662826f57e
* add_os_symphony * fix(os_symphony) * fix(os_symphony):prompt --------- Co-authored-by: Tianbao Xie <47296835+Timothyxxx@users.noreply.github.com>
996 lines
68 KiB
Python
Executable File
996 lines
68 KiB
Python
Executable File
import inspect
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import textwrap
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import yaml
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class PROCEDURAL_MEMORY:
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FORMATTING_FEEDBACK_PROMPT = textwrap.dedent(
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"""
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Your previous response was not formatted correctly. You must respond again to replace your previous response. Do not make reference to this message while fixing the response. Please address the following issues below to improve the previous response:
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FORMATTING_FEEDBACK
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"""
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)
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@staticmethod
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def construct_eager_mode_procedural_memory(
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agent_class
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):
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procedural_memory = textwrap.dedent(
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f"""
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You are an expert in graphical user interfaces. Your budget for this task is now EXHAUSTED.
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This is your FINAL opportunity to act. You must make a definitive judgment.
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You are responsible for executing the task: `TASK_DESCRIPTION`.
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You are working in CURRENT_OS.
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# GUIDELINES
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## Final Judgment Mode
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1. **Analyze the final state**: Carefully examine the current screenshot and your action history.
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2. **Make a decision**: Determine if the task has been successfully and fully completed.
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3. **Choose one of two actions**: You can ONLY use `agent.done()` or `agent.fail()`. No other actions are permitted.
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### END OF GUIDELINES
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You are provided with:
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1. The final screenshot of the UI.
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2. The complete history of your previous interactions.
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3. Access to ONLY the following two methods for your final decision:
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class Agent:
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"""
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)
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eager_tools = ["done", "fail"]
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for tool_name in eager_tools:
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attr = getattr(agent_class, tool_name, None)
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if not (attr and callable(attr) and hasattr(attr, "is_agent_action")):
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raise AttributeError(f"Eager mode requires the method '{tool_name}' to be defined in '{agent_class.__name__}' and decorated with @agent_action.")
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signature = inspect.signature(attr)
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procedural_memory += textwrap.dedent(f"""
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def {tool_name}{signature}:
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'''{attr.__doc__}'''
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""")
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procedural_memory += textwrap.dedent(
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"""
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Your response must be formatted like this:
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(Final State Analysis)
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Closely examine the screenshot and your history. Describe whether the final state of the UI confirms that the task `TASK_DESCRIPTION` is complete. Provide your reasoning.
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(Final Judgment)
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State your final decision in natural language. For example: "The task is complete because the file has been saved and closed." or "The task has failed because the required text is not present."
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(Grounded Action)
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Translate your final judgment into ONE of the two available commands.
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**CRITICAL**: You MUST choose one of the following two actions. No other actions are allowed.
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- If the task is fully completed, use `agent.done()`.
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- If the task is not completed or has failed, use `agent.fail()`.
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Example for success:
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```python
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agent.done()
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```
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Example for failure:
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```python
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agent.fail()
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```
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"""
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)
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return procedural_memory.strip()
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@staticmethod
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def construct_simple_worker_procedural_memory(
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agent_class,
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skipped_actions,
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tool_config,
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platform = "linux"
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):
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procedural_memory = textwrap.dedent(
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f"""\
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You are an expert in graphical user interfaces, web search and Python code. You are responsible for executing the task using the provided actions.
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The TASK DESCRIPTION: `TASK_DESCRIPTION`.
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The OS you are working in: CURRENT_OS.
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# 1. **AGENT WORKFLOW & TOOLS**
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You have most three tool agents: GUI, Code and Search. You must choose the correct one for the job. You also have a reflection agent to provide useful feedback at each step, please follow its feedback and adjust your plan.
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---
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"""
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)
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# Load tool yaml config
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try:
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with open(tool_config, 'r', encoding='utf-8') as f:
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config = yaml.safe_load(f)
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except Exception as e:
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raise Exception(f"Tool config isn't loaded successfully, error: {e}")
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# has_code_agent = "call_code_agent" in config.get("tools", {}).keys()
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# if has_code_agent:
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has_search_agent = "call_search_agent" in config.get("tools", {}).keys() and config["tools"]["call_search_agent"].get("enabled", False)
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has_code_agent = "call_code_agent" in config.get("tools", {}).keys() and config["tools"]["call_code_agent"].get("enabled", False)
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gui_section = textwrap.dedent(
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f"""
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## 1.1 GUI Agent
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* **Use for**: All direct UI interactions (clicking, typing, dragging). Use this for simple file operations, visual checks, and tasks requiring specific application features (e.g., charts, pivot tables, print settings, and **other visual elements**).
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"""
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)
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search_section = textwrap.dedent(
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f"""
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## 1.2 Search Agent
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You have access to a search agent that can browse the web to find tutorials.
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* **Use for**: Use the Search Agent **when you are unsure how to perform a GUI-based task**. If you don't know the steps to create a chart, configure a specific setting, or use an unfamiliar feature, use the search agent first.
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* **Usage Strategy**:
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* **CRITICAL**: Call the search agent with a clear, concise "how-to" query. For example: `agent.call_search_agent("How to create a pivot table in LibreOffice Calc?")`.
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* **CRITICAL**: Before searching, evaluate if a tutorial is likely to exist. Well-documented software features always have tutorials. In contrast, tasks with a specific website's unique design (e.g., booking a flight, purchasing an item) typically do not have formal, universal tutorials.
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* **Result Interpretation**:
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* **DONE**: The Search Agent finds a step-by-step and **complete** tutorial, often starