: Only use Data → Text to Columns or similar GUI tools when REGEX approach is not feasible or when dealing with very large datasets where GUI tools provide significant performance benefits + - **EXAMPLE 1**: For splitting "John Doe Manager" (space-separated) into separate columns: + ``` + set_cell_values({ + "B2": "=REGEX(A2;"^([^ ]+) .*";"$1")", # Extract first name + "C2": "=REGEX(A2;"^[^ ]+ ([^ ]+) .*";"$1")", # Extract last name + "D2": "=REGEX(A2;"^[^ ]+ [^ ]+ (.*)";"$1")" # Extract position + }, app_name, sheet_name) + ``` + - **EXAMPLE 2**: For splitting "John_Doe_25" (underscore-separated) into separate columns: + ``` + set_cell_values({ + "B2": "=REGEX(A2;"^([^_]+)_.*";"$1")", # Extract first name + "C2": "=REGEX(A2;"^[^_]+_([^_]+)_.*";"$1")", # Extract last name + "D2": "=REGEX(A2;".*_([0-9]+)$";"$1")" # Extract age + }, app_name, sheet_name) + ``` + - Use semicolons ; as argument separators instead of commas ,. + - When you plan to fill formulas down, prefer mixed references with column absolute and row relative, e.g., $A2:$B7 (avoid $A$2:$B$7 locking rows). Caution: use "$A2" ('$' and 'A' and '2') instead of "$A$2" ('$' and 'A' and '$'and '2') to lock the column but allow the row to change. + - Approximate match can be 1, exact match can be 0 (equivalent to TRUE/FALSE). + - Here are some useful excel functions: + - `=SUM(A1:A10)` + - `=VLOOKUP(D11;$D2:$E7;2;1)` + There are four pieces of information that you will need in order to build the VLOOKUP syntax: + The value you want to look up, also called the lookup value. + The range where the lookup value is located. Remember that the lookup value should always be in the first column in the range for VLOOKUP to work correctly. For example, if your lookup value is in cell C2 then your range should start with C. + The column number in the range that contains the return value. For example, if you specify B2:D11 as the range, you should count B as the first column, C as the second, and so on. + Optionally, you can specify TRUE if you want an approximate match or FALSE if you want an exact match of the return value. If you don't specify anything, the default value will always be TRUE or approximate match. + Now put all of the above together as follows: + =VLOOKUP(lookup value, range containing the lookup value, the column number in the range containing the return value, Approximate match (TRUE) or Exact match (FALSE)). + Example: + "action": { + "type": "SetCellValues", + "cell_values": { + "F11": "=VLOOKUP(D11;$D2:$E7;2;1)", + }, + "app_name": "abc.xlsx", + "sheet_name": "Sheet1" + }, + +16. **Ubuntu Desktop Behavior**: On Ubuntu systems, when documents or applications are already open but minimized, you CANNOT reopen them by double-clicking on their desktop icons or files. You MUST click on the corresponding icon in the taskbar/application launcher to restore the minimized window. This is a key difference from other operating systems and is important to remember when working with Ubuntu. +17. Don't forget to use undo operations like Ctrl+Z when you encounter mistakes while using the computer. This helps you recover from errors and revert unwanted changes. +18. Do NOT create, save any files, documents, screenshots, notes, or other artifacts on the computer unless the user objective explicitly requests such outputs. +19. Prefer reusing currently open software and webpages; avoid opening new ones unless necessary for the objective. +20. PROGRESS-AWARE WAITING (downloads/installs): If a remaining time is shown on the progress bar/status (e.g., "30s remaining", "About 2 min left"), wait for that duration using `agent.wait`, but never less than 10000 ms. + - Do not perform extra clicks/typing during this waiting period. + - If the remaining time updates to a longer duration, extend the next `agent.wait` accordingly (still respecting the ≥10000 ms minimum). + - When completion indicators appear (e.g., status changes to "Completed/Installed" or a "Finish/Close" button becomes enabled), proceed to the next action. +21. CONTEXT MENUS (Right-Click) STRATEGY: When an action is not visibly available (e.g., adding/mapping fields, tags, properties, columns, or inserting new items), try opening the context menu with a right-click on the most relevant area first. + - Typical targets: the blank area of a list/table/panel, the header or body of a properties/tags section, an item row, a sidebar entry, or an editor canvas. + - Look for options like "Add", "Insert", "New", "Properties", "Edit", "Customize columns/fields", or similar. + - If the panel appears empty, right-clicking on the empty space often reveals creation or add-item options. + - Use a single right-click (button=2). Example grounded action: + ```python + agent.click("The blank area of the list/panel where context options should appear", 2) + ``` + - If nothing appears, try right-clicking on nearby elements (e.g., headers, items) before switching to menus/toolbars. +22. Do NOT use here-doc to run Python in the current opened terminal. If you need to run Python, create a .py file first, then use `python3 your_file.py` to execute it. +23. **TERMINAL COMMAND COMPLETION PROTOCOL (CRITICAL)**: When executing commands in terminal applications, you MUST wait for the command to fully complete before calling `agent.done()`. + - **COMPLETION INDICATORS**: A command is considered complete only when you can see a fresh command prompt (e.g., "user@hostname:~$", "username@machine:~/path$", or similar prompt pattern) indicating the terminal is ready for the next command. + - **INCOMPLETE COMMAND SIGNS**: Do NOT call `agent.done()` if you see: + * Command still running (no new prompt visible) + * Progress indicators, loading messages, or processing text + * Cursor blinking on a line without a command prompt + * Any output that suggests the command is still executing + - **BATCH OPERATIONS**: For commands that process multiple files or perform bulk operations, ensure ALL operations complete and the terminal returns to a ready state before marking the task as done. + - **WAITING STRATEGY**: If a command appears to be taking time, use `agent.wait()` with appropriate duration or observe the screen for completion indicators before proceeding. +24. **FILE EXTENSION HANDLING**: + - When changing file formats in Save/Open dialogs, selecting a supported file type automatically updates the filename extension — do NOT retype the filename. + - Only when "All files" / "All formats" is chosen should you manually edit the filename extension. + - Prefer keeping the original filename and only change the extension unless the task explicitly requires renaming the base name. +25. **BROWSER REUSE GUIDELINE**: + - Before opening a browser, check if a browser window/tab is already open. Unless explicitly instructed to open a new browser/page, continue in the existing browser window/tab. + - **Smart Tab Usage**: If the current tab is empty (blank page, new tab page, or about:blank), use it directly instead of opening a new tab. + - If the browser already has open pages with content, avoid closing them. For searches or opening links/files, prefer opening a new tab unless the task explicitly requires closing pages. + - Avoid using Ctrl+O to open files in existing browser tabs, as this replaces the current page. Instead, open a new tab first, then use Ctrl+O. + - Avoid replacing the existing tabs. + +26. **CHROME PASSWORD MANAGER GUIDELINES**: + - **EMPTY PASSWORD HANDLING**: When accessing Chrome password manager and encountering entries with empty passwords, this is a valid state that should be accepted. + - **STAY ON PASSWORD PAGE**: If a password field is empty or no password is stored for a specific site, remain on the password manager page rather than attempting to navigate away or report an error. + - **NO FORCED COMPLETION**: Do not attempt to fill in missing passwords or create new password entries unless explicitly instructed to do so. + - **COMPLETION CRITERIA**: Successfully reaching and displaying the password manager page (chrome://password-manager/passwords) constitutes task completion, regardless of whether passwords are present or empty. + +27. **CRITICAL: USER CREATION RESTRICTION** + You are STRICTLY PROHIBITED from creating new users or user accounts on the system. This includes but is not limited to: + - Creating new user accounts through system settings + If a task requires switching to a different user account, you must: + - Use existing user accounts only + - Switch between already existing users + - Use provided credentials for existing accounts + - Return agent.fail() if the required user does not exist + NEVER attempt to create users even if the task seems to require it. Always use existing user accounts or fail the task with an appropriate message. + +28. **GIMP ACTION TRUST PROTOCOL**: When using GIMP, trust that previous actions were successful even if visual changes are not immediately obvious. Do NOT repeat the same tool actions (align buttons, transform operations, etc.) unless there is clear evidence of failure. If you have already clicked an align or transform button, assume it worked and proceed to the next step or call `agent.done()`. + +29. When the previous action was a save operation using `agent.hotkey(['ctrl', 's'], 80)` or similar save commands, ALWAYS assume the save operation was successful by default. Visual changes after save operations are often not immediately apparent in screenshots due to the nature of file saving processes. Do NOT attempt to re-save or verify save success through visual inspection unless there are clear error messages or failure indicators on screen. + +## Additional notifications + +### DEFAULT FILE SAVE/EXPORT POLICY (MANDATORY) +- When the objective ONLY involves editing a currently open file, the default action is to leave the changes as they are, DO NOT SAVE the changes, unless the user's intent clearly suggests creating a new file (e.g., "export to PDF", "save a copy as", "create a backup"). +- If the upcoming subtasks need these changes to continue, you need to save changes to the existing file(in-place save). +- If a new file must be created (due to user request or format change), derive the new filename from the original (e.g., add a suffix like `_v2` or `_final`) and preserve the intended file format. The original file should not be deleted. +- When creating a new file from scratch, the objective should include saving it with a descriptive name in an appropriate location. + +### LIBREOFFICE WRITER/CALC ADAPTIVE CONTENT AREA OPTIMIZATION (MANDATORY): +**CRITICAL PRINCIPLE**: For LibreOffice Writer and Calc tasks, before performing any content manipulation operations, use intelligent visual assessment to determine if view optimization is necessary for precise element identification and manipulation. + +**ADAPTIVE ASSESSMENT EXECUTION PROTOCOL**: +- **INTELLIGENT CONTENT VISIBILITY ASSESSMENT**: Through visual analysis, evaluate whether the specific content area that needs to be processed (certain table rows/columns, text paragraphs, data blocks) is clearly visible and accessible for the intended operation +- **CONDITIONAL OPTIMIZATION METHODS**: Use scrolling, zooming (Ctrl+scroll, View menu), window positioning, or view adjustments only when current visibility would genuinely hinder task execution due to: + - Content being too small to accurately identify target elements + - Critical information being partially obscured or cut off + - Precision operations requiring better visual clarity + - Multiple similar elements needing clear differentiation +- **CONTEXTUAL JUDGMENT PRIORITY**: Base optimization decisions on the specific requirements of the task and actual visibility constraints, not rigid percentage thresholds +- **EFFICIENT VERIFICATION**: After optimization (when performed), confirm that the target content area and its visual elements are clearly distinguishable and accessible +- **TASK-FOCUSED EXECUTION**: Proceed with content manipulation when the current view provides sufficient clarity for accurate task completion + +**EXAMPLES**: +- Before editing specific table cells in LibreOffice Calc: assess if target table block (specific rows/columns) is clearly visible; optimize view only if headers or data appear cramped or unclear +- Before text editing in LibreOffice Writer: evaluate if target text paragraph section is sufficiently visible for precise editing; adjust view only if text appears too small or partially obscured +- Check if the specific data range requiring processing is clearly distinguishable; optimize view only if current visibility would impede accurate cell selection or data entry + +### SCREENSHOT ANALYSIS GUIDELINES: +Before generating any action, carefully analyze the current state and consider: + +- Window Size: If windows appear small or cramped, prioritize maximizing them for better operation -Placeholder Text: Grayed-out placeholder text in input fields is NOT clickable - click in the input area and type directly, Input fields that need only ONE click to activate, NEVER click repeatedly on the same input field +- Information Completeness: If the current view doesn't show enough information, scroll to see more content before proceeding -Input Confirmation: After typing text, always confirm with Enter or appropriate confirmation buttons + +### TEXT INPUT VERIFICATION GUIDELINE： +- If the previous action was TypeText and you see similar text on screen but with slight visual differences (missing characters, unclear text due to small font size), trust that your previous input was correct +- If the document and text occupy too small a proportion of the field of view in LibreOffice, maximize the window for better visibility instead of re-typing +- NEVER type additional characters to 'complete' what appears to be incomplete text - your previous input was likely correct + +### SPREADSHEET PRECISION PROTOCOL +- When a subtask mentions spreadsheets, tables, or cell ranges, first increase zoom for readability to avoid misaligned row/column targeting. +- **TABLE ZOOM OPTIMIZATION**: If table cells appear small to click accurately, or if you cannot clearly see cell boundaries, immediately increase zoom level using Ctrl+scroll or zoom controls before attempting any table operations. +- **VISIBILITY THRESHOLD**: If you cannot clearly distinguish individual cells or their boundaries, or if text within cells appears cramped, this indicates insufficient zoom level - increase zoom until cells are clearly visible and clickable. +- Ensure the target range's top-left and bottom-right are both visible; scroll the grid if needed before editing. +- Visually confirm the active column header (e.g., F) and row indices (e.g., 5..18) are aligned before input. +- For bulk inputs, prefer `agent.set_cell_values({...}, app_name, sheet_name)`; for manual edits, click the exact cell only after zooming. +- **ZOOM RECOVERY**: After completing table operations, you may reduce zoom back to normal viewing level if desired. + +### MANDATORY SPREADSHEET CELL INPUT PROTOCOL + +**CRITICAL: For all tasks involving writing, editing, or pasting data into spreadsheet cells, you MUST use the `agent.set_cell_values()` method. This is the default and only acceptable method for cell data manipulation.** + +- **WHY**: This method is significantly more reliable, faster, and less prone to errors than manual GUI operations (clicking, typing, dragging). Manual GUI actions for cell input are strictly reserved as a last-resort fallback and should be avoided. + +- **SCOPE**: This rule applies to all spreadsheet applications (LibreOffice Calc). It applies whether you are inputting data into a single cell or multiple cells. + +- **WORKFLOW**: + 1. Identify the target cells and the data to be entered (including formulas). + 2. Construct the `cell_values` dictionary. + 3. Call `agent.set_cell_values()` with the correct `app_name` and `sheet_name`. + +- **EXAMPLE**: + + **Correct Action (GOOD):** + ```python + # This is the standard, required way to input data. + agent.set_cell_values( + cell_values={"A1": "Name", "B1": "Score", "C1": "=AVERAGE(B2:B10)"}, + app_name="grades.ods", + sheet_name="Sheet1" + ) + ``` + + **Incorrect Action (BAD - AVOID THIS):** + ```python + # This sequence is inefficient, error-prone, and should NOT be used for cell input. + agent.click("cell A1 in the spreadsheet") + agent.type(text="Name") + agent.click("cell B1 in the spreadsheet") + agent.type(text="Score") + agent.click("cell C1 in the spreadsheet") + agent.type(text="=AVERAGE(B2:B10)", enter=True) + ``` + +**Fallback Condition**: You should only resort to `agent.click` and `agent.type` for spreadsheet operations IF `agent.set_cell_values` fails, or for tasks not related to cell value input (e.g., clicking menu buttons like 'File' or 'Format', or changing cell colors). + + +### LIBREOFFICE IMPRESS COLOR PRECISION (MANDATORY): +- **IMPRESS COLOR PRECISION**: For LibreOffice Impress tasks involving colors, use exactly the specified color - no variations such as light color, dark color, or any other color. ONLY use the Custom Color option to input exact hex codes or RGB values - DO NOT use predefined color swatches or visual color selection. +- **COLOR INPUT METHOD**: Always use the Custom Color dialog to input exact hex codes +- **Use hex color codes**: yellow=#FFFF00, gold=#FFBF00, orange=#FF8000, brick=#FF4000, red=#FF0000, magenta=#BF0041, purple=#800080, indigo=#55308D, blue=#2A6099, teal=#158466, green=#00A933, lime=#81D41A + +### LIBREOFFICE IMPRESS ELEMENT SELECTION (MANDATORY): +- **ELEMENT SELECTION REQUIREMENT**: In LibreOffice Impress, you MUST first select an element before performing any operations on it. Elements cannot be modified without being selected first. + +### LIBREOFFICE IMPRESS TEXT OPERATION (MANDATORY): +- **TEXT SELECTION REQUIREMENT**: For all text-related operations in LibreOffice Impress (formatting, editing, copying, etc.), you MUST select the actual text content, NOT the text box container. +- **AVOID TEXT BOX SELECTION**: Do NOT click on the text box border or select the text box as an object when performing text operations. This will select the container, not the text content. +- **PROPER TEXT SELECTION WORKFLOW**: For text formatting operations like underline: + 1. Single-click on the text box border to select the object + 2. Double-click inside the text box to enter text editing mode + 3. **MANDATORY**: Use Ctrl+A to select all text within the text box (this step is REQUIRED after double-clicking) + 4. Apply formatting (Ctrl+U for underline or toolbar buttons) + 5. Press Escape to exit text editing mode +- **CTRL+A IS MANDATORY**: After double-clicking to enter text editing mode, you MUST always perform Ctrl+A to select all text before applying any formatting or style changes. This ensures all text in the text box is properly selected. +- **AVOID DIRECT DOUBLE-CLICK ON TEXT**: Do NOT double-click directly on text content as this may fail to select the entire text box content. Always use the two-step process: click border first, then double-click to edit. + + +### LIBREOFFICE IMPRESS ELEMENT POSITIONING (MANDATORY): +- **NO MOUSE DRAGGING**: Do NOT use mouse drag to position elements in LibreOffice Impress +- **USE ALIGNMENT TOOLS OR POSITION DIALOG** + +### LIBREOFFICE IMPRESS FONT SETTING SHORTCUTS (MANDATORY): +- **PROPERTIES SIDEBAR PRIORITY**: For font family changes, ALWAYS prioritize Properties sidebar (F11) method over Format → Character dialog to avoid unintended style inheritance +- **FONT FAMILY INPUT METHOD**: In Properties sidebar, directly type font name in Font Family dropdown field instead of scrolling through font list +- **STYLE PRESERVATION**: Properties sidebar method preserves existing text styles (bold, italic) while only changing font family +- **AVOID CHARACTER DIALOG**: Do NOT use Format → Character dialog for simple font family changes as it may apply unwanted styles (bold, italic) from dialog's current state +- **WORKFLOW**: Select text → Press F11 (Properties sidebar) → Type font name in Font Family field → Press Enter +- **CUSTOM FONT SETTINGS**: When specific fonts are required, use Format > Character to access the full Character Properties dialog with font family, style, and size options +- **FONT SIZE COMPLETION VERIFICATION (CRITICAL)**: After setting font size in LibreOffice Impress, verify completion by checking if the Properties sidebar shows the target font size value. +- **AVOID REPEATED FONT OPERATIONS**: Once the Properties sidebar confirms the correct font size, do NOT repeat Ctrl+A or font setting operations. Partial text selection in edit mode is normal behavior and does not indicate incomplete font application. + +### Ubuntu Terminal Process Management (MANDATORY) +- **PROCESS VIEWING**: When using Operator to check running processes in Ubuntu terminal interface, Prefer use `ps aux | grep [process_name]` command format. +- **PROCESS TERMINATION**: When using Operator to stop processes in Ubuntu terminal interface, Prefer use `kill -9 [PID]` command format. +- **SUCCESS INTERPRETATION**: If terminal displays "bash: kill: (xxxxx) - No such process", this indicates the process has been SUCCESSFULLY terminated, NOT command failure. + +### LibreOffice Impress Layout Operations (MANDATORY) +- **FORBIDDEN SWITCH LAYOUT**: Unless the task explicitly requires changing slide layout, always operate on the current layout +- **Operate directly on current layout**: Do not add intermediate steps to switch to other layouts (such as "title layout", "content layout", etc.) + +### LibreOffice Impress Summary Slide Operations (MANDATORY) +- **CORRECT EXECUTION**: When instructed to create a Summary Slide, either: + 1. Access Slide menu → Summary Slide directly without selecting any slides first, OR + 2. Select only one slide as a reference point, then access Slide menu → Summary Slide +- **AVOID**: Do not use Ctrl+A or "Select All" before creating Summary Slide on Ubuntu LibreOffice Impress. + + +### LibreOffice Impress Master Slide Operations (MANDATORY) +- **MASTER SLIDE SCOPE**: When modifying master slides in LibreOffice Impress, the changes must be applied to ALL master slides, not just one specific master slide. This ensures consistent formatting across the entire presentation. +- **BULK MASTER SLIDE OPERATIONS**: When multiple master slides need the same modifications, use Ctrl+A to select all master slides in the master view, then apply changes simultaneously to all selected master slides for efficiency. + +### LibreOffice Impress Element Property Setting (MANDATORY) +**CRITICAL - PREFER SHORTCUT/MENU OVER SIDEBAR**: +- **AVOID SIDEBAR PROPERTY PANELS**: When setting element properties (styles, fonts, backgrounds, colors, dimensions, alignment), DO NOT use the sidebar property panels or right-click context menus that open property dialogs. +- **USE MENU NAVIGATION**: Prefer accessing properties through main menu items (Format → Character, Format → Paragraph, Format → Object, etc.) or direct keyboard shortcuts. + +### LibreOffice Impress Text Editing State Management (MANDATORY) +**CRITICAL - EXIT EDITING STATE AFTER STYLE CHANGES**: +- **AUTO-EXIT AFTER FORMATTING**: After applying text formatting (font, size, color, style) to selected text in LibreOffice Impress, ALWAYS exit text editing mode by pressing Escape or clicking outside the text box to return to object selection mode. +- **SEQUENTIAL OPERATIONS**: When performing multiple text formatting operations, exit editing state between each operation to maintain proper object selection and prevent text input conflicts. +- **AVOID CONTINUOUS EDITING**: Do not remain in text editing mode when the formatting task is complete. + +### LIBREOFFICE WRITER TEXT CASE CONVERSION (MANDATORY): +- **TEXT SELECTION REQUIREMENT**: For text modification operations (case conversion, formatting, font changes, etc.), you MUST first select ALL text in the document using Ctrl+A before applying any changes. + +### LIBREOFFICE WRITER DEFAULT FONT SETTING (MANDATORY): +- **DEFAULT FONT CONFIGURATION**: To set a default font in LibreOffice Writer, you must access the Basic Fonts (Western) settings and save the configuration. + +### LIBREOFFICE WRITER WORKFLOW COMPLETION (MANDATORY): +- **TRUST STANDARD WORKFLOW**: When performing batch operations in LibreOffice Writer (batch formatting, Underline, etc.), trust th LibreOffice workflow and do NOT repeatedly verify each individual change or operation. + + + +### COLOR GRADIENT ARRANGEMENT BY CCT (Important) +- When a subtask requires warm/cool gradient, treat it as Correlated Color Temperature (CCT), not by simple RGB channels (e.g., average red). +- Use CCT as the metric: lower CCT ≈ cooler (bluish) and higher CCT ≈ warmer (yellowish/red). Order segments in CCT ascending for "progressively warmer left to right". +- Preferred approach: obtain each segment's representative color, convert to CIE xy/XYZ and compute CCT (e.g., McCamy approximation). Do not recolor; only reorder. +- Avoid heuristics like average R, R-G, or saturation as the primary metric unless CCT cannot be computed. +- Compute CCT programmatically (e.g., convert to XYZ/xy and apply McCamy/Robertson). Do not guess or eyeball; no heuristic substitutes. + +### LIBREOFFICE CALC SPECIALIZED OPERATIONS (MANDATORY) + +#### Fill Handle Operations +- **FILL HANDLE PRIORITY**: The Fill Handle is a powerful and frequently used feature in LibreOffice Calc. When you select one or more cells, move the mouse to the bottom-right corner of the selection, and the cursor will change to a small black cross (Fill Handle). +- **DOUBLE-CLICK FILL STRATEGY**: Prioritize using double-click on the Fill Handle for data block operations. This automatically fills down to the end of the adjacent data range. +- **FILL HANDLE WORKFLOW**: + 1. Select the source cell(s) containing the pattern or formula + 2. Move mouse to bottom-right corner until cursor becomes a black cross + 3. Double-click to auto-fill down to the end of adjacent data + 4. For manual control, drag the Fill Handle to the desired range + +#### Essential Calc Keyboard Shortcuts +- **CLEAR CELL FORMATTING**: Use Ctrl+M to clear cell formatting while preserving cell content +- **FLEXIBLE COLUMN SELECTION**: Choose selection method based on task context: + - **Data Processing**: Use Ctrl+Shift+Down to select from current cell to the last non-empty cell in the column. DO NOT use this while the selected cell is the last non-empty one. + - **Data Validation/Setup**: For tasks requiring selection of empty cells (e.g., data validation, dropdown setup), select the entire intended range including empty cells using manual selection or Ctrl+Shift+End as appropriate for the task requirements. +- **NAVIGATION SHORTCUTS**: + - Ctrl+Down: Jump to last non-empty cell in column + - Ctrl+Right: Jump to last non-empty cell in row + - Ctrl+Home: Go to cell A1 + - Ctrl+End: Go to last used cell in worksheet + +#### Chart Creation and Management +- **CHART CREATION STARTING POINT**: When creating charts, the selected cell(s) or range serves as the starting data source. Ensure proper data selection before initiating chart creation. +- **CHART EDITING STATE**: When working with charts in LibreOffice Calc: + 1. Double-click on chart to enter edit mode + 2. Chart will be highlighted with selection handles + 3. **EXITING CHART EDIT MODE**: To return to normal spreadsheet operations, click outside the chart area or press Escape + 4. Ensure you exit chart edit mode before continuing with other spreadsheet operations + +#### Freeze Panes Operations +- **FREEZE PANES RANGE MECHANICS**: When executing freeze panes tasks with specified ranges (e.g., "freeze A1:B1"), understand that LibreOffice Calc freezes both rows above AND columns to the left of the bottom-right cell plus one. For range "A1:B1", select cell C2 (one column right and one row down from B1) before applying freeze panes via View menu, which will freeze row 1 and columns A-B. +- **FREEZE POINT SELECTION**: Always select the cell that represents the freeze point (bottom-right of intended frozen area plus one cell) before using View → Freeze Rows and Columns. + +#### Cell and table Content Grouping and Layout Analysis +- When analyzing the screen, consider visual cues such as whitespace, empty rows/columns, borders, and headers to identify distinct and logically related data blocks or UI element groups. Infer structural relationships (e.g., two separate tables side-by-side) from this visual layout. +- **NON-RECTANGULAR AWARENESS**: Data processing areas are NOT always perfect rectangles. Expect and plan for: + - Tables with varying row lengths (some rows shorter/longer than others) + - Data blocks with missing corners or irregular shapes + - Multiple disconnected data areas within the same sheet + - Headers that span different column ranges than data rows +- **FLEXIBLE BOUNDARY DETECTION**: When working out cell operations, describe target coordinates by content and logical boundaries. + +#### Data Range Selection Best Practices +- **SMART SELECTION**: Use Ctrl+Shift+End to select from current position to the last used cell +- **COLUMN/ROW SELECTION**: Click column header (A, B, C...) to select entire column, click row number to select entire row +- **RANGE NAMING**: For frequently used ranges, consider using Insert > Names > Define to create named ranges + +#### Number Formatting and TEXT Function Usage +- **CONSISTENT DECIMAL DISPLAY**: When formatting numbers with units (M, B, K, etc.), use TEXT() function to ensure consistent decimal places for all values including zeros + - **CORRECT**: `=TEXT(ROUND(A2/1000000;1);"0.0") & " M"` displays "0.0 M" for zero values + - **INCORRECT**: `=ROUND(A2/1000000;1) & " M"` displays "0 M" for zero values +- **TEXT FUNCTION SYNTAX**: Use TEXT(value;format_text) where format_text controls decimal display: + - "0.0" forces one decimal place for all numbers + - "0.00" forces two decimal places for all numbers + - This ensures visual consistency across all formatted cells +- **ROUNDING WITH FORMATTING**: Combine ROUND() and TEXT() functions for precise decimal control: + - ROUND(value;decimal_places) for mathematical rounding + - TEXT() wrapper for consistent visual formatting + +#### Advanced Data Operations +- **AUTO-FILL PATTERNS**: Fill Handle can detect and continue patterns (dates, numbers, text series) +- **FORMULA COPYING**: When copying formulas with Fill Handle, cell references automatically adjust (relative references) +- **ABSOLUTE REFERENCES**: Use $ symbol (e.g., $A1) to prevent reference changes during Fill Handle operations +- **SET_CELL_VALUES OPERATION**: When using the `set_cell_values` method, do not worry about which cells are currently selected. This operation works in the background to populate spreadsheet cells with values and does not affect or depend on the current cell selection state + +#### Dialog Box and Option Recognition +- **CHECKBOX STATE IDENTIFICATION**: When analyzing dialog boxes and popup windows, carefully identify the selection state of checkboxes and options: + - **SELECTED STATE**: Orange checkmark (✓) indicates the option is selected/enabled + - **UNSELECTED STATE**: Empty/blank checkbox indicates the option is not selected/disabled + - Pay close attention to these visual indicators when determining current settings or making selections +- **DIALOG ELEMENT ANALYSIS**: Carefully examine all elements within the current dialog box and verify their interactive states: + - Identify all input fields, buttons, dropdowns, and checkboxes present in the dialog + - Determine which fields are editable/fillable (enabled) versus read-only or disabled + - Check if input fields are currently empty, pre-filled, or contain placeholder text + - Verify button states (enabled/clickable vs disabled/grayed out) before attempting interactions + +### VSCODE ZOOM CONTROLS: +- **ADJUST IF NEEDED**: Continue zooming until optimal visibility is achieved +- **ZOOM IN**: Ctrl+Plus (+) or Ctrl+Equal (=) +- **ZOOM OUT**: Ctrl+Minus (-) +- **RESET ZOOM**: Ctrl+0 (zero) +- **COMMAND PALETTE**: Ctrl+Shift+P → "View: Zoom In/Out/Reset" + +## LIBREOFFICE WRITER GUIDELINES + +### LibreOffice Writer Footer Operations (MANDATORY) +- **FOOTER ACTIVATION PRIORITY**: For adding page numbers or other footer content in LibreOffice Writer, ALWAYS prioritize the menu-based approach: Insert → Header and Footer → Footer → Default Page Style (or appropriate page style). This method is more reliable than attempting to double-click on page margins. +- **AVOID PHYSICAL FOOTER TARGETING**: Do NOT attempt to locate and double-click on the physical footer area at the bottom of pages. This approach is prone to failure due to scroll position and visual targeting issues. +- **FOOTER STATE VERIFICATION**: Verify footer activation by looking for the gray footer area at the bottom of the page with a blinking cursor, not by scrolling to find physical page boundaries. diff --git a/mm_agents/maestro/prompts/module/worker/technician_role.txt b/mm_agents/maestro/prompts/module/worker/technician_role.txt new file mode 100644 index 0000000..456ec35 --- /dev/null +++ b/mm_agents/maestro/prompts/module/worker/technician_role.txt @@ -0,0 +1,197 @@ +# Overview +- You are the Technician in a GUI-Agent system, specializing in system-level operations via backend service execution. +- You are a programmer, you need to solve a task step-by-step given by the user. +- You can write code in ```bash...``` code blocks for bash scripts, and ```python...``` code blocks for python code. +- If you want to use sudo, follow the format: "echo [CLIENT_PASSWORD] | sudo -S [YOUR COMMANDS]". + +**CRITICAL: Task Objective Alignment Check** +Before writing any script or making any decision, you MUST carefully review whether the current subtask description conflicts with the main Task Objective. If there is any conflict or contradiction: +- The Task Objective takes absolute priority over subtask description +- Adapt your script/approach to align with the Task Objective +- Never execute scripts that would contradict or undermine the main Task Objective + +## Your Capabilities +- Execute bash and python scripts through network backend service +- Perform multiple script executions within a single subtask until completion +- Handle file system operations, software installations, system configurations +- Process batch operations and automated system tasks +- Access system credentials and sudo privileges via structured commands + + +## Your Constraints +- **No Visual Feedback**: Desktop screenshots show no terminal state changes during your operations +- **Fresh Terminal Per Script**: Each script executes in a new terminal session +- **Consistent Starting Directory**: Every new terminal starts from the same base directory +- **No GUI Interaction**: You cannot see or interact with graphical applications +- **Must Verify Results**: Include verification and progress reporting within your scripts + +## Technical Requirements +- Write complete scripts in ```bash``` or ```python``` code blocks +- Use absolute paths or handle directory navigation explicitly in each script +- For sudo operations, use format: `echo [CLIENT_PASSWORD] | sudo -S [YOUR COMMANDS]` +- Include progress indicators and result verification in your scripts +- Print intermediate and final results to track execution + +## File Operation Best Practices - MANDATORY +- **CRITICAL: NEVER modify files without inspection first**: Any file modification task MUST start with examination commands +- **TWO-STEP PROCESS REQUIRED**: + 1. FIRST execution: Use inspection commands (`cat`, `head`, `tail`, `grep`, `less`) to examine the file + 2. SECOND execution: Only after understanding the structure, perform the actual modifications +- **FORBIDDEN: Direct modification**: Never write scripts that modify files without first examining their current content +- **For configuration files**: Always identify existing sections, current values, and file structure before making any changes +- **Verification mandatory**: After editing, confirm changes were applied correctly using inspection commands + +## Operational Guidelines for Efficiency +- **Skip Security Confirmations**: Bypass interactive prompts for downloads, installations, and file operations +- **Direct Overwrite Policy**: Automatically overwrite existing files, configurations, and directories without confirmation +- **Streamlined Downloads**: Download content directly without integrity checks, signature verification, or manual approval +- **Force Operations**: Use force flags (-f, --force, -y, --yes) to avoid interruptions from warnings or confirmations +- **Assume Permissions**: Proceed with operations assuming necessary permissions are available +- **Batch Processing**: Handle multiple files, packages, or operations in single commands when possible +- **Skip Backups**: Avoid creating backup copies unless explicitly required by the task objective +- **Ignore Warnings**: Continue execution despite non-critical warnings or deprecation notices +- **Auto-Accept Licenses**: Automatically accept software licenses, terms of service, and usage agreements +- **Override Conflicts**: Resolve package conflicts, dependency issues, and version mismatches by choosing the most recent or compatible option +- **Suppress Verbose Output**: Use quiet modes where available to reduce unnecessary output while maintaining essential progress information + + +## Decision Protocol +- If you will provide executable code, return exactly one code block (bash or python). This is treated as a "Grounded Action" and classified as generate_action. +- If you will NOT provide code, you MUST use the structured decision format below with clear markers. + +## Structured Decision Format +When making a decision, you MUST use this exact format with the markers shown: + +DECISION_START +Decision: [DONE|FAILED|SUPPLEMENT|NEED_QUALITY_CHECK] +Message: [Your detailed explanation here] +DECISION_END + +DECISION_START and DECISION_END are required markers that must be included exactly as shown. + +## Decision Types and Message Requirements +- DONE: Explain what was accomplished and why no further action is needed +- FAILED: Explain what went wrong, what was attempted, and why the task cannot proceed +- SUPPLEMENT: Specify exactly what information is missing, why it's needed, and how it would help complete the task +- NEED_QUALITY_CHECK: Describe what should be checked, why validation is needed, and what specific aspects require inspection + +## MANDATORY: System Operation Limitations and Validation +- **Environment Variable Modifications**: Check if environment variable changes are allowed by system policies before attempting +- **Restricted Directory Operations**: Confirm access rights to system directories before file operations +- **Service Management Permissions**: Validate ability to start/stop/modify system services before attempting + +### Information and Resource Availability +- **External Dependencies**: Verify all required packages, repositories, and external resources are accessible +- **Network Connectivity**: Confirm network access is available for downloads and remote operations +- **Disk Space Validation**: Check available disk space before large file operations +- **System Resource Requirements**: Verify system meets requirements for installation/configuration tasks + +### Task Scope and Feasibility Validation +- **System Compatibility**: Confirm the target system supports the requested operations +- **Service Dependencies**: Verify all required services and dependencies are available +- **Configuration File Accessibility**: Ensure target configuration files exist and are modifiable +- **User Account Restrictions**: Respect user creation restrictions and only work with existing accounts + +### Reality Check Before Execution +- **Permission Verification**: Use appropriate commands to check permissions before modification attempts +- **Resource Availability Check**: Verify system resources are sufficient for the planned operations +- **Dependency Validation**: Confirm all required components are available before proceeding +- **Rollback Capability**: Ensure changes can be undone if issues arise + +**CRITICAL**: Use FAILED decision immediately when detecting system limitations that prevent task completion, rather than attempting operations that will fail due to policy restrictions or insufficient permissions. + +**CRITICAL: When using NEED_QUALITY_CHECK, you MUST provide a CandidateAction in your response.** +The CandidateAction should contain the bash or python script you want to execute after quality check passes. + +Format your response like this: +DECISION_START +Decision: NEED_QUALITY_CHECK +Message: [Detailed explanation] +DECISION_END + +CandidateAction: +```bash +echo "Example script to run after quality check" +``` + +## Output Format +Your response should be formatted like this: + +(Screenshot Analysis) +Describe what you see on the current screen, including applications, file system state, terminal output, etc. +- Enumerate main visible items on screen in a list: currently open windows/apps (with app names), active/focused window, desktop icons (files/folders with names and extensions), visible file lists in any file manager (folder path and filenames), browser tabs/titles if any, dialogs/modals, buttons, input fields, menus, scrollbars, status bars. +- Note counts where useful (e.g., "Desktop shows 6 icons: Report.docx, data.csv, images/, README.md, ..."), and highlight any potentially relevant targets for the subtask. +- If the view is cramped or truncated, mention that scrolling/maximizing is likely needed; if information appears incomplete, specify exactly what is missing. + +(Next Action) +Either: +1) Exactly one code block with the full script to run (no extra text outside the block), OR +2) The structured decision format with DECISION_START and DECISION_END markers + +## Examples + +### Example 1: Code Output +```bash +#!/bin/bash +echo "Installing package..." +sudo apt-get update +sudo apt-get install -y nginx +echo "Installation complete" +``` + +### Example 2: File Inspection Before Modification +```bash +#!/bin/bash +echo "Examining _config.yaml file structure..." +cat ~/Code/Website/_config.yaml | head -50 +echo "Searching for name and email sections..." +grep -n -i "name\|email\|contact" ~/Code/Website/_config.yaml +``` + +### Example 3: Decision Output +DECISION_START +Decision: DONE +Message: The nginx service is already running and configured correctly. The configuration file shows all required settings are in place, and the service status is active. No further action is needed. +DECISION_END + +### Example 4: Another Decision Output +DECISION_START +Decision: SUPPLEMENT +Message: Need the target server's IP address and SSH credentials to proceed with the deployment. Without these connection details, I cannot establish a connection to perform the installation. +DECISION_END + +### Example 5: Quality Check with CandidateAction +DECISION_START +Decision: NEED_QUALITY_CHECK +Message: Need to verify the current disk space before proceeding with the large file download. The download requires 2GB but I cannot see current available space clearly. +DECISION_END + +CandidateAction: +```bash +wget -O /tmp/largefile.zip https://example.com/file.zip +echo "Download completed successfully" +``` + +## Important Notes +- Never mix code blocks with decisions in the same response +- Always analyze the current context from provided history and task description +- Consider system dependencies, permissions, and resource requirements +- Maintain security best practices in all script operations +- Focus on completing the assigned system-level task efficiently and safely +- Do not recolor or apply overlays/filters unless explicitly requested; only reorder segments. +- Compute CCT via code (e.g., XYZ/xy + McCamy/Robertson). No guessing/eyeballing; avoid heuristic proxies. +**CRITICAL: USER CREATION RESTRICTION** + You are STRICTLY PROHIBITED from creating new users or user accounts on the system. This includes but is not limited to: + - Creating new user accounts through system settings + If a task requires switching to a different user account, you must: + - Use existing user accounts only + - Switch between already existing users + - Use provided credentials for existing accounts + - Return agent.fail() if the required user does not exist + NEVER attempt to create users even if the task seems to require it. Always use existing user accounts or fail the task with an appropriate message. + +### COLOR GRADIENT ARRANGEMENT BY CCT (Important) +- When a subtask requires warm/cool gradient, treat it as Correlated Color Temperature (CCT), not by simple RGB channels (e.g., average red). +- Use CCT as the metric: lower CCT ≈ cooler (bluish) and higher CCT ≈ warmer (yellowish/red). Order segments in CCT ascending for “progressively warmer left to right”. +- Preferred approach: obtain each segment’s representative color, convert to CIE xy/XYZ and compute CCT (e.g., McCamy approximation). Do not recolor; only reorder. +- Avoid heuristics like average R, R-G, or saturation as the primary metric unless CCT cannot be computed. \ No newline at end of file diff --git a/mm_agents/maestro/prompts/module/worker/text_span.txt b/mm_agents/maestro/prompts/module/worker/text_span.txt new file mode 100644 index 0000000..00791bc --- /dev/null +++ b/mm_agents/maestro/prompts/module/worker/text_span.txt @@ -0,0 +1,9 @@ +You are an expert in graphical user interfaces. Your task is to process a phrase of text, and identify the most relevant word on the computer screen. +You are provided with a phrase, a table with all the text on the screen, and a screenshot of the computer screen. You will identify the single word id that is best associated with the provided phrase. +This single word must be displayed on the computer screenshot, and its location on the screen should align with the provided phrase. +Each row in the text table provides 2 pieces of data in the following order. 1st is the unique word id. 2nd is the corresponding word. + +To be successful, it is very important to follow all these rules: +1. First, think step by step and generate your reasoning about which word id to click on. +2. Then, output the unique word id. Remember, the word id is the 1st number in each row of the text table. +3. If there are multiple occurrences of the same word, use the surrounding context in the phrase to choose the correct one. Pay very close attention to punctuation and capitalization. \ No newline at end of file diff --git a/mm_agents/maestro/prompts/registry.py b/mm_agents/maestro/prompts/registry.py new file mode 100644 index 0000000..c0b8689 --- /dev/null +++ b/mm_agents/maestro/prompts/registry.py @@ -0,0 +1,187 @@ +import os +import threading +from typing import Dict, List, Set + + +class PromptRegistry: + """Central registry for system and module prompts. + + - Loads .txt prompt files from the local `system` and `module` directories on initialization + - Allows programmatic registration of prompts (module-level or dynamic) + - Provides a simple, thread-safe API for getting/setting/listing prompts + - Names for module prompts are based on relative path inside `module`, e.g. `manager/task_planner` + """ + + def __init__(self): + self._lock = threading.Lock() + self._prompts: Dict[str, str] = {} + self._base_dir = os.path.dirname(__file__) + self._system_dir = os.path.join(self._base_dir, "system") + self._module_dir = os.path.join(self._base_dir, "module") + # Track namespaces + self._module_keys: Set[str] = set() + self._system_keys: Set[str] = set() + self._load_system_prompts() + self._load_module_prompts() + + def _load_system_prompts(self) -> None: + system_prompts: Dict[str, str] = {} + system_keys: Set[str] = set() + try: + if os.path.isdir(self._system_dir): + for fname in os.listdir(self._system_dir): + if not fname.lower().endswith(".txt"): + continue + key = os.path.splitext(fname)[0] + fpath = os.path.join(self._system_dir, fname) + try: + with open(fpath, "r", encoding="utf-8") as f: + system_prompts[key] = f.read() + system_keys.add(key) + except Exception: + # Skip unreadable files but continue loading others + continue + finally: + with self._lock: + # System prompts become the baseline; module/dynamic can override + self._prompts.update(system_prompts) + self._system_keys = system_keys + + def _load_module_prompts(self) -> None: + module_prompts: Dict[str, str] = {} + module_keys: Set[str] = set() + try: + if os.path.isdir(self._module_dir): + for root, _dirs, files in os.walk(self._module_dir): + for fname in files: + if not fname.lower().endswith(".txt"): + continue + fpath = os.path.join(root, fname) + rel = os.path.relpath(fpath, self._module_dir) + key = os.path.splitext(rel)[0].replace(os.sep, "/") + try: + with open(fpath, "r", encoding="utf-8") as f: + module_prompts[key] = f.read() + module_keys.add(key) + except Exception: + continue + finally: + with self._lock: + # Module prompts can override system prompts of the same key + self._prompts.update(module_prompts) + self._module_keys = module_keys + + def refresh(self) -> None: + """Reload system and module prompts from disk. Keeps any programmatically registered prompts unless overwritten by disk files.""" + with self._lock: + old_prompts = dict(self._prompts) + # Reload from disk + with self._lock: + self._prompts = {} + self._module_keys = set() + self._system_keys = set() + self._load_system_prompts() + self._load_module_prompts() + # Reapply dynamic prompts that are not present on disk + with self._lock: + for name, content in old_prompts.items(): + if name not in self._prompts: + self._prompts[name] = content + + def _names_from_dir(self, directory: str) -> List[str]: + if not os.path.isdir(directory): + return [] + return [os.path.splitext(f)[0] for f in os.listdir(directory) if f.lower().endswith(".txt")] + + def get(self, name: str, default: str = "") -> str: + with self._lock: + return self._prompts.get(name, default) + + def set(self, name: str, content: str) -> None: + """Register or override a prompt by name.""" + with self._lock: + self._prompts[name] = content + + def exists(self, name: str) -> bool: + with self._lock: + return name in self._prompts + + def exists_in_module(self, name: str) -> bool: + with self._lock: + return name in self._module_keys + + def module_children_exist(self, prefix: str) -> bool: + with self._lock: + prefix_slash = prefix + "/" + return any(k.startswith(prefix_slash) for k in self._module_keys) + + def all_names(self) -> List[str]: + with self._lock: + return sorted(self._prompts.keys()) + + def list_by_prefix(self, prefix: str) -> List[str]: + with self._lock: + return sorted([n for n in self._prompts.keys() if n.startswith(prefix)]) + + def as_dict(self) -> Dict[str, str]: + with self._lock: + return dict(self._prompts) + + +class PromptNamespace: + """Hierarchical attribute-style access to module prompts. + + Usage: + from gui_agents.prompts import module + text = module.evaluator.final_check_role + text2 = module.manager.planner_role + + Resolution rules: + - Resolve only against module prompts (under `module/`), ignoring system prompts + - If an exact module key exists (e.g., "evaluator/final_check_role"), return its string content + - Else, if there are module children under that path, return a deeper namespace object + - Else, raise AttributeError + """ + + def __init__(self, registry: PromptRegistry, parts: List[str] = None): #type: ignore + self._registry = registry + self._parts = parts or [] + + def __getattr__(self, name: str): + prefix = "/".join(self._parts + [name]) if self._parts else name + # Exact leaf in module space + if self._registry.exists_in_module(prefix): + return self._registry.get(prefix, "") + # Nested namespace in module space? + if self._registry.module_children_exist(prefix): + return PromptNamespace(self._registry, self._parts + [name]) + raise AttributeError(f"No module prompt or namespace '{prefix}'") + + +# Singleton registry instance for convenient imports +prompt_registry = PromptRegistry() + +# Convenience top-level helpers + +def get_prompt(name: str, default: str = "") -> str: + return prompt_registry.get(name, default) + + +def register_prompt(name: str, content: str) -> None: + prompt_registry.set(name, content) + + +def list_prompts() -> List[str]: + return prompt_registry.all_names() + + +def list_prompts_by_prefix(prefix: str) -> List[str]: + return prompt_registry.list_by_prefix(prefix) + + +def refresh_prompts() -> None: + prompt_registry.refresh() + + +# Hierarchical accessor for module prompts +module = PromptNamespace(prompt_registry, []) \ No newline at end of file diff --git a/mm_agents/maestro/store/registry.py b/mm_agents/maestro/store/registry.py new file mode 100644 index 0000000..9f8973e --- /dev/null +++ b/mm_agents/maestro/store/registry.py @@ -0,0 +1,22 @@ +# gui_agents/s2/store/registry.py + +# Usage: in any file, get the object through Registry.get +# from gui_agents.store.registry import Registry +# GlobalStateStore = Registry.get("GlobalStateStore") + +class Registry: + _services: dict[str, object] = {} + + @classmethod + def register(cls, name: str, obj: object): + cls._services[name] = obj + + @classmethod + def get(cls, name: str) -> object: + if name not in cls._services: + raise KeyError(f"{name!r} not registered in Registry") + return cls._services[name] + + @classmethod + def clear(cls): + cls._services.clear() diff --git a/mm_agents/maestro/tools/__init__.py b/mm_agents/maestro/tools/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mm_agents/maestro/tools/model.md b/mm_agents/maestro/tools/model.md new file mode 100644 index 0000000..42ea5cb --- /dev/null +++ b/mm_agents/maestro/tools/model.md @@ -0,0 +1,385 @@ +# Supported Model Providers and Model Lists + +## LLM Model Providers + +### 1. OpenAI + +**Provider** + +- `openai` + +**Supported Models:** + +- `gpt-5` Window: 400,000 Max Output Tokens: 128,000 +- `gpt-5-mini` Window: 400,000 Max Output Tokens: 128,000 +- `gpt-4.1-nano` Window: 400,000 Max Output Tokens: 128,000 +- `gpt-4.1` Window: 1,047,576 Max Output Tokens: 32,768 +- `gpt-4.1-mini` Window: 1,047,576 Max Output Tokens: 32,768 +- `gpt-4.1-nano` Window: 1,047,576 Max Output Tokens: 32,768 +- `gpt-4o` Window: 128,000 Max Output Tokens: 16,384 +- `gpt-4o-mini` Window: 128,000 Max Output Tokens: 16,384 +- `o1` Window: 200,000 Max Output Tokens: 100,000 +- `o1-pro` Window: 200,000 Max Output Tokens: 100,000 +- `o1-mini` Window: 200,000 Max Output Tokens: 100,000 +- `o3` Window: 200,000 Max Output Tokens: 100,000 +- `o3-pro` Window: 200,000 Max Output Tokens: 100,000 +- `o3-mini` Window: 200,000 Max Output Tokens: 100,000 +- `o4-mini` Window: 200,000 Max Output Tokens: 100,000 + +**Embedding Models:** + +- `text-embedding-3-small` +- `text-embedding-3-large` +- `text-embedding-ada-002` + +📚 **Reference Link:** + +--- + +### 2. Anthropic Claude + +**Provider** + +- `anthropic` + +**Supported Models:** + +- `claude-opus-4-1-20250805` Context window: 200K Max output: 32000 +- `claude-opus-4-20250514` Context window: 200K Max output: 32000 +- `claude-sonnet-4-20250514` Context window: 200K Max output: 64000 +- `claude-3-7-sonnet-20250219` Context window: 200K Max output: 64000 +- - `claude-3-5-sonnet-20240620` Context window: 200K Max output: 64000 +- `claude-3-5-haiku-20241022` Context window: 200K Max output: 8192 + +📚 **Reference Link:** + +--- + +### 3. AWS Bedrock + +**Provider** + +- `bedrock` + + +**Supported Claude Models:** + +- `Claude-Opus-4` +- `Claude-Sonnet-4` +- `Claude-Sonnet-3.7` +- `Claude-Sonnet-3.5` + +📚 **Reference Link:** + +--- + +### 4. Google Gemini + +**Provider** + +- `gemini` + +**Supported Models:** + +- `gemini-2.5-pro` in: 1,048,576 out: 65536 +- `gemini-2.5-flash` in: 1,048,576 out: 65536 +- `gemini-2.0-flash` in: 1,048,576 out: 8192 +- `gemini-1.5-pro` in: 2,097,152 out: 8192 +- `gemini-1.5-flash` in: 1,048,576 out: 8192 + +**Embedding Models:** + +- `gemini-embedding-001` + +📚 **Reference Link:** + +--- + +### 5. Groq + +**Provider** + +- `groq` + +**Supported Models:** + +- `Kimi-K2-Instruct` +- `Llama-4-Scout-17B-16E-Instruct` +- `Llama-4-Maverick-17B-128E-Instruct` +- `Llama-Guard-4-12B` +- `DeepSeek-R1-Distill-Llama-70B` +- `Qwen3-32B` +- `Llama-3.3-70B-Instruct` + +📚 **Reference Link:** + +--- + +### 6. Monica (Proxy Platform) + +**Provider** + +- `monica` + +**OpenAI Models:** + +- `gpt-4.1` +- `gpt-4.1-mini` +- `gpt-4.1-nano` +- `gpt-4o-2024-11-20` +- `gpt-4o-mini-2024-07-18` +- `o4-mini` +- `o3` + +**Anthropic Claude Models:** + +- `claude-opus-4-20250514` +- `claude-sonnet-4-20250514` +- `claude-3-7-sonnet-latest` +- `claude-3-5-sonnet-20241022` +- `claude-3-5-sonnet-20240620` +- `claude-3-5-haiku-20241022` + + +**Google Gemini Models:** + +- `gemini-2.5-pro-preview-03-25` +- `gemini-2.5-flash-lite` +- `gemini-2.5-flash-preview-05-20` +- `gemini-2.0-flash-001` +- `gemini-1.5-pro-002` +- `gemini-1.5-flash-002` + +**DeepSeek Models:** + +- `deepseek-reasoner` +- `deepseek-chat` + +**Meta Llama Models:** + +- `Llama-4-Scout-17B-16E-Instruct` Context length: 10M tokens +- `Llama-4-Maverick-17B-128E-Instruct ` Context length: 1M tokens +- `llama-3.3-70b-instruct` +- `llama-3-70b-instruct` +- `llama-3.1-405b-instruct` + +**xAI Grok Models:** + +- `grok-3-beta` +- `grok-beta` + +📚 **Reference Link:** + +--- + +### 7. OpenRouter (Proxy Platform) + +**Provider** + +- `openrouter` + +**OpenAI Models:** + +- `gpt-4.1` +- `gpt-4.1-mini` +- `o1` +- `o1-pro` +- `o1-mini` +- `o3` +- `o3-pro` +- `o3-mini` +- `o4-mini` + +**xAI Grok Models:** + +- `grok-4` Total Context: 256K Max Output: 256K +- `grok-3` +- `grok-3-mini` + +**Anthropic Claude Models:** + +- `claude-opus-4` +- `claude-sonnet-4` + +**Google Gemini Models:** + +- `gemini-2.5-flash` +- `gemini-2.5-pro` + +📚 **Reference Link:** + +--- + +### 8. Azure OpenAI + +**Provider** + +- `azure` + + +**Supported Models:** + +- `gpt-4.1` +- `gpt-4.1-mini` +- `gpt-4.1-nano` +- `o1` +- `o3` +- `o4-mini` + +📚 **Reference Link:** + +--- + +### 9. Lybic AI + +**Provider:** + +- `lybic` + +**Supported Models:** + +- `gpt-5` +- `gpt-4.1` +- `gpt-4.1-mini` +- `gpt-4.1-nano` +- `gpt-4.5-preview` +- `gpt-4o` +- `gpt-4o-realtime-preview` +- `gpt-4o-mini` +- `o1` +- `o1-pro` +- `o1-mini` +- `o3` +- `o3-pro` +- `o3-mini` +- `o4-mini` + +**Note:** Lybic AI provides OpenAI-compatible API endpoints with the same model names and pricing structure. + +📚 **Reference Link:** + +--- + +### 10. DeepSeek + +**Provider** + +- `deepseek` + +**Supported Models:** + +- `deepseek-chat` Context length: 128K, Output length: Default 4K, Max 8K +- `deepseek-reasoner` Context length: 128K, Output length: Default 32K, Max 64K + +📚 **Reference Link:** + +--- + +### 11. Alibaba Cloud Qwen + +**Supported Models:** + +- `qwen-max-latest` Context window: 32,768 Max input token length: 30,720 Max generation token length: 8,192 +- `qwen-plus-latest` Context window: 131,072 Max input token length: 98,304 (thinking) Max generation token length: 129,024 Max output: 16,384 +- `qwen-turbo-latest` Context window: 1,000,000 Max input token length: 1,000,000 Max generation token length: 16,384 +- `qwen-vl-max-latest` (Grounding) Context window: 131,072 Max input token length: 129,024 Max generation token length: 8,192 +- `qwen-vl-plus-latest` (Grounding) Context window: 131,072 Max input token length: 129,024 Max generation token length: 8,192 + +**Embedding Models:** + +- `text-embedding-v4` +- `text-embedding-v3` + +📚 **Reference Link:** + +--- + +### 12. ByteDance Doubao + +**Supported Models:** + +- `doubao-seed-1-6-flash-250615` Context window: 256k Max input token length: 224k Max generation token length: 32k Max thinking content token length: 32k +- `doubao-seed-1-6-thinking-250715` Context window: 256k Max input token length: 224k Max generation token length: 32k Max thinking content token length: 32k +- `doubao-seed-1-6-250615` Context window: 256k Max input token length: 224k Max generation token length: 32k Max thinking content token length: 32k +- `doubao-1.5-vision-pro-250328` (Grounding) Context window: 128k Max input token length: 96k Max generation token length: 16k Max thinking content token length: 32k +- `doubao-1-5-thinking-vision-pro-250428` (Grounding) Context window: 128k Max input token length: 96k Max generation token length: 16k Max thinking content token length: 32k +- `doubao-1-5-ui-tars-250428` (Grounding) Context window: 128k Max input token length: 96k Max generation token length: 16k Max thinking content token length: 32k + +**Embedding Models:** + +- `doubao-embedding-large-text-250515` +- `doubao-embedding-text-240715` + +📚 **Reference Link:** + +--- + +### 13. Zhipu GLM + +**Supported Models:** + +- `GLM-4.5` Max in: 128k Max output: 0.2K +- `GLM-4.5-X` Max in: 128k Max output: 0.2K +- `GLM-4.5-Air` Max in: 128k Max output: 0.2K +- `GLM-4-Plus` +- `GLM-4-Air-250414` +- `GLM-4-AirX` (Grounding) +- `GLM-4V-Plus-0111` (Grounding) + +**Embedding Models:** + +- `Embedding-3` +- `Embedding-2` + +📚 **Reference Link:** + +--- + +### 14. SiliconFlow + +**Supported Models:** + +- `Kimi-K2-Instruct` Context Length: 128K +- `DeepSeek-V3` +- `DeepSeek-R1` +- `Qwen3-32B` + +📚 **Reference Link:** + +--- + +## 🔤 Dedicated Embedding Providers + +### 15. Jina AI + +**Embedding Models:** + +- `jina-embeddings-v4` +- `jina-embeddings-v3` + +📚 **Reference Link:** + +--- + +## 🔍 AI Search Engines + +### 16. Bocha AI + +**Service Type:** AI Research & Search + +📚 **Reference Link:** + +--- + +### 17. Exa + +**Service Type:** AI Research & Search + +**Pricing Model:** + +- $5.00 / 1k agent searches +- $5.00 / 1k exa-research agent page reads +- $10.00 / 1k exa-research-pro agent page reads +- $5.00 / 1M reasoning tokens + +📚 **Reference Link:** diff --git a/mm_agents/maestro/tools/new_tools.py b/mm_agents/maestro/tools/new_tools.py new file mode 100644 index 0000000..378f0ec --- /dev/null +++ b/mm_agents/maestro/tools/new_tools.py @@ -0,0 +1,826 @@ +""" +Tools module for GUI agents. + +This module provides various tools for GUI agents to perform tasks such as web search, +context fusion, subtask planning, trajectory reflection, memory retrieval, grounding, +evaluation, and action generation. +""" + +import os +import json +import base64 +import requests +import time +from typing import Dict, Any, Optional, List, Union, Tuple +from abc import ABC, abstractmethod +import logging +from ..core.mllm import LLMAgent, WebSearchAgent, EmbeddingAgent +import threading +from ..prompts import get_prompt, module + +logger = logging.getLogger("desktopenv.tools") + +class BaseTool(ABC): + """Base class for all tools.""" + _prompts_dict = None + _prompts_dict_lock = threading.Lock() + # Directory retained for backward compatibility; no longer scanned directly + _prompts_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "prompts") + + @classmethod + def _load_prompts_dict(cls): + # Deprecated: kept for compatibility if other code accesses _prompts_dict. + # Now pull prompts via the registry to avoid direct filesystem coupling. + if cls._prompts_dict is None: + with cls._prompts_dict_lock: + if cls._prompts_dict is None: + cls._prompts_dict = {} + + def __init__(self, provider: str, model_name: str, tool_name: str): + """ + Initialize the base tool. + Args: + provider: API provider name (e.g., "gemini", "openai") + model_name: Model name to use (e.g., "gemini-2.5-pro") + tool_name: Name of the tool (used as key in prompts files) + """ + self.provider = provider + self.model_name = model_name + self.tool_name = tool_name + self._load_prompts_dict() + self._prompt_template = self._get_prompt_template() + # Create LLMAgent instance for tool usage + self.engine_params = { + "engine_type": provider, + "model": model_name + } + self.llm_agent = LLMAgent(engine_params=self.engine_params, system_prompt=self._prompt_template) + + def _get_prompt_template(self) -> str: + if self.tool_name is None: + return "" + # Prefer reading prompt text directly from gui_agents.prompts.module + try: + prompt_category_map = { + # manager prompts + "query_formulator": ("manager", "query_formulator"), + "narrative_summarization": ("manager", "narrative_summarization"), + "context_fusion": ("manager", "context_fusion"), + "planner_role": ("manager", "planner_role"), + "supplement_role": ("manager", "supplement_role"), + "dag_translator": ("manager", "dag_translator"), + "objective_alignment": ("manager", "objective_alignment"), + # worker prompts + "operator_role": ("worker", "operator_role"), + "technician_role": ("worker", "technician_role"), + "analyst_role": ("worker", "analyst_role"), + "grounding": ("worker", "grounding"), + "text_span": ("worker", "text_span"), + "episode_summarization": ("worker", "episode_summarization"), + # evaluator prompts + "worker_success_role": ("evaluator", "worker_success_role"), + "worker_stale_role": ("evaluator", "worker_stale_role"), + "periodic_role": ("evaluator", "periodic_role"), + "final_check_role": ("evaluator", "final_check_role"), + } + + # Tools that should be prefixed with system architecture info + tools_require_system_prefix = { + "planner_role", + "supplement_role", + "dag_translator", + "operator_role", + "technician_role", + "analyst_role", + "worker_success_role", + "worker_stale_role", + "periodic_role", + "final_check_role", + "objective_alignment", + } + + category_tuple = prompt_category_map.get(self.tool_name) + + prompt_text = "" + if category_tuple is None: + # Try root-level attribute on module (e.g., system_architecture) + if hasattr(module, self.tool_name): + prompt_text = getattr(module, self.tool_name) + else: + return "" + else: + category_name, key_name = category_tuple + category_obj = getattr(module, category_name, None) + if category_obj is None: + return "" + value = getattr(category_obj, key_name, None) + if isinstance(value, str) and value: + prompt_text = value + else: + return "" + + # Optionally prefix with system architecture information for selected tools + if ( + isinstance(prompt_text, str) + and prompt_text + and self.tool_name in tools_require_system_prefix + ): + system_info = getattr(module, "system_architecture", "") + if isinstance(system_info, str) and system_info: + return f"{system_info}\n\n{prompt_text}" + + return prompt_text + except Exception: + # Fallback to registry to allow central overrides if available + return "" + + def _call_lmm(self, input_data: Dict[str, Any], temperature: float = 0.0): + """ + Call the LMM model for inference using the prompt template with retry mechanism + + Args: + input_data: Dictionary containing input data to format the prompt template + temperature: Temperature parameter to control randomness of output + + Returns: + Model response as text + """ + # self.llm_agent.reset() + + # Extract text and image inputs + text_input = input_data.get('str_input', '') + image_input = input_data.get('img_input', None) + + # Add the message with the formatted prompt + self.llm_agent.reset() + self.llm_agent.add_message(text_input, image_content=image_input, role="user") + + # Implement safe retry mechanism + max_retries = 3 + attempt = 0 + content, total_tokens, cost_string = "", [0, 0, 0], "" + + while attempt < max_retries: + try: + content, total_tokens, cost_string = self.llm_agent.get_response(temperature=temperature) + break # If successful, break out of the loop + except Exception as e: + attempt += 1 + logger.error(f"LLM call attempt {attempt} failed: {str(e)}") + if attempt == max_retries: + logger.error("Max retries reached. Returning error message.") + return f"Error: LLM call failed after {max_retries} attempts: {str(e)}", [0, 0, 0], "" + time.sleep(1.0) + return content, total_tokens, cost_string + + @abstractmethod + def execute(self, tool_input: Dict[str, Any]) -> Tuple[str, List[int], str]: + """ + Execute the tool with the given input. + + Args: + tool_input: Dictionary containing the input for the tool + Expected to have 'str_input' and/or 'img_input' keys + + Returns: + The output of the tool as a string + """ + pass + + +class ToolFactory: + """Factory class for creating tools.""" + + @staticmethod + def create_tool(tool_name: str, provider: str, model_name: str, **kwargs) -> 'BaseTool': + """ + Create a tool instance based on the tool name. + + Args: + tool_name: Name of the tool to create + provider: API provider name + model_name: Model name to use + **kwargs: Additional parameters to pass to the tool + + Returns: + An instance of the specified tool + + Raises: + ValueError: If the tool name is not recognized + """ + tool_map = { + "embedding": (EmbeddingTool, None), # all + + "query_formulator": (QueryFormulatorTool, "query_formulator"), # manager + "websearch": (WebSearchTool, None), # manager + "narrative_summarization": (NarrativeSummarizationTool, "narrative_summarization"), # manager + "context_fusion": (ContextFusionTool, "context_fusion"), # manager + "planner_role": (SubtaskPlannerTool, "planner_role"), # manager + "supplement_role": (SubtaskPlannerTool, "supplement_role"), # manager + "dag_translator": (DAGTranslatorTool, "dag_translator"), # manager + "objective_alignment": (ObjectiveAlignmentTool, "objective_alignment"), # manager + + "operator_role": (ActionGeneratorTool, "operator_role"), # worker + "technician_role": (ActionGeneratorTool, "technician_role"), # worker + "analyst_role": (ActionGeneratorTool, "analyst_role"), # worker + "grounding": (GroundingTool, "grounding"), # worker + "text_span": (TextSpanTool, "text_span"), # worker + "episode_summarization": (EpisodeSummarizationTool, "episode_summarization"), # worker + + "worker_success_role": (EvaluatorTool, "worker_success_role"), # evaluator + "worker_stale_role": (EvaluatorTool, "worker_stale_role"), # evaluator + "periodic_role": (EvaluatorTool, "periodic_role"), # evaluator + "final_check_role": (EvaluatorTool, "final_check_role"), # evaluator + } + + if tool_name not in tool_map: + raise ValueError(f"Unknown tool name: {tool_name}") + + tool_class, prompt_key = tool_map[tool_name] + + # WebSearchTool and EmbeddingTool don't need a prompt + if tool_name == "websearch": + return tool_class(provider, model_name, None, **kwargs) + if tool_name == "embedding": + return tool_class(provider, model_name, None, **kwargs) + + return tool_class(provider, model_name, prompt_key, **kwargs) + + +class WebSearchTool(BaseTool): + """Tool for performing web searches.""" + + def __init__(self, provider: str, model_name: str, tool_name: str): + """ + Initialize the web search tool. + + Args: + provider: API provider name (e.g., "bocha", "exa") + model_name: Model name to use (not used for WebSearchAgent) + tool_name: Name of the tool (used as key in prompts.json) + """ + self.provider = provider + + # Create WebSearchAgent instance for search + self.engine_params = { + "engine_type": provider, + "model": model_name, + } + + # Initialize WebSearchAgent + self.search_agent = WebSearchAgent(engine_params=self.engine_params) + + def execute(self, tool_input: Dict[str, Any]) -> Tuple[str, List[int], str]: + """ + Execute a web search with the given query. + + Args: + tool_input: Dictionary containing the search query + Expected to have 'str_input' key with the search query + + Returns: + Search results as a string + """ + query = tool_input.get('str_input', '') + if not query: + return "Error: No search query provided", [0, 0, 0], "" + + try: + # Get the answer from the search results + answer, total_tokens, cost = self.search_agent.get_answer(query) + + # Return just the answer + return answer, total_tokens, cost # type: ignore + + except Exception as e: + logger.error(f"Error during web search: {str(e)}") + return f"Error: Web search failed: {str(e)}", [0, 0, 0], "" + + +class ContextFusionTool(BaseTool): + """Tool for fusing multiple contexts together.""" + + def execute(self, tool_input: Dict[str, Any]): + """ + Fuse multiple contexts together. + + Args: + tool_input: Dictionary containing the contexts to fuse + Expected to have 'str_input' key with JSON-formatted contexts + + Returns: + Fused context as a string + """ + contexts = tool_input.get('str_input', '') + if not contexts: + return "Error: No contexts provided" + + # Use the prompt template and LMM for context fusion + return self._call_lmm(tool_input) + + +class SubtaskPlannerTool(BaseTool): + """Tool for planning subtasks.""" + + def execute(self, tool_input: Dict[str, Any]): + """ + Plan subtasks for a given task. + + Args: + tool_input: Dictionary containing the task description + Expected to have 'str_input' key with the task description + May also have 'img_input' key with a screenshot + + Returns: + Subtask plan as a string + """ + task = tool_input.get('str_input', '') + if not task: + return "Error: No task description provided" + + # Use the prompt template and LMM for subtask planning + return self._call_lmm(tool_input) + + +class NarrativeSummarizationTool(BaseTool): + """Tool for summarizing narrative memories.""" + + def execute(self, tool_input: Dict[str, Any]): + """ + Summarize narrative memories. + + Args: + tool_input: Dictionary containing the narrative memory data + Expected to have 'str_input' key with the narrative memory data + May also have 'img_input' key with relevant images + + Returns: + Summarized narrative as a string + """ + narrative_data = tool_input.get('str_input', '') + if not narrative_data: + return "Error: No narrative memory data provided" + + # Use the prompt template and LMM for narrative summarization + return self._call_lmm(tool_input) + + +class EpisodeSummarizationTool(BaseTool): + """Tool for summarizing episodic memories.""" + + def execute(self, tool_input: Dict[str, Any]): + """ + Summarize episodic memories. + + Args: + tool_input: Dictionary containing the episodic memory data + Expected to have 'str_input' key with the episodic memory data + May also have 'img_input' key with relevant images + + Returns: + Summarized episode as a string + """ + episode_data = tool_input.get('str_input', '') + if not episode_data: + return "Error: No episodic memory data provided" + + # Use the prompt template and LMM for episode summarization + return self._call_lmm(tool_input) + + +class TextSpanTool(BaseTool): + """Tool for processing text spans.""" + + def execute(self, tool_input: Dict[str, Any]): + """ + Process text spans for a given input. + + Args: + tool_input: Dictionary containing the text input + Expected to have 'str_input' key with the text content + May also have 'img_input' key with a screenshot + + Returns: + Processed text spans as a string + """ + text = tool_input.get('str_input', '') + if not text: + return "Error: No text content provided" + + # Use the prompt template and LMM for text span processing + return self._call_lmm(tool_input) + + +class DAGTranslatorTool(BaseTool): + """Tool for translating task descriptions into a DAG (Directed Acyclic Graph) structure.""" + + def execute(self, tool_input: Dict[str, Any]): + """ + Translate task descriptions into a DAG structure. + + Args: + tool_input: Dictionary containing the task description + Expected to have 'str_input' key with the task description + May also have 'img_input' key with a screenshot + + Returns: + DAG representation as a string + """ + task = tool_input.get('str_input', '') + if not task: + return "Error: No task description provided" + + # Use the prompt template and LMM for DAG translation + return self._call_lmm(tool_input) + + +class ObjectiveAlignmentTool(BaseTool): + """Tool for aligning and rewriting user objective with current screen context.""" + + def execute(self, tool_input: Dict[str, Any]): + """ + Align ambiguous or high-level user objective with the current desktop screenshot context + and output a refined objective and assumptions. + + Args: + tool_input: Dict with keys: + - 'str_input': the raw user objective or context text + - 'img_input': optional screenshot image content + + Returns: + Refined objective as text (ideally JSON-structured), token count, and cost string + """ + text = tool_input.get('str_input', '') + if not text: + return "Error: No objective text provided", [0, 0, 0], "" + # Forward to LMM with the prompt template + return self._call_lmm(tool_input) + + +class TrajReflectorTool(BaseTool): + """Tool for reflecting on execution trajectories.""" + + def execute(self, tool_input: Dict[str, Any]): + """ + Reflect on an execution trajectory. + + Args: + tool_input: Dictionary containing the trajectory + Expected to have 'str_input' key with the trajectory + + Returns: + Reflection as a string + """ + trajectory = tool_input.get('str_input', '') + if not trajectory: + return "Error: No trajectory provided" + + # Use the prompt template and LMM for trajectory reflection + return self._call_lmm(tool_input) + +class GroundingTool(BaseTool): + """Tool for grounding agent actions in the environment.""" + + def execute(self, tool_input: Dict[str, Any]): + """ + Ground agent actions in the environment. + + Args: + tool_input: Dictionary containing the action and environment state + Expected to have 'str_input' key with the action + Expected to have 'img_input' key with a screenshot + + Returns: + Grounded action as a string + """ + action = tool_input.get('str_input', '') + screenshot = tool_input.get('img_input') + + if not action: + return "Error: No action provided" + if not screenshot: + return "Error: No screenshot provided" + + # Use the prompt template and LMM for action grounding + return self._call_lmm(tool_input) + + def get_grounding_wh(self): + """ + Get grounding width and height based on provider and model name. + + Returns: + If provider is doubao and model_name contains 'ui-tars', returns two values: + grounding_width (int): Width value (1024) + grounding_height (int): Height value (768) + Otherwise returns None, None + """ + if self.provider == "doubao" and ("ui-tars" in self.model_name or "ep-" in self.model_name): + grounding_width = 1000 + grounding_height = 1000 + return grounding_width, grounding_height + return None, None + + +class EvaluatorTool(BaseTool): + """Tool for evaluating agent performance.""" + + def execute(self, tool_input: Dict[str, Any]): + """ + Evaluate agent performance. + + Args: + tool_input: Dictionary containing the evaluation data + Expected to have 'str_input' key with the evaluation data + + Returns: + Evaluation result as a string + """ + eval_data = tool_input.get('str_input', '') + if not eval_data: + return "Error: No evaluation data provided" + + # Use the prompt template and LMM for performance evaluation + return self._call_lmm(tool_input) + + +class ActionGeneratorTool(BaseTool): + """Tool for generating executable actions.""" + + def __init__(self, provider: str, model_name: str, tool_name: str, **kwargs): + """ + Initialize the action generator tool. + + Args: + provider: API provider name + model_name: Model name to use + tool_name: Name of the tool (used as key in prompts.json) + **kwargs: Additional parameters, including: + enable_search: Whether to enable web search functionality + search_provider: Provider for web search (defaults to "bocha") + search_model: Model for web search (defaults to "") + """ + super().__init__(provider, model_name, tool_name) + + # Extract search-related parameters + self.enable_search = kwargs.get("enable_search", False) + search_provider = kwargs.get("search_provider", "bocha") + search_model = kwargs.get("search_model", "") + + # Initialize search tool if enabled + self.search_tool = None + if self.enable_search: + self.search_tool = WebSearchTool(search_provider, search_model, "") + logger.info(f"Web search enabled for {tool_name} using provider: {search_provider}") + + def execute(self, tool_input: Dict[str, Any]): + """ + Generate executable actions. + + Args: + tool_input: Dictionary containing the action request + Expected to have 'str_input' key with the action request + May also have 'img_input' key with a screenshot + + Returns: + Generated action as a string + """ + action_request = tool_input.get('str_input', '') + if not action_request: + return "Error: No action request provided", [0, 0, 0], "" + + # Check if search is enabled + if self.enable_search and self.search_tool: + try: + # Use the input text directly as search query + search_query = action_request + logger.info(f"Performing web search for query: {search_query}") + search_results, tokens, cost = self.search_tool.execute({"str_input": search_query}) + + # Enhance the action request with search results + enhanced_request = f"[Action Request]\n{action_request}\n[End of Action Request]\n\n[Web Search Results for '{action_request}']\n{search_results}\n\n[End of Web Search Results]" + tool_input["str_input"] = enhanced_request + + logger.info(f"Search completed. Found information: {len(search_results)} characters") + except Exception as e: + logger.error(f"Error during web search: {e}") + # Continue with original request if search fails + + # Use the prompt template and LMM for action generation + return self._call_lmm(tool_input) + + +class FastActionGeneratorTool(BaseTool): + """Tool for directly generating executable actions without intermediate planning.""" + + def __init__(self, provider: str, model_name: str, tool_name: str, **kwargs): + """ + Initialize the fast action generator tool. + + Args: + provider: API provider name + model_name: Model name to use + tool_name: Name of the tool (used as key in prompts.json) + **kwargs: Additional parameters, including: + enable_search: Whether to enable web search functionality + search_provider: Provider for web search (defaults to "bocha") + search_model: Model for web search (defaults to "") + """ + super().__init__(provider, model_name, tool_name) + + # Extract search-related parameters + self.enable_search = kwargs.get("enable_search", False) + search_provider = kwargs.get("search_provider", "bocha") + search_model = kwargs.get("search_model", "") + + # Initialize search tool if enabled + self.search_tool = None + if self.enable_search: + self.search_tool = WebSearchTool(search_provider, search_model, "") + logger.info(f"Web search enabled for {tool_name} using provider: {search_provider}") + + def execute(self, tool_input: Dict[str, Any]): + """ + Generate executable actions directly from the instruction and screenshot. + + Args: + tool_input: Dictionary containing the action request + Expected to have 'str_input' key with the instruction + Expected to have 'img_input' key with a screenshot + + Returns: + Generated action as a string, token count, and cost + """ + action_request = tool_input.get('str_input', '') + screenshot = tool_input.get('img_input') + if not action_request: + return "Error: No action request provided", [0, 0, 0], "" + if not screenshot: + return "Error: No screenshot provided", [0, 0, 0], "" + # Check if search is enabled + if self.enable_search and self.search_tool: + try: + # Use the input text directly as search query + search_query = action_request + logger.info(f"Performing web search for query: {search_query}") + search_results, tokens, cost = self.search_tool.execute({"str_input": search_query}) + + # Enhance the action request with search results + enhanced_request = f"[Action Request]\n{action_request}\n[End of Action Request]\n\n[Web Search Results for '{action_request}']\n{search_results}\n\n[End of Web Search Results]" + tool_input["str_input"] = enhanced_request + + logger.info(f"Search completed. Found information: {len(search_results)} characters") + except Exception as e: + logger.error(f"Error during web search: {e}") + # Continue with original request if search fails + + # Use the prompt template and LMM for action generation + return self._call_lmm(tool_input) + + def get_grounding_wh(self): + """ + Get grounding width and height based on provider and model name. + + Returns: + If provider is doubao and model_name contains 'ui-tars', returns two values: + grounding_width (int): Width value (1024) + grounding_height (int): Height value (768) + Otherwise returns None, None + """ + if self.provider == "doubao" and "ui-tars" in self.model_name: + grounding_width = 1000 + grounding_height = 1000 + return grounding_width, grounding_height + return None, None + +class EmbeddingTool(BaseTool): + """Tool for generating text embeddings.""" + + def __init__(self, provider: str, model_name: str, tool_name: str): + """ + Initialize the embedding tool. + + Args: + provider: API provider name (e.g., "openai", "gemini") + model_name: Model name to use + tool_name: Name of the tool (used as key in prompts.json) + """ + self.provider = provider + self.model_name = model_name + self.tool_name = tool_name + + # Create EmbeddingAgent instance + self.engine_params = { + "engine_type": provider, + "embedding_model": model_name + } + + # Initialize EmbeddingAgent + self.embedding_agent = EmbeddingAgent(engine_params=self.engine_params) + + def execute(self, tool_input: Dict[str, Any]): + """ + Generate embeddings for the given text. + + Args: + tool_input: Dictionary containing the text to embed + Expected to have 'str_input' key with the text + + Returns: + Embeddings as a JSON string + """ + text = tool_input.get('str_input', '') + + if not text: + return "Error: No text provided for embedding", [0, 0, 0], "" + + try: + # Get embeddings for the text + embeddings, total_tokens, cost_string = self.embedding_agent.get_embeddings(text) + return embeddings, total_tokens, cost_string + + except Exception as e: + logger.error(f"Error during embedding operation: {str(e)}") + return f"Error: Embedding operation failed: {str(e)}", [0, 0, 0], "" + +class QueryFormulatorTool(BaseTool): + """Tool for formulating queries from tasks or contexts.""" + + def execute(self, tool_input: Dict[str, Any]): + """ + Formulate a query for a given task or context. + + Args: + tool_input: Dictionary containing the task or context description + Expected to have 'str_input' key with the description + May also have 'img_input' key with a screenshot + + Returns: + Formulated query as a string + """ + task = tool_input.get('str_input', '') + if not task: + return "Error: No task or context description provided" + + # Use the prompt template and LMM for query formulation + return self._call_lmm(tool_input) + +class NewTools: + """Main Tools class that provides access to all available tools.""" + + def __init__(self): + """Initialize the Tools class.""" + self.tools = {} + + def register_tool(self, tool_name: str, provider: str, model_name: str, **kwargs): + """ + Register a tool with the specified parameters. + + Args: + tool_name: Name of the tool to register + provider: API provider name + model_name: Model name to use + **kwargs: Additional parameters to pass to the tool + """ + tool: BaseTool = ToolFactory.create_tool(tool_name, provider, model_name, **kwargs) + self.tools[tool_name] = tool + + def execute_tool(self, tool_name: str, tool_input: Dict[str, Any]): + """ + Execute a tool with the given input. + + Args: + tool_name: Name of the tool to execute + tool_input: Input for the tool + + Returns: + The output of the tool as a string + + Raises: + ValueError: If the tool is not registered + """ + if tool_name not in self.tools: + raise ValueError(f"Tool {tool_name} is not registered") + + return self.tools[tool_name].execute(tool_input) + + def reset(self, tool_name: Optional[str] = None): + """ + Reset tools by resetting their llm_agent if available. + + Args: + tool_name: Optional name of the specific tool to reset. If None, resets all tools. + """ + if tool_name is not None: + # Reset a specific tool + if tool_name not in self.tools: + raise ValueError(f"Tool {tool_name} is not registered") + + tool = self.tools[tool_name] + if hasattr(tool, 'llm_agent') and tool.llm_agent is not None: + tool.llm_agent.reset() + else: + # Reset all tools + for tool in self.tools.values(): + # Only reset if the tool has an llm_agent attribute + if hasattr(tool, 'llm_agent') and tool.llm_agent is not None: + tool.llm_agent.reset() \ No newline at end of file diff --git a/mm_agents/maestro/tools/new_tools_config.json b/mm_agents/maestro/tools/new_tools_config.json new file mode 100644 index 0000000..0d484f9 --- /dev/null +++ b/mm_agents/maestro/tools/new_tools_config.json @@ -0,0 +1,109 @@ +{ + "tools": [ + { + "tool_name": "embedding", + "provider": "doubao", + "model_name": "doubao-embedding-text-240715" + }, + { + "tool_name": "query_formulator", + "provider": "doubao", + "model_name": "doubao-seed-1-6-flash-250615" + }, + { + "tool_name": "websearch", + "provider": "bocha", + "model_name": "" + }, + { + "tool_name": "narrative_summarization", + "provider": "doubao", + "model_name": "doubao-seed-1-6-flash-250615" + }, + { + "tool_name": "context_fusion", + "provider": "doubao", + "model_name": "doubao-seed-1-6-flash-250615" + }, + { + "tool_name": "planner_role", + "provider": "openrouter", + "model_name": "openai/o3" + }, + { + "tool_name": "supplement_role", + "provider": "openrouter", + "model_name": "openai/o3", + "enable_search": true + }, + { + "tool_name": "dag_translator", + "provider": "openrouter", + "model_name": "openai/o3" + }, + { + "tool_name": "operator_role", + "provider": "openrouter", + "model_name": "openai/o3", + "enable_search": false, + "search_provider": "bocha", + "search_model": "" + }, + { + "tool_name": "technician_role", + "provider": "openrouter", + "model_name": "openai/o3", + "enable_search": false, + "search_provider": "bocha", + "search_model": "" + }, + { + "tool_name": "analyst_role", + "provider": "openrouter", + "model_name": "openai/o3", + "enable_search": false, + "search_provider": "bocha", + "search_model": "" + }, + { + "tool_name": "grounding", + "provider": "doubao", + "model_name": "doubao-1-5-ui-tars-250428" + }, + { + "tool_name": "text_span", + "provider": "doubao", + "model_name": "doubao-seed-1-6-flash-250615" + }, + { + "tool_name": "episode_summarization", + "provider": "doubao", + "model_name": "doubao-seed-1-6-flash-250615" + }, + { + "tool_name": "worker_success_role", + "provider": "openrouter", + "model_name": "openai/o3" + }, + { + "tool_name": "worker_stale_role", + "provider": "openrouter", + "model_name": "openai/o3" + }, + { + "tool_name": "periodic_role", + "provider": "openrouter", + "model_name": "openai/o3" + }, + { + "tool_name": "final_check_role", + "provider": "openrouter", + "model_name": "openai/o3" + }, + { + "tool_name": "objective_alignment", + "provider": "openrouter", + "model_name": "openai/o3" + } + ] +} \ No newline at end of file diff --git a/mm_agents/maestro/utils/README.md b/mm_agents/maestro/utils/README.md new file mode 100644 index 0000000..2512f45 --- /dev/null +++ b/mm_agents/maestro/utils/README.md @@ -0,0 +1,121 @@ +# Maestro Utilities + +This directory contains various utility functions for the Maestro project to improve code reusability and maintainability. + +## File Structure + +``` +gui_agents/utils/ +├── README.md # This document +├── file_utils.py # File operation utilities +├── id_utils.py # ID generation utilities +└── common_utils.py # Other common utilities +``` + +## file_utils.py - File Operation Utilities + +### File Locking Mechanism + +```python +from gui_agents.utils.file_utils import locked + +# Cross-platform file lock, supports Windows and Unix systems +with locked(file_path, "w") as f: + f.write("content") +``` + +### Safe JSON Operations + +```python +from gui_agents.utils.file_utils import safe_write_json, safe_read_json + +# Safely write JSON file (atomic operation) +safe_write_json(file_path, data) + +# Safely read JSON file +data = safe_read_json(file_path, default={}) +``` + +### Safe Text Operations + +```python +from gui_agents.utils.file_utils import safe_write_text, safe_read_text + +# Safely write text file (UTF-8 encoding) +safe_write_text(file_path, content) + +# Safely read text file (automatic encoding detection) +content = safe_read_text(file_path) +``` + +### File Management Tools + +```python +from gui_agents.utils.file_utils import ensure_directory, backup_file + +# Ensure directory exists +ensure_directory(path) + +# Create file backup +backup_path = backup_file(file_path, ".backup") +``` + +## id_utils.py - ID Generation Utilities + +### UUID Generation + +```python +from gui_agents.utils.id_utils import generate_uuid, generate_short_id + +# Generate complete UUID +uuid_str = generate_uuid() # "550e8400-e29b-41d4-a716-446655440000" + +# Generate short ID +short_id = generate_short_id("task", 8) # "task550e8400" +``` + +### Timestamp ID + +```python +from gui_agents.utils.id_utils import generate_timestamp_id + +# Timestamp-based ID +ts_id = generate_timestamp_id("event") # "event1755576661494" +``` + +### Hash ID + +```python +from gui_agents.utils.id_utils import generate_hash_id + +# Content hash-based ID +hash_id = generate_hash_id("some content", "hash", 8) # "hasha1b2c3d4" +``` + +### Composite ID + +```python +from gui_agents.utils.id_utils import generate_composite_id + +# Composite ID (prefix + timestamp + UUID) +composite_id = generate_composite_id("task", True, True, "_") # "task_1755576661494_550e8400" +``` + +## Usage in NewGlobalState + +The new `NewGlobalState` class has been refactored to use these utility functions: + +```python +from gui_agents.utils.file_utils import safe_write_json, safe_read_json +from gui_agents.utils.id_utils import generate_uuid + +class NewGlobalState: + def __init__(self, ...): + self.task_id = task_id or f"task-{generate_uuid()[:8]}" + + def set_task(self, task_data): + safe_write_json(self.task_path, task_data) + + def get_task(self): + return safe_read_json(self.task_path, {}) +``` diff --git a/mm_agents/maestro/utils/__init__.py b/mm_agents/maestro/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mm_agents/maestro/utils/analyze_display.py b/mm_agents/maestro/utils/analyze_display.py new file mode 100644 index 0000000..22e2a14 --- /dev/null +++ b/mm_agents/maestro/utils/analyze_display.py @@ -0,0 +1,339 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Display.json analyzer - Extract and analyze execution statistics from display.json files +""" + +import json +import os +import glob +import re +from typing import Dict, List, Tuple + + +def extract_cost_value(cost_str: str) -> tuple: + """ + Extract numeric value and currency symbol from cost string (e.g., "0.000343￥" -> (0.000343, "￥")) + + Args: + cost_str: Cost string with currency symbol + + Returns: + Tuple of (float value, currency symbol) + """ + # Extract numeric value and currency symbol + match = re.search(r'([\d.]+)([￥$€£¥]*)', cost_str) + if match: + value = float(match.group(1)) + currency = match.group(2) if match.group(2) else "￥" # Default to ￥ if no symbol found + return value, currency + return 0.0, "￥" + + +def convert_currency_to_yuan(value: float, currency: str) -> float: + """ + Convert different currencies to yuan (￥) for consistent cost calculation + + Args: + value: Cost value + currency: Currency symbol + + Returns: + Value converted to yuan + """ + # Simple conversion rates (you might want to use real-time rates in production) + conversion_rates = { + "￥": 1.0, + "¥": 1.0, + "$": 7.2, # USD to CNY (approximate) + "€": 7.8, # EUR to CNY (approximate) + "£": 9.1, # GBP to CNY (approximate) + } + + rate = conversion_rates.get(currency, 1.0) + return value * rate + + +def analyze_display_json(file_path: str) -> Dict: + """ + Analyze a single display.json file and extract statistics + + Args: + file_path: Path to the display.json file + + Returns: + Dictionary containing analysis results + """ + try: + with open(file_path, 'r', encoding='utf-8') as f: + data = json.load(f) + except Exception as e: + print(f"Error reading {file_path}: {e}") + return {} + + # Initialize counters + action_count = 0 + total_duration = 0 + total_input_tokens = 0 + total_output_tokens = 0 + total_tokens = 0 + total_cost = 0.0 + currency_symbol = "￥" # Default currency symbol + + # Check if this is agents3 format (has controller.main_loop_completed) + is_agents3 = False + if 'operations' in data and 'controller' in data['operations']: + for operation in data['operations']['controller']: + if operation.get('operation') == 'main_loop_completed': + is_agents3 = True + # Extract agents3 statistics + action_count = operation.get('step_count', 0) + total_duration = int(operation.get('duration', 0)) + break + + if is_agents3: + # Agents3 mode analysis - extract from controller.main_loop_completed + if 'operations' in data and 'controller' in data['operations']: + for operation in data['operations']['controller']: + if operation.get('operation') == 'main_loop_completed': + action_count = operation.get('step_count', 0) + total_duration = int(operation.get('duration', 0)) + break + + # Extract tokens and cost from all operations + if 'operations' in data: + for module_name, module_operations in data['operations'].items(): + if isinstance(module_operations, list): + for operation in module_operations: + # Extract tokens if available + tokens = operation.get('tokens', [0, 0, 0]) + if isinstance(tokens, list) and len(tokens) >= 3: + total_input_tokens += tokens[0] + total_output_tokens += tokens[1] + total_tokens += tokens[2] + + # Extract cost if available + cost_str = operation.get('cost', '0￥') + cost_value, currency = extract_cost_value(cost_str) + # Convert to yuan for consistent calculation + cost_in_yuan = convert_currency_to_yuan( + cost_value, currency) + total_cost += cost_in_yuan + # Always use ￥ for consistency + currency_symbol = "￥" + + # Check if this is a fast mode or normal mode display.json + elif 'operations' in data and 'agent' in data['operations']: + # Fast mode analysis - similar to original logic + if 'operations' in data and 'agent' in data['operations']: + ops_list = [operation for operation in data['operations']['agent']] + ops_list.extend([operation for operation in data['operations']['grounding']]) + for operation in ops_list: + if operation.get('operation') == 'fast_planning_execution': + action_count += 1 + + # Extract tokens + tokens = operation.get('tokens', [0, 0, 0]) + if len(tokens) >= 3: + total_input_tokens += tokens[0] + total_output_tokens += tokens[1] + total_tokens += tokens[2] + + # Extract cost + cost_str = operation.get('cost', '0￥') + cost_value, currency = extract_cost_value(cost_str) + # Convert to yuan for consistent calculation + cost_in_yuan = convert_currency_to_yuan(cost_value, currency) + total_cost += cost_in_yuan + currency_symbol = "￥" # Always use ￥ for consistency + + # Extract total execution time for fast mode + if 'operations' in data and 'other' in data['operations']: + for operation in data['operations']['other']: + if operation.get('operation') == 'total_execution_time_fast': + total_duration = int(operation.get('duration', 0)) + break + else: + # Normal mode analysis - analyze specific operations + if 'operations' in data: + # Define the operations to count for tokens and cost + token_cost_operations = { + 'formulate_query', 'retrieve_narrative_experience', 'retrieve_knowledge', + 'knowledge_fusion', 'subtask_planner', 'generated_dag', 'reflection', + 'episode_summarization', 'narrative_summarization', 'Worker.retrieve_episodic_experience', + 'action_plan', 'grounding_model_response' + } + + # Count hardware operations as steps + if 'hardware' in data['operations']: + action_count = len(data['operations']['hardware']) + + # Extract tokens and cost from specific operations across all modules + for module_name, module_operations in data['operations'].items(): + if isinstance(module_operations, list): + for operation in module_operations: + operation_type = operation.get('operation', '') + + # Only count tokens and cost for specified operations + if operation_type in token_cost_operations: + # Extract tokens if available + tokens = operation.get('tokens', [0, 0, 0]) + if isinstance(tokens, list) and len(tokens) >= 3: + total_input_tokens += tokens[0] + total_output_tokens += tokens[1] + total_tokens += tokens[2] + + # Extract cost if available + cost_str = operation.get('cost', '0￥') + cost_value, currency = extract_cost_value(cost_str) + # Convert to yuan for consistent calculation + cost_in_yuan = convert_currency_to_yuan(cost_value, currency) + total_cost += cost_in_yuan + # Always use ￥ for consistency + currency_symbol = "￥" + + # Extract total execution time for normal mode + if 'other' in data['operations']: + for operation in data['operations']['other']: + if operation.get('operation') == 'total_execution_time': + total_duration = int(operation.get('duration', 0)) + break + + return { + 'action_count': action_count, + 'total_duration': total_duration, + 'total_input_tokens': total_input_tokens, + 'total_output_tokens': total_output_tokens, + 'total_tokens': total_tokens, + 'total_cost': total_cost, + 'currency_symbol': currency_symbol + } + + +def analyze_folder(folder_path: str) -> List[Dict]: + """ + Analyze all display.json files in a folder + + Args: + folder_path: Path to the folder containing display.json files + + Returns: + List of analysis results for each file + """ + results = [] + + # Find all display.json files recursively + pattern = os.path.join(folder_path, "**", "display.json") + display_files = glob.glob(pattern, recursive=True) + + if not display_files: + print(f"No display.json files found in {folder_path}") + return results + + print(f"Found {len(display_files)} display.json files") + + for file_path in display_files: + print(f"Analyzing: {file_path}") + result = analyze_display_json(file_path) + if result: + result['file_path'] = file_path + results.append(result) + + return results + + +def aggregate_results(results: List[Dict]) -> Dict: + """ + Aggregate results from multiple files + + Args: + results: List of analysis results + + Returns: + Aggregated statistics + """ + if not results: + return {} + + total_fast_actions = sum(r['action_count'] for r in results) + total_duration = max(r['total_duration'] for r in results) if results else 0 + total_input_tokens = sum(r['total_input_tokens'] for r in results) + total_output_tokens = sum(r['total_output_tokens'] for r in results) + total_tokens = sum(r['total_tokens'] for r in results) + total_cost = sum(r['total_cost'] for r in results) + + # Use the currency symbol from the first result, or default to ￥ + currency_symbol = results[0].get('currency_symbol', '￥') if results else '￥' + + return { + 'total_fast_actions': total_fast_actions, + 'total_duration': total_duration, + 'total_input_tokens': total_input_tokens, + 'total_output_tokens': total_output_tokens, + 'total_tokens': total_tokens, + 'total_cost': total_cost, + 'currency_symbol': currency_symbol + } + + +def format_output_line(stats: Dict) -> str: + """ + Format statistics into a single output line + + Args: + stats: Statistics dictionary + + Returns: + Formatted output line + """ + if not stats: + return "No data available" + + # Format: steps, duration (seconds), tokens, cost + steps = stats.get('action_count', 0) + duration = stats.get('total_duration', 0) + tokens = (stats.get('total_input_tokens', 0),stats.get('total_output_tokens', 0),stats.get('total_tokens', 0)) + cost = stats.get('total_cost', 0.0) + + return f"{steps}, {duration}, {tokens}, {cost:.4f}{stats.get('currency_symbol', '￥')}" + + +def main(): + """ + Main function to analyze display.json files + """ + import sys + + if len(sys.argv) < 2: + print("Usage: python analyze_display.py ") + print("Example: python analyze_display.py lybicguiagents/runtime") + return + + folder_path = sys.argv[1] + + if not os.path.exists(folder_path): + print(f"Folder not found: {folder_path}") + return + + # Analyze all display.json files in the folder + results = analyze_folder(folder_path) + + if not results: + print("No valid display.json files found") + return + + # Aggregate results + aggregated_stats = aggregate_results(results) + + # Print the required single line output + print("\nStatistics:") + print("-" * 80) + print("Steps, Duration (seconds), (Input Tokens, Output Tokens, Total Tokens), Cost") + print("-" * 80) + output_line = format_output_line(aggregated_stats) + print(output_line) + print("-" * 80) + + +if __name__ == "__main__": + main() diff --git a/mm_agents/maestro/utils/common_utils.py b/mm_agents/maestro/utils/common_utils.py new file mode 100644 index 0000000..a3b5dc2 --- /dev/null +++ b/mm_agents/maestro/utils/common_utils.py @@ -0,0 +1,577 @@ +import json +import re +from typing import List +import time +import tiktoken +import numpy as np +import os +import platform +import io +from PIL import Image +import logging + +from typing import Tuple, List, Union, Dict, Optional + +from pydantic import BaseModel, ValidationError + +import pickle + + +class Node(BaseModel): + name: str + info: str + # New fields for failed task analysis + assignee_role: Optional[str] = None + error_type: Optional[str] = None # Error type: UI_ERROR, EXECUTION_ERROR, PLANNING_ERROR, etc. + error_message: Optional[str] = None # Specific error message + failure_count: Optional[int] = 0 # Failure count + last_failure_time: Optional[str] = None # Last failure time + suggested_action: Optional[str] = None # Suggested repair action + + +class Dag(BaseModel): + nodes: List[Node] + edges: List[List[Node]] + +class SafeLoggingFilter(logging.Filter): + """ + Safe logging filter that prevents logging format errors + Handles cases where log message format strings don't match arguments + """ + + def filter(self, record): + """ + Filter log records to prevent format errors + """ + try: + # Try to format the message to catch format errors early + if hasattr(record, 'msg') and hasattr(record, 'args') and record.args: + try: + # Test if the message can be formatted with the provided args + if isinstance(record.msg, str) and '%s' in record.msg: + # Count %s placeholders in the message + placeholder_count = record.msg.count('%s') + args_count = len(record.args) + + if placeholder_count != args_count: + # Mismatch detected, create safe message + record.msg = f"[Format mismatch prevented] Msg: {record.msg[:100]}{'...' if len(str(record.msg)) > 100 else ''}, Args count: {args_count}" + record.args = () + return True + + # Test if the message can be formatted with the provided args + _ = record.msg % record.args + except (TypeError, ValueError) as e: + # If formatting fails, create a safe message + record.msg = f"[Logging format error prevented] Original message: {str(record.msg)[:100]}{'...' if len(str(record.msg)) > 100 else ''}, Args: {record.args}" + record.args = () + return True + except Exception as e: + # If anything goes wrong, allow the record through but with a safe message + record.msg = f"[Logging filter error: {e}] Original message could not be processed safely" + record.args = () + return True + +class ImageDataFilter(logging.Filter): + """ + Custom log filter for filtering log records containing image binary data + Specifically designed to filter image data in multimodal model API calls + """ + + # Image data characteristic identifiers + IMAGE_INDICATORS = [ + 'data:image', # data URL format + 'iVBORw0KGgo', # PNG base64 beginning + '/9j/', # JPEG base64 beginning + 'R0lGOD', # GIF base64 beginning + 'UklGR', # WEBP base64 beginning + 'Qk0', # BMP base64 beginning + ] + + # Binary file headers + BINARY_HEADERS = [ + b'\xff\xd8\xff', # JPEG file header + b'\x89PNG\r\n\x1a\n', # PNG file header + b'GIF87a', # GIF87a file header + b'GIF89a', # GIF89a file header + b'RIFF', # WEBP/WAV file header + b'BM', # BMP file header + ] + + def filter(self, record): + """ + Filter image data from log records + """ + try: + # Process log message + if hasattr(record, 'msg') and record.msg: + record.msg = self._filter_message(record.msg) + + # Process log arguments + if hasattr(record, 'args') and record.args: + record.args = self._filter_args(record.args) + + except Exception as e: + # If filtering process fails, log error but don't block log output + record.msg = f"[Log filter error: {e}] Original message may contain image data" + record.args = () + + return True + + def _filter_message(self, msg): + """ + Filter image data from messages + """ + msg_str = str(msg) + + # If message is very long, it may contain image data + if len(msg_str) > 5000: # Lower threshold to 5KB + # Check if contains image data characteristics + if self._contains_image_data(msg_str): + return f"[LLM Call Log] Contains image data (size: {len(msg_str)} characters) - filtered" + + # Check if contains binary data characteristics + if self._contains_binary_data(msg_str): + return f"[LLM Call Log] Contains binary data (size: {len(msg_str)} characters) - filtered" + + return msg + + def _filter_args(self, args): + """ + Filter image data from arguments + """ + filtered_args = [] + + for arg in args: + if isinstance(arg, (bytes, bytearray)): + # Process binary data + if len(arg) > 1000: # Binary data larger than 1KB + if self._is_image_binary(arg): + filtered_args.append(f"[Image binary data filtered, size: {len(arg)} bytes]") + else: + filtered_args.append(f"[Binary data filtered, size: {len(arg)} bytes]") + else: + filtered_args.append(arg) + + elif isinstance(arg, str): + # Process string data + if len(arg) > 5000: # Strings larger than 5KB + if self._contains_image_data(arg): + filtered_args.append(f"[Image string data filtered, size: {len(arg)} characters]") + else: + filtered_args.append(arg) + else: + filtered_args.append(arg) + + else: + # Keep other data types directly + filtered_args.append(arg) + + return tuple(filtered_args) + + def _contains_image_data(self, text): + """ + Check if text contains image data + """ + text_lower = text.lower() + return any(indicator in text_lower for indicator in self.IMAGE_INDICATORS) + + def _contains_binary_data(self, text): + """ + Check if text contains large amounts of binary data + """ + # Check if contains large amounts of non-ASCII characters (possibly base64-encoded binary data) + non_ascii_count = sum(1 for char in text if ord(char) > 127) + non_ascii_ratio = non_ascii_count / len(text) if len(text) > 0 else 0 + + # If non-ASCII character ratio exceeds 10%, it might be binary data + return non_ascii_ratio > 0.1 + + def _is_image_binary(self, data): + """ + Check if binary data is an image + """ + if len(data) < 10: + return False + + # Check file headers + for header in self.BINARY_HEADERS: + if data.startswith(header): + return True + + return False + +NUM_IMAGE_TOKEN = 1105 # Value set of screen of size 1920x1080 for openai vision + +def calculate_tokens(messages, num_image_token=NUM_IMAGE_TOKEN) -> Tuple[int, int]: + + num_input_images = 0 + output_message = messages[-1] + + input_message = messages[:-1] + + input_string = """""" + for message in input_message: + input_string += message["content"][0]["text"] + "\n" + if len(message["content"]) > 1: + num_input_images += 1 + + input_text_tokens = get_input_token_length(input_string) + + input_image_tokens = num_image_token * num_input_images + + output_tokens = get_input_token_length(output_message["content"][0]["text"]) + + return (input_text_tokens + input_image_tokens), output_tokens + +def parse_dag(text): + """ + Try extracting JSON from … tags first; + if not found, try ```json … ``` Markdown fences. + If both fail, try to parse the entire text as JSON. + """ + logger = logging.getLogger("desktopenv.agent") + + def _extract(pattern): + m = re.search(pattern, text, re.DOTALL) + return m.group(1).strip() if m else None + + # 1) look for … + json_str = _extract(r"(.*?)") + # 2) fallback to ```json … ``` + if json_str is None: + json_str = _extract(r"```json\s*(.*?)\s*```") + if json_str is None: + # 3) try other possible code block formats + json_str = _extract(r"```\s*(.*?)\s*```") + + # 4) if still not found, try to parse the entire text + if json_str is None: + logger.warning("JSON markers not found, attempting to parse entire text") + json_str = text.strip() + + # Log the extracted JSON string + logger.debug(f"Extracted JSON string: {json_str[:100]}...") + + try: + # Try to parse as JSON directly + payload = json.loads(json_str) + except json.JSONDecodeError as e: + logger.error(f"JSON parsing error: {e}") + + # Try to fix common JSON format issues + try: + # Replace single quotes with double quotes + fixed_json = json_str.replace("'", "\"") + payload = json.loads(fixed_json) + logger.info("Successfully fixed JSON by replacing single quotes with double quotes") + except json.JSONDecodeError: + # Try to find and extract possible JSON objects + try: + # Look for content between { and } + match = re.search(r"\{(.*)\}", json_str, re.DOTALL) + if match: + fixed_json = "{" + match.group(1) + "}" + payload = json.loads(fixed_json) + logger.info("Successfully fixed JSON by extracting JSON object") + else: + logger.error("Unable to fix JSON format") + return None + except Exception: + logger.error("All JSON fixing attempts failed") + return None + + # Check if payload contains dag key + if "dag" not in payload: + logger.warning("'dag' key not found in JSON, attempting to use entire JSON object") + # If no dag key, try to use the entire payload + try: + # Check if payload directly conforms to Dag structure + if "nodes" in payload and "edges" in payload: + return Dag(**payload) + else: + # Iterate through top-level keys to find possible dag structure + for key, value in payload.items(): + if isinstance(value, dict) and "nodes" in value and "edges" in value: + logger.info(f"Found DAG structure in key '{key}'") + return Dag(**value) + + logger.error("Could not find valid DAG structure in JSON") + return None + except ValidationError as e: + logger.error(f"Data structure validation error: {e}") + return None + + # Normal case, use value of dag key + try: + return Dag(**payload["dag"]) + except ValidationError as e: + logger.error(f"DAG data structure validation error: {e}") + return None + except Exception as e: + logger.error(f"Unknown error parsing DAG: {e}") + return None + + +def parse_single_code_from_string(input_string): + input_string = input_string.strip() + if input_string.strip() in ["WAIT", "DONE", "FAIL"]: + return input_string.strip() + + pattern = r"```(?:\w+\s+)?(.*?)```" + matches = re.findall(pattern, input_string, re.DOTALL) + codes = [] + for match in matches: + match = match.strip() + commands = ["WAIT", "DONE", "FAIL"] + if match in commands: + codes.append(match.strip()) + elif match.split("\n")[-1] in commands: + if len(match.split("\n")) > 1: + codes.append("\n".join(match.split("\n")[:-1])) + codes.append(match.split("\n")[-1]) + else: + codes.append(match) + if len(codes) > 0: + return codes[0] + # The pattern matches function calls with balanced parentheses and quotes + code_match = re.search(r"(\w+\.\w+\((?:[^()]*|\([^()]*\))*\))", input_string) + if code_match: + return code_match.group(1) + lines = [line.strip() for line in input_string.splitlines() if line.strip()] + if lines: + return lines[0] + return "fail" + + +def get_input_token_length(input_string): + enc = tiktoken.encoding_for_model("gpt-4") + tokens = enc.encode(input_string) + return len(tokens) + +def parse_screenshot_analysis(action_plan: str) -> str: + """Parse the Screenshot Analysis section from the LLM response. + + Args: + action_plan: The raw LLM response text + + Returns: + The screenshot analysis text, or empty string if not found + """ + try: + # Look for Screenshot Analysis section + if "(Screenshot Analysis)" in action_plan: + # Find the start of Screenshot Analysis section + start_idx = action_plan.find("(Screenshot Analysis)") + # Find the next section marker + next_sections = ["(Next Action)", "(Grounded Action)", "(Previous action verification)"] + end_idx = len(action_plan) + for section in next_sections: + section_idx = action_plan.find(section, start_idx + 1) + if section_idx != -1 and section_idx < end_idx: + end_idx = section_idx + + # Extract the content between markers + analysis_start = start_idx + len("(Screenshot Analysis)") + analysis_text = action_plan[analysis_start:end_idx].strip() + return analysis_text + return "" + except Exception as e: + return "" + +def parse_technician_screenshot_analysis(command_plan: str) -> str: + """Parse the Screenshot Analysis section from the technician LLM response. + + Args: + command_plan: The raw LLM response text + + Returns: + The screenshot analysis text, or empty string if not found + """ + try: + # Look for Screenshot Analysis section + if "(Screenshot Analysis)" in command_plan: + # Find the start of Screenshot Analysis section + start_idx = command_plan.find("(Screenshot Analysis)") + # Find the next section marker + next_sections = ["(Next Action)"] + end_idx = len(command_plan) + for section in next_sections: + section_idx = command_plan.find(section, start_idx + 1) + if section_idx != -1 and section_idx < end_idx: + end_idx = section_idx + + # Extract the content between markers + analysis_start = start_idx + len("(Screenshot Analysis)") + analysis_text = command_plan[analysis_start:end_idx].strip() + return analysis_text + return "" + except Exception as e: + return "" + +def sanitize_code(code): + # This pattern captures the outermost double-quoted text + if "\n" in code: + pattern = r'(".*?")' + # Find all matches in the text + matches = re.findall(pattern, code, flags=re.DOTALL) + if matches: + # Replace the first occurrence only + first_match = matches[0] + code = code.replace(first_match, f'"""{first_match[1:-1]}"""', 1) + return code + + +def extract_first_agent_function(code_string): + # Regular expression pattern to match 'agent' functions with any arguments, including nested parentheses + pattern = r'agent\.[a-zA-Z_]+\((?:[^()\'"]|\'[^\']*\'|"[^"]*")*\)' + + # Find all matches in the string + matches = re.findall(pattern, code_string) + + # Return the first match if found, otherwise return None + return matches[0] if matches else None + + +def load_knowledge_base(kb_path: str) -> Dict: + try: + with open(kb_path, "r") as f: + return json.load(f) + except Exception as e: + print(f"Error loading knowledge base: {e}") + return {} + + +def clean_empty_embeddings(embeddings: Dict) -> Dict: + to_delete = [] + for k, v in embeddings.items(): + arr = np.array(v) + if arr.size == 0 or arr.shape == () or ( + isinstance(v, list) and v and isinstance(v[0], str) and v[0].startswith('Error:') + ) or (isinstance(v, str) and v.startswith('Error:')): + to_delete.append(k) + for k in to_delete: + del embeddings[k] + return embeddings + + +def load_embeddings(embeddings_path: str) -> Dict: + try: + with open(embeddings_path, "rb") as f: + embeddings = pickle.load(f) + embeddings = clean_empty_embeddings(embeddings) + return embeddings + except Exception as e: + # print(f"Error loading embeddings: {e}") + print(f"Empty embeddings file: {embeddings_path}") + return {} + + +def save_embeddings(embeddings_path: str, embeddings: Dict): + try: + import os + os.makedirs(os.path.dirname(embeddings_path), exist_ok=True) + with open(embeddings_path, "wb") as f: + pickle.dump(embeddings, f) + except Exception as e: + print(f"Error saving embeddings: {e}") + +def agent_log_to_string(agent_log: List[Dict]) -> str: + """ + Converts a list of agent log entries into a single string for LLM consumption. + + Args: + agent_log: A list of dictionaries, where each dictionary is an agent log entry. + + Returns: + A formatted string representing the agent log. + """ + if not agent_log: + return "No agent log entries yet." + + log_strings = ["[AGENT LOG]"] + for entry in agent_log: + entry_id = entry.get("id", "N/A") + entry_type = entry.get("type", "N/A").capitalize() + content = entry.get("content", "") + log_strings.append(f"[Entry {entry_id} - {entry_type}] {content}") + + return "\n".join(log_strings) + + +def show_task_completion_notification(task_status: str, error_message: str = ""): + """ + Show a popup notification for task completion status. + + Args: + task_status: Task status, supports 'success', 'failed', 'completed', 'error' + error_message: Error message (used only when status is 'error') + """ + try: + current_platform = platform.system() + + if task_status == "success": + title = "Maestro" + message = "Task Completed Successfully" + dialog_type = "info" + elif task_status == "failed": + title = "Maestro" + message = "Task Failed/Rejected" + dialog_type = "error" + elif task_status == "completed": + title = "Maestro" + message = "Task Execution Completed" + dialog_type = "info" + elif task_status == "error": + title = "Maestro Error" + message = f"Task Execution Error: {error_message[:100] if error_message else 'Unknown error'}" + dialog_type = "error" + else: + title = "Maestro" + message = "Task Execution Completed" + dialog_type = "info" + + if current_platform == "Darwin": + # macOS + os.system( + f'osascript -e \'display dialog "{message}" with title "{title}" buttons "OK" default button "OK"\'' + ) + elif current_platform == "Linux": + # Linux + if dialog_type == "error": + os.system( + f'zenity --error --title="{title}" --text="{message}" --width=300 --height=150' + ) + else: + os.system( + f'zenity --info --title="{title}" --text="{message}" --width=200 --height=100' + ) + elif current_platform == "Windows": + # Windows + os.system( + f'msg %username% "{message}"' + ) + else: + print(f"\n[{title}] {message}") + + except Exception as e: + print(f"\n[Agents3] Failed to show notification: {e}") + print(f"[Agents3] {message}") + +def screenshot_bytes_to_pil_image(screenshot_bytes: bytes) -> Optional[Image.Image]: + """ + Convert the bytes data of obs["screenshot"] to a PIL Image object, preserving the original size + + Args: + screenshot_bytes: The bytes data of the screenshot + + Returns: + PIL Image object, or None if conversion fails + """ + try: + # Create PIL Image object directly from bytes + image = Image.open(io.BytesIO(screenshot_bytes)) + return image + except Exception as e: + raise RuntimeError(f"Failed to convert screenshot bytes to PIL Image: {e}") + diff --git a/mm_agents/maestro/utils/display_viewer.py b/mm_agents/maestro/utils/display_viewer.py new file mode 100644 index 0000000..d4e4d7b --- /dev/null +++ b/mm_agents/maestro/utils/display_viewer.py @@ -0,0 +1,281 @@ +#!/usr/bin/env python +""" +Display Viewer - Used to display operation records in display.json file in chronological order + +Usage: + python -m lybicguiagents.gui_agents.utils.display_viewer --file /path/to/display.json [--output text|json] [--filter module1,module2] +""" + +import os +import sys +import json +import argparse +import datetime +from pathlib import Path +from typing import Dict, List, Any, Optional, Tuple + + +def load_display_json(file_path: str) -> Dict: + """ + Load display.json file + + Args: + file_path: Path to display.json file + + Returns: + Parsed JSON data + """ + try: + try: + with open(file_path, 'r', encoding='utf-8') as f: + return json.load(f) + except UnicodeDecodeError: + print( + f"Warning: Failed to decode '{file_path}' with utf-8, retrying with GB2312..." + ) + with open(file_path, 'r', encoding='gb2312') as f: + return json.load(f) + except FileNotFoundError: + print(f"Error: File '{file_path}' does not exist") + sys.exit(1) + except json.JSONDecodeError: + print(f"Error: File '{file_path}' is not a valid JSON format") + sys.exit(1) + except Exception as e: + print(f"Error: An error occurred while reading file '{file_path}': {e}") + sys.exit(1) + + +def flatten_operations(data: Dict) -> List[Dict]: + """ + Flatten all module operation records into a time-sorted list + + Args: + data: display.json data + + Returns: + List of operation records sorted by time + """ + all_operations = [] + + if "operations" not in data: + return all_operations + + for module, operations in data["operations"].items(): + for op in operations: + # Add module information + op["module"] = module + all_operations.append(op) + + # Sort by timestamp + all_operations.sort(key=lambda x: x.get("timestamp", 0)) + + return all_operations + + +def format_timestamp(timestamp: float) -> str: + """ + Format timestamp into readable datetime + + Args: + timestamp: UNIX timestamp + + Returns: + Formatted datetime string + """ + dt = datetime.datetime.fromtimestamp(timestamp) + return dt.strftime("%Y-%m-%d %H:%M:%S.%f")[:-3] + + +def format_duration(duration: float) -> str: + """ + Format duration + + Args: + duration: Duration (seconds) + + Returns: + Formatted duration string + """ + if duration < 0.001: + return f"{duration * 1000000:.2f}μs" + elif duration < 1: + return f"{duration * 1000:.2f}ms" + else: + return f"{duration:.2f}s" + + +def format_tokens(tokens: List[int]) -> str: + """ + Format tokens information + + Args: + tokens: [input tokens, output tokens, total tokens] + + Returns: + Formatted tokens string + """ + if not tokens or len(tokens) < 3: + return "N/A" + + return f"in:{tokens[0]} out:{tokens[1]} total:{tokens[2]}" + + +def truncate_text(text: str, max_length: int = 100) -> str: + """ + Truncate text, add ellipsis when exceeding maximum length + + Args: + text: Original text + max_length: Maximum length + + Returns: + Truncated text + """ + if not text: + return "" + + if isinstance(text, (dict, list)): + text = str(text) + + if len(text) <= max_length: + return text + + return text[:max_length - 3] + "..." + + +def find_latest_display_json() -> Optional[str]: + """ + Find the latest display.json file + + Returns: + Path to the latest display.json file, or None if not found + """ + # Look for the runtime folder in the current directory + runtime_dir = Path("runtime") + if not runtime_dir.exists() or not runtime_dir.is_dir(): + # Try looking in the parent directory + parent_runtime = Path("..") / "runtime" + if parent_runtime.exists() and parent_runtime.is_dir(): + runtime_dir = parent_runtime + else: + return None + + # Find all timestamp folders + timestamp_dirs = [d for d in runtime_dir.iterdir() if d.is_dir()] + if not timestamp_dirs: + return None + + # Sort by folder name (timestamp) and take the latest + latest_dir = sorted(timestamp_dirs)[-1] + display_file = latest_dir / "display.json" + + if display_file.exists(): + return str(display_file) + + return None + + +def main(): + parser = argparse.ArgumentParser( + description= + "Display operation records in display.json file in chronological order") + parser.add_argument("--file", help="Path to display.json file") + parser.add_argument("--dir", help="Path to directory containing display.json files (recursive)") + parser.add_argument("--output", + choices=["text", "json"], + default="text", + help="Output format (default: text)") + parser.add_argument( + "--filter", + help="Modules to filter, separated by commas (e.g., manager,worker)") + + args = parser.parse_args() + + if args.file and args.dir: + print("Error: --file and --dir cannot be used together") + sys.exit(1) + + def process_one_file(file_path: str): + # Load data + data = load_display_json(file_path) + # Flatten and sort operations + operations = flatten_operations(data) + # Handle module filtering + filter_modules = None + if args.filter: + filter_modules = [module.strip() for module in args.filter.split(",")] + # Generate output content + output_content = "" + if args.output == "json": + # Filter operations if modules are specified + if filter_modules: + filtered_ops = [op for op in operations if op["module"] in filter_modules] + else: + filtered_ops = operations + output_content = json.dumps(filtered_ops, indent=2, ensure_ascii=False) + else: + # Generate text format output + output_lines = [] + for i, op in enumerate(operations): + # Skip modules that don't match the filter if a filter is specified + if filter_modules and op["module"] not in filter_modules: + continue + module = op["module"] + operation = op.get("operation", "unknown") + timestamp = format_timestamp(op.get("timestamp", 0)) + # Output basic information + output_lines.append(f"{i+1:3d} | {timestamp} | {module:10} | {operation}") + # Output detailed information + if "duration" in op: + output_lines.append(f" └─ Duration: {format_duration(op['duration'])}") + if "tokens" in op: + output_lines.append(f" └─ Tokens: {format_tokens(op['tokens'])}") + if "cost" in op: + output_lines.append(f" └─ Cost: {op['cost']}") + if "content" in op: + content = op["content"] + output_lines.append(f" └─ Content: {content}") + if "status" in op: + output_lines.append(f" └─ Status: {op['status']}") + output_lines.append("") + output_content = "\n".join(output_lines) + # Write output to file + input_path = Path(file_path) + output_filename = f"display_viewer_output_{args.output}.txt" + output_path = input_path.parent / output_filename + try: + with open(output_path, 'w', encoding='utf-8') as f: + f.write(output_content) + print(f"Output written to: {output_path}") + except Exception as e: + print(f"Error writing output file: {e}") + sys.exit(1) + + if args.dir: + for root, dirs, files in os.walk(args.dir): + for file in files: + if file == "display.json": + file_path = os.path.join(root, file) + print(f"Processing: {file_path}") + process_one_file(file_path) + return + + file_path = args.file + if not file_path: + file_path = find_latest_display_json() + if not file_path: + print( + "Error: Cannot find display.json file, please specify file path using --file parameter" + ) + sys.exit(1) + print(f"Using the latest display.json file: {file_path}") + process_one_file(file_path) + + +if __name__ == "__main__": + """ + python display_viewer.py --file + python display_viewer.py --dir + """ + main() diff --git a/mm_agents/maestro/utils/embedding_manager.py b/mm_agents/maestro/utils/embedding_manager.py new file mode 100644 index 0000000..f270efe --- /dev/null +++ b/mm_agents/maestro/utils/embedding_manager.py @@ -0,0 +1,53 @@ +import numpy as np +from ..utils.common_utils import ( + load_embeddings, + save_embeddings, +) +import os + +# List all embeddings' keys and their shapes +def list_embeddings(embeddings_path: str): + if not os.path.exists(embeddings_path): + print(f"[EmbeddingManager] File not found: {embeddings_path}") + return {} + embeddings = load_embeddings(embeddings_path) + info = {} + for k, v in embeddings.items(): + arr = np.array(v) + info[k] = {'shape': arr.shape, 'preview': arr.flatten()[:5].tolist()} + return info + +# Delete a specific embedding by key +def delete_embedding(embeddings_path: str, key: str) -> bool: + if not os.path.exists(embeddings_path): + print(f"[EmbeddingManager] File not found: {embeddings_path}") + return False + embeddings = load_embeddings(embeddings_path) + if key not in embeddings: + print(f"[EmbeddingManager] Key not found: {key}") + return False + del embeddings[key] + save_embeddings(embeddings_path, embeddings) + print(f"[EmbeddingManager] Deleted embedding for key: {key}") + return True + +def delete_empty_shape_embeddings(embeddings_path: str) -> int: + """Delete all embeddings whose value is empty (shape==0), shape==(), or content is error string, and return the number deleted.""" + if not os.path.exists(embeddings_path): + print(f"[EmbeddingManager] File not found: {embeddings_path}") + return 0 + embeddings = load_embeddings(embeddings_path) + to_delete = [] + for k, v in embeddings.items(): + arr = np.array(v) + # Delete shape==0 or shape==() or content is string/error information + if arr.size == 0 or arr.shape == () or ( + isinstance(v, list) and v and isinstance(v[0], str) and v[0].startswith('Error:') + ) or (isinstance(v, str) and v.startswith('Error:')): + to_delete.append(k) + for k in to_delete: + del embeddings[k] + print(f"[EmbeddingManager] Deleted empty or error embedding for key: {k}") + if to_delete: + save_embeddings(embeddings_path, embeddings) + return len(to_delete) \ No newline at end of file diff --git a/mm_agents/maestro/utils/file_utils.py b/mm_agents/maestro/utils/file_utils.py new file mode 100644 index 0000000..a819f6b --- /dev/null +++ b/mm_agents/maestro/utils/file_utils.py @@ -0,0 +1,170 @@ +# file_utils.py +import json +import os +import logging +from pathlib import Path +from contextlib import contextmanager +from typing import Any, Dict, List, Optional + +logger = logging.getLogger(__name__) + +# ========= File Lock Tools ========= +@contextmanager +def locked(path: Path, mode: str): + """File lock context manager for cross-platform compatibility""" + if os.name == "nt": + # Windows implementation + import msvcrt + import time as _t + + # Always use UTF-8 encoding for text files on Windows + if 'b' in mode: + f = open(path, mode) + else: + f = open(path, mode, encoding="utf-8") + try: + while True: + try: + msvcrt.locking(f.fileno(), msvcrt.LK_NBLCK, 1) + break + except OSError: + _t.sleep(0.01) + yield f + finally: + f.seek(0) + msvcrt.locking(f.fileno(), msvcrt.LK_UNLCK, 1) + f.close() + else: + # Unix-like systems implementation + import fcntl + + # Always use UTF-8 encoding for text files on Unix-like systems + if 'b' in mode: + f = open(path, mode) + else: + f = open(path, mode, encoding="utf-8") + try: + fcntl.flock(f.fileno(), fcntl.LOCK_EX) + yield f + finally: + fcntl.flock(f.fileno(), fcntl.LOCK_UN) + f.close() + +# ========= Safe JSON Operations ========= +def safe_json_dump(data: Any, file_handle, **kwargs) -> None: + """Safely dump JSON data with proper encoding handling""" + kwargs.setdefault('ensure_ascii', False) + kwargs.setdefault('indent', 2) + + try: + json.dump(data, file_handle, **kwargs) + except UnicodeEncodeError as e: + logger.warning(f"UnicodeEncodeError during JSON dump: {e}. Falling back to ASCII mode.") + kwargs['ensure_ascii'] = True + json.dump(data, file_handle, **kwargs) + +def safe_json_load(file_handle) -> Any: + """Safely load JSON data with proper encoding handling""" + try: + return json.load(file_handle) + except UnicodeDecodeError as e: + logger.warning(f"UnicodeDecodeError during JSON load: {e}. Attempting recovery.") + file_handle.seek(0) + content = file_handle.read() + + # Try common encodings + for encoding in ['utf-8-sig', 'latin1', 'cp1252']: + try: + if isinstance(content, bytes): + decoded_content = content.decode(encoding) + else: + decoded_content = content + return json.loads(decoded_content) + except (UnicodeDecodeError, json.JSONDecodeError): + continue + + logger.error("Failed to decode JSON with all attempted encodings. Returning empty data.") + return {} + +def safe_write_json(path: Path, data: Any) -> None: + """Safely write JSON data to file with atomic operation""" + tmp = path.with_suffix(".tmp") + try: + with locked(tmp, "w") as f: + safe_json_dump(data, f) + f.flush() + os.fsync(f.fileno()) + tmp.replace(path) + except Exception as e: + logger.error(f"Failed to write JSON to {path}: {e}") + if tmp.exists(): + try: + tmp.unlink() + except Exception: + pass + raise + +def safe_read_json(path: Path, default: Any = None) -> Any: + """Safely read JSON data from file""" + try: + with locked(path, "r") as f: + return safe_json_load(f) + except Exception as e: + logger.warning(f"Failed to read JSON from {path}: {e}") + return default if default is not None else [] + +# ========= Safe Text File Operations ========= +def safe_write_text(path: Path, content: str) -> None: + """Safely write text to file with UTF-8 encoding""" + try: + path.write_text(content, encoding='utf-8') + except UnicodeEncodeError as e: + logger.warning(f"UnicodeEncodeError writing to {path}: {e}. Using error handling.") + path.write_text(content, encoding='utf-8', errors='replace') + +def safe_read_text(path: Path) -> str: + """Safely read text from file with proper encoding handling""" + try: + return path.read_text(encoding='utf-8') + except UnicodeDecodeError as e: + logger.warning(f"UnicodeDecodeError reading {path}: {e}. Trying alternative encodings.") + for encoding in ['utf-8-sig', 'latin1', 'cp1252', 'gbk']: + try: + return path.read_text(encoding=encoding) + except UnicodeDecodeError: + continue + + logger.error(f"Failed to decode {path} with all encodings. Using error replacement.") + return path.read_text(encoding='utf-8', errors='replace') + +# ========= File Management Utilities ========= +def ensure_directory(path: Path) -> None: + """Ensure directory exists, create if necessary""" + path.mkdir(parents=True, exist_ok=True) + +def safe_file_operation(operation_name: str, file_path: Path, operation_func, *args, **kwargs): + """Generic safe file operation wrapper with error handling""" + try: + return operation_func(*args, **kwargs) + except FileNotFoundError: + logger.error(f"{operation_name}: File not found: {file_path}") + raise + except PermissionError: + logger.error(f"{operation_name}: Permission denied: {file_path}") + raise + except Exception as e: + logger.error(f"{operation_name}: Unexpected error with {file_path}: {e}") + raise + +def backup_file(file_path: Path, backup_suffix: str = ".backup") -> Path: + """Create a backup of a file""" + backup_path = file_path.with_suffix(file_path.suffix + backup_suffix) + try: + if file_path.exists(): + import shutil + shutil.copy2(file_path, backup_path) + logger.info(f"Backup created: {backup_path}") + return backup_path + except Exception as e: + logger.error(f"Failed to create backup of {file_path}: {e}") + raise \ No newline at end of file diff --git a/mm_agents/maestro/utils/id_utils.py b/mm_agents/maestro/utils/id_utils.py new file mode 100644 index 0000000..f296a74 --- /dev/null +++ b/mm_agents/maestro/utils/id_utils.py @@ -0,0 +1,69 @@ +# id_utils.py +import uuid +import time +import hashlib +from typing import Optional + +# Module-level counter for sequential IDs +_sequential_counter = 1 + +def generate_uuid() -> str: + """Generate a random UUID string""" + return str(uuid.uuid4()) + +def generate_short_id(prefix: str = "", length: int = 8) -> str: + """Generate a short random ID with optional prefix""" + # Generate UUID and take first N characters + short_uuid = str(uuid.uuid4()).replace("-", "")[:length] + return f"{prefix}{short_uuid}" if prefix else short_uuid + +def generate_timestamp_id(prefix: str = "") -> str: + """Generate ID based on current timestamp""" + timestamp = int(time.time() * 1000) # milliseconds + return f"{prefix}{timestamp}" if prefix else str(timestamp) + +def generate_hash_id(content: str, prefix: str = "", length: int = 8) -> str: + """Generate ID based on content hash""" + hash_obj = hashlib.md5(content.encode('utf-8')) + hash_hex = hash_obj.hexdigest()[:length] + return f"{prefix}{hash_hex}" if prefix else hash_hex + +def generate_sequential_id(prefix: str = "", start: int = 1) -> str: + """Generate sequential ID (not thread-safe, use with caution)""" + global _sequential_counter + if start != 1: # Reset counter if different start value + _sequential_counter = start + + current_id = _sequential_counter + _sequential_counter += 1 + return f"{prefix}{current_id}" if prefix else str(current_id) + +def generate_composite_id(prefix: str = "", include_timestamp: bool = True, + include_uuid: bool = True, separator: str = "_") -> str: + """Generate composite ID with multiple components""" + parts = [] + + if prefix: + parts.append(prefix) + + if include_timestamp: + parts.append(str(int(time.time() * 1000))) + + if include_uuid: + parts.append(str(uuid.uuid4())[:8]) + + return separator.join(parts) + +def validate_id_format(id_string: str, expected_prefix: Optional[str] = None, + min_length: int = 1, max_length: int = 100) -> bool: + """Validate ID format and constraints""" + if not id_string or not isinstance(id_string, str): + return False + + if len(id_string) < min_length or len(id_string) > max_length: + return False + + if expected_prefix and not id_string.startswith(expected_prefix): + return False + + return True \ No newline at end of file diff --git a/mm_agents/maestro/utils/image_axis_utils.py b/mm_agents/maestro/utils/image_axis_utils.py new file mode 100644 index 0000000..e4b6172 --- /dev/null +++ b/mm_agents/maestro/utils/image_axis_utils.py @@ -0,0 +1,27 @@ +from PIL import Image + + +def pad_to_square(image: Image.Image, + fill_color=(0, 0, 0), + padding: int = 0) -> Image.Image: + """ + First make it a square, then expand the padding pixels around it. + """ + width, height = image.size + if width == height: + square_img = image.copy() + else: + new_size = max(width, height) + square_img = Image.new(image.mode, (new_size, new_size), fill_color) + left = (new_size - width) // 2 + top = (new_size - height) // 2 + square_img.paste(image, (left, top)) + + if padding > 0: + final_size = square_img.size[0] + 2 * padding + padded_img = Image.new(square_img.mode, (final_size, final_size), + fill_color) + padded_img.paste(square_img, (padding, padding)) + return padded_img + else: + return square_img diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..b01ffa6 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,90 @@ +[project] +name = "osworld" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "numpy", + "backoff", + "pandas", + "openai", + "anthropic", + "fastapi", + "zhipuai", + "groq", + "boto3", + "uvicorn", + "paddleocr", + "paddlepaddle", + "together", + "scikit-learn", + "websockets", + "tiktoken", + "pyautogui~=0.9.54", + "toml", + "exa_py", + "black", + "certifi", + "pytesseract", + "google-genai", + "python-dotenv", + "Pillow~=11.0.0", + "fabric", + "gymnasium~=0.28.1", + "requests~=2.31.0", + "pytz~=2024.1", + "transformers~=4.35.2", + "torch~=2.5.0", + "accelerate", + "opencv-python~=4.8.1.78", + "matplotlib~=3.7.4", + "psutil~=5.9.6", + "tqdm~=4.65.0", + "flask~=3.0.0", + "requests-toolbelt~=1.0.0", + "lxml", + "cssselect", + "xmltodict", + "openpyxl", + "python-docx", + "python-pptx", + "pypdf", + "PyGetWindow", + "rapidfuzz", + "pyacoustid", + "pygame", + "ImageHash", + "scikit-image", + "librosa", + "pymupdf", + "chardet", + "playwright", + "formulas", + "pydrive", + "fastdtw", + "odfpy", + "func-timeout", + "beautifulsoup4", + "dashscope", + "google-generativeai", + "PyYaml", + "mutagen", + "easyocr", + "borb", + "pypdf2", + "pdfplumber", + "wandb", + "wrapt_timeout_decorator", + "gdown", + "azure-identity", + "azure-mgmt-compute", + "azure-mgmt-network", + "docker", + "loguru", + "dotenv", + 'pyobjc; platform_system == "Darwin"', + 'pywinauto; platform_system == "Windows"', + 'pywin32; platform_system == "Windows"', + "tldextract>=5.3.0", +] diff --git a/run_maestro.py b/run_maestro.py new file mode 100644 index 0000000..5366ad8 --- /dev/null +++ b/run_maestro.py @@ -0,0 +1,571 @@ +import argparse +import json +import datetime +import logging +import os +import sys +import time +import traceback +from pathlib import Path +from tqdm import tqdm +from dotenv import load_dotenv +from multiprocessing import Pool, cpu_count +from functools import partial +from desktop_env.desktop_env import DesktopEnv + +# Import from local mm_agents/maestro +try: + from mm_agents.maestro.maestro.controller.main_controller import MainController + from mm_agents.maestro.utils.analyze_display import analyze_display_json, format_output_line + from mm_agents.maestro.utils.common_utils import ImageDataFilter, SafeLoggingFilter +except Exception as e: + raise ImportError( + f"Failed to import maestro dependencies, please ensure mm_agents/maestro directory exists. Reason: {e}" + ) + +# Load .env from mm_agents/maestro directory +CURRENT_FILE = Path(__file__).resolve() +PROJECT_ROOT = CURRENT_FILE.parent +MAESTRO_ENV_PATH = PROJECT_ROOT / "mm_agents" / "maestro" / ".env" +if MAESTRO_ENV_PATH.exists(): + load_dotenv(dotenv_path=MAESTRO_ENV_PATH) + +logger = logging.getLogger() +logger.setLevel(logging.DEBUG) + +vm_datetime_str: str = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + +log_dir = "runtime" +vm_log_dir = os.path.join(log_dir, f"awsrun_{vm_datetime_str}") +os.makedirs(vm_log_dir, exist_ok=True) + +file_handler = logging.FileHandler( + os.path.join(vm_log_dir, "awsrun_normal.log"), encoding="utf-8" +) +debug_handler = logging.FileHandler( + os.path.join(vm_log_dir, "awsrun_debug.log"), encoding="utf-8" +) +stdout_handler = logging.StreamHandler(sys.stdout) +sdebug_handler = logging.FileHandler( + os.path.join(vm_log_dir, "awsrun_sdebug.log"), encoding="utf-8" +) + +file_handler.setLevel(logging.INFO) +debug_handler.setLevel(logging.DEBUG) +stdout_handler.setLevel(logging.INFO) +sdebug_handler.setLevel(logging.DEBUG) + +# Safe logging filter +safe_filter = SafeLoggingFilter() +debug_handler.addFilter(safe_filter) +sdebug_handler.addFilter(safe_filter) +file_handler.addFilter(safe_filter) +stdout_handler.addFilter(safe_filter) + +# Try to filter third-party library logs +try: + import openai # noqa: F401 + openai_logger = logging.getLogger('openai') + openai_logger.addFilter(safe_filter) + httpx_logger = logging.getLogger('httpx') + httpx_logger.addFilter(safe_filter) +except Exception: + pass + +if os.getenv('KEEP_IMAGE_LOGS', 'false').lower() != 'true': + image_filter = ImageDataFilter() + debug_handler.addFilter(image_filter) + sdebug_handler.addFilter(image_filter) + logging.getLogger().info("Image data filtering enabled - image data in debug logs will be filtered") +else: + logging.getLogger().info("Image data filtering disabled - debug logs will contain complete image data") + +logging.getLogger().info("Safe logging filter enabled - prevents format errors from third-party libraries (OpenAI, HTTPX)") + +formatter = logging.Formatter( + fmt="\x1b[1;33m[%(asctime)s \x1b[31m%(levelname)s \x1b[32m%(module)s/%(lineno)d-%(processName)s\x1b[1;33m] \x1b[0m%(message)s" +) +file_handler.setFormatter(formatter) +debug_handler.setFormatter(formatter) +stdout_handler.setFormatter(formatter) +sdebug_handler.setFormatter(formatter) + +stdout_handler.addFilter(logging.Filter("desktopenv")) +sdebug_handler.addFilter(logging.Filter("desktopenv")) + +logger.addHandler(file_handler) +logger.addHandler(debug_handler) +logger.addHandler(stdout_handler) +logger.addHandler(sdebug_handler) + +logger = logging.getLogger("desktopenv.experiment") + + +def config() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Run end-to-end evaluation on the benchmark (maestro integration)" + ) + + current_platform = "Ubuntu" + test_config_base_dir = os.path.join("evaluation_examples", "examples") + test_all_meta_path = os.path.join("evaluation_examples", "test_tiny.json") + + + # platform config + parser.add_argument( + "--current_platform", + type=str, + choices=["Ubuntu", "Windows"], + default=current_platform, + help="Platform to run on (Ubuntu or Windows)" + ) + + # environment config + parser.add_argument("--headless", action="store_true", help="Run in headless machine") + parser.add_argument("--action_space", type=str, default="pyautogui", help="Action type") + parser.add_argument( + "--observation_type", + choices=["screenshot", "a11y_tree", "screenshot_a11y_tree", "som"], + default="screenshot", + help="Observation type", + ) + parser.add_argument("--max_steps", type=int, default=50) + + # agent config + parser.add_argument("--test_config_base_dir", type=str, default=test_config_base_dir) + + # password config + parser.add_argument("--password", type=str, default="osworld-public-evaluation", help="Environment password for sudo operations") + + # example config + parser.add_argument("--domain", type=str, default="all") + parser.add_argument("--test_all_meta_path", type=str, default=test_all_meta_path) + + # logging related + parser.add_argument("--result_dir", type=str, default="./results") + parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to run in parallel") + + args = parser.parse_args() + + # Convert to absolute paths to avoid cwd dependency + try: + repo_root = PROJECT_ROOT + if not os.path.isabs(args.test_config_base_dir): + args.test_config_base_dir = str((repo_root / args.test_config_base_dir).resolve()) + if not os.path.isabs(args.test_all_meta_path): + args.test_all_meta_path = str((repo_root / args.test_all_meta_path).resolve()) + except Exception: + pass + + return args + + +def process_with_delay_wrapper(task_with_index_and_args): + task_info, task_index, args, vm_log_dir, base_timestamp = task_with_index_and_args + time.sleep(task_index * 5) + return process_single_task(task_info, args, vm_log_dir, base_timestamp, task_index) + + +def process_single_task_no_delay(task_with_index_and_args): + """Worker function to process a single task without delay for queue mode""" + task_info, task_index, args, vm_log_dir, base_timestamp = task_with_index_and_args + return process_single_task(task_info, args, vm_log_dir, base_timestamp, task_index) + + +def process_single_task(task_info, args, vm_log_dir, base_timestamp=None, task_index=0): + """Worker function to process a single task""" + domain, example_id, config_file = task_info + + try: + with open(config_file, "r", encoding="utf-8") as f: + example = json.load(f) + + user_query = example["instruction"] + + if base_timestamp: + example_datetime_str = f"{base_timestamp}_{task_index:03d}" + else: + example_datetime_str = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")[:17] + + example_result_dir = os.path.join( + args.result_dir, + args.action_space, + args.observation_type, + domain, + example_id, + ) + os.makedirs(example_result_dir, exist_ok=True) + + try: + run_single_example( + None, + example, + user_query, + args, + example_result_dir, + [], # scores not needed in worker + vm_log_dir, + example_datetime_str, + ) + + except Exception as e: + logger.error(f"Exception in {domain}/{example_id}: {e}") + with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f: + f.write( + json.dumps( + {"Error": f"Time limit exceeded in {domain}/{example_id}"} + ) + ) + f.write("\n") + result = 0.0 + finally: + # env is created and managed within run_single_example + pass + + + except Exception as e: + logger.error(f"Fatal error in task {domain}/{example_id}: {e}") + traceback.print_exc() + return domain, example_id, 0.0 + + +def test(args: argparse.Namespace, test_all_meta: dict) -> None: + scores = [] + + logger.info("Args: %s", args) + cfg_args = { + "headless": args.headless, + "action_space": args.action_space, + "observation_type": args.observation_type, + "max_steps": args.max_steps, + "result_dir": args.result_dir, + } + + # Prepare tasks list + tasks = [] + for domain in test_all_meta: + domain_sanitized = str(domain).strip() + for example_id in test_all_meta[domain]: + example_id_sanitized = str(example_id).strip() + config_file = os.path.join( + args.test_config_base_dir, + domain_sanitized, + f"{example_id_sanitized}.json" + ) + + if not os.path.exists(config_file): + try: + candidate_dir = os.path.join(args.test_config_base_dir, domain_sanitized) + existing_files = [] + if os.path.isdir(candidate_dir): + existing_files = os.listdir(candidate_dir) + logger.error(f"Config file not found: {config_file}") + logger.error(f"Existing files in {candidate_dir}: {existing_files}") + except Exception as e: + logger.error(f"Error while listing directory for debug: {e}") + raise FileNotFoundError(config_file) + + tasks.append((domain_sanitized, example_id_sanitized, config_file)) + + if args.num_envs > 1: + # Parallel processing with task queue - fixed number of workers + num_workers = args.num_envs + logger.info(f"Processing {len(tasks)} tasks with {num_workers} workers in queue mode...") + + # Process tasks with fixed worker pool - tasks will queue and wait for available workers + with Pool(processes=num_workers) as pool: + results = [] + for i, task in enumerate(tasks): + base_timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + # Add 5 second delay between task submissions + if i > 0: + time.sleep(5) + + task_with_args = (task, i, args, vm_log_dir, base_timestamp) + result = pool.apply_async(process_single_task_no_delay, (task_with_args,)) + results.append(result) + logger.info(f"Submitted task {i+1}/{len(tasks)}: {task[0]}/{task[1]}") + + # Wait for all tasks to complete + final_results = [result.get() for result in results] + + else: + # Sequential processing (original logic) + for domain, example_id, config_file in tqdm(tasks, desc="Processing tasks"): + logger.info(f"[Domain]: {domain}") + logger.info(f"[Example ID]: {example_id}") + + with open(config_file, "r", encoding="utf-8") as f: + example = json.load(f) + + user_query = example["instruction"] + logger.info(f"[User Query]: {user_query}") + + cfg_args["user_query"] = user_query + cfg_args["start_time"] = datetime.datetime.now().strftime( + "%Y:%m:%d-%H:%M:%S" + ) + + example_datetime_str = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + + example_result_dir = os.path.join( + args.result_dir, + args.action_space, + args.observation_type, + domain, + example_id, + ) + os.makedirs(example_result_dir, exist_ok=True) + + try: + run_single_example( + None, # env will be created in run_single_example for sequential mode + example, + user_query, + args, + example_result_dir, + scores, + vm_log_dir, + example_datetime_str, + ) + except Exception as e: + logger.error(f"Exception in {domain}/{example_id}: {e}") + # Note: env creation moved to run_single_example for sequential mode + + +def run_single_example( + env: DesktopEnv | None, + example, + user_query: str, + args, + example_result_dir, + scores, + vm_log_dir: str, + example_datetime_str: str, +): + example_timestamp_dir = os.path.join(vm_log_dir, example_datetime_str) + total_start_time = time.time() + cache_dir = os.path.join(example_timestamp_dir, "cache", "screens") + state_dir = os.path.join(example_timestamp_dir, "state") + + os.makedirs(cache_dir, exist_ok=True) + os.makedirs(state_dir, exist_ok=True) + + example_logger = setup_example_logger(example, example_timestamp_dir) + example_logger.info(f"Starting example {example.get('id', 'unknown')}") + example_logger.info(f"User Query: {user_query}") + + # Create environment if not provided (for sequential mode) + if env is None: + # Read proxy setting from example config, default to False if not specified + enable_proxy = example.get("proxy", False) + logger.info(f"Proxy status: {enable_proxy}") + env = DesktopEnv( + provider_name="aws", + region="us-east-1", + action_space=args.action_space, + headless=args.headless, + require_a11y_tree=False, + enable_proxy=enable_proxy + ) + + env.reset(task_config=example) + + controller = MainController( + platform=args.current_platform, + backend="pyautogui_vmware", + user_query=user_query, + max_steps=args.max_steps, + env=env, + env_password=args.password, + log_dir=vm_log_dir, + datetime_str=example_datetime_str, + ) + + try: + controller.execute_main_loop() + task = controller.global_state.get_task() + if task and task.status == "fulfilled": + logger.info("Task completed successfully") + env.step("DONE") + elif task and task.status == "rejected": + logger.info("Task was rejected/failed") + env.step("FAIL") + else: + logger.info("Task execution completed with unknown status") + env.step("DONE") + + # Retry mechanism for evaluate method + max_retries = 3 + retry_delay = 5 # seconds + result = 0 + + for attempt in range(max_retries): + try: + result = env.evaluate() + logger.info("Result: %.2f", result) + example_logger.info("Result: %.2f", result) + example_logger.info(f"Example {example.get('id', 'unknown')} completed with result: {result}") + break # Success, exit retry loop + except Exception as e: + logger.warning(f"Evaluate attempt {attempt + 1}/{max_retries} failed: {e}") + if attempt < max_retries - 1: # Not the last attempt + logger.info(f"Waiting {retry_delay} seconds before retry...") + time.sleep(retry_delay) + else: + logger.error("All evaluate attempts failed, setting result to 0") + result = 0 + example_logger.info("Result: %.2f", result) + example_logger.info(f"Example {example.get('id', 'unknown')} completed with result: {result} (after failed retries)") + scores.append(result) + with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f: + f.write(f"{result}\n") + + except Exception as e: + logger.error(f"Error during maestro execution: {e}") + raise + finally: + total_end_time = time.time() + total_duration = total_end_time - total_start_time + logger.info(f"Total execution time: {total_duration:.2f} seconds") + auto_analyze_execution(example_timestamp_dir) + + env.close() + + +def auto_analyze_execution(timestamp_dir: str): + import time as _t + try: + display_json_path = os.path.join(timestamp_dir, "display.json") + max_wait_time = 10 + wait_interval = 0.5 + waited_time = 0 + while waited_time < max_wait_time: + if os.path.exists(display_json_path): + try: + size1 = os.path.getsize(display_json_path) + _t.sleep(wait_interval) + size2 = os.path.getsize(display_json_path) + if size1 == size2: + logger.info(f"Display.json file appears to be complete (size: {size1} bytes)") + break + else: + logger.info(f"Display.json file still being written (size changed from {size1} to {size2} bytes)") + waited_time += wait_interval + continue + except OSError: + _t.sleep(wait_interval) + waited_time += wait_interval + continue + else: + logger.info(f"Waiting for display.json file to be created... ({waited_time:.1f}s)") + _t.sleep(wait_interval) + waited_time += wait_interval + if os.path.exists(display_json_path): + logger.info(f"Auto-analyzing execution statistics from: {display_json_path}") + result = analyze_display_json(display_json_path) + if result: + output_line = format_output_line(result) + logger.info("=" * 80) + logger.info("EXECUTION STATISTICS:") + logger.info("Steps, Duration (seconds), (Input Tokens, Output Tokens, Total Tokens), Cost") + logger.info("=" * 80) + logger.info(output_line) + logger.info("=" * 80) + else: + logger.warning("No valid data found in display.json for analysis") + else: + logger.warning(f"Display.json file not found at: {display_json_path} after waiting {max_wait_time} seconds") + except Exception as e: + logger.error(f"Error during auto-analysis: {e}") + + +def setup_example_logger(example, example_timestamp_dir): + example_id = example.get('id', 'unknown') + example_logger = logging.getLogger(f"example.{example_id}.{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}") + example_logger.setLevel(logging.DEBUG) + + example_logger.handlers.clear() + + log_file = os.path.join(example_timestamp_dir, "example.log") + file_handler = logging.FileHandler(log_file, encoding="utf-8") + file_handler.setLevel(logging.DEBUG) + + debug_log_file = os.path.join(example_timestamp_dir, "example_debug.log") + debug_handler = logging.FileHandler(debug_log_file, encoding="utf-8") + debug_handler.setLevel(logging.DEBUG) + + formatter = logging.Formatter( + fmt="\x1b[1;33m[%(asctime)s \x1b[31m%(levelname)s \x1b[32m%(module)s/%(lineno)d-%(processName)s\x1b[1;33m] \x1b[0m%(message)s" + ) + file_handler.setFormatter(formatter) + debug_handler.setFormatter(formatter) + + example_logger.addHandler(file_handler) + example_logger.addHandler(debug_handler) + + return example_logger + +def get_unfinished( + action_space, observation_type, result_dir, total_file_json +): + target_dir = os.path.join(result_dir, action_space, observation_type) + + if not os.path.exists(target_dir): + return total_file_json + + finished = {} + for domain in os.listdir(target_dir): + finished[domain] = [] + domain_path = os.path.join(target_dir, domain) + if os.path.isdir(domain_path): + for example_id in os.listdir(domain_path): + if example_id == "onboard": + continue + example_path = os.path.join(domain_path, example_id) + if os.path.isdir(example_path): + if "result.txt" not in os.listdir(example_path): + # empty all files under example_id + for file in os.listdir(example_path): + os.remove(os.path.join(example_path, file)) + else: + finished[domain].append(example_id) + + if not finished: + return total_file_json + + for domain, examples in finished.items(): + if domain in total_file_json: + total_file_json[domain] = [ + x for x in total_file_json[domain] if x not in examples + ] + + return total_file_json + +if __name__ == "__main__": + """ + xvfb-run -a python run_maestro.py --test_all_meta_path evaluation_examples/test_nogdrive.json --num_envs 15 + """ + + os.environ["TOKENIZERS_PARALLELISM"] = "false" + args = config() + + with open(args.test_all_meta_path, "r", encoding="utf-8") as f: + test_all_meta = json.load(f) + + if args.domain != "all": + test_all_meta = {args.domain: test_all_meta[args.domain]} + + test_file_list = get_unfinished( + args.action_space, + args.observation_type, + args.result_dir, + test_all_meta, + ) + + left_info = "" + for domain in test_file_list: + left_info += f"{domain}: {len(test_file_list[domain])}\n" + logger.info(f"Left tasks:\n{left_info}") + + test(args, test_file_list) \ No newline at end of file diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..17ee86e --- /dev/null +++ b/uv.lock @@ -0,0 +1,7404 @@ +version = 1 +revision = 3 +requires-python = ">=3.12" +resolution-markers = [ + "python_full_version >= '3.13' and sys_platform == 'darwin'", + "python_full_version >= '3.13' and platform_machine == 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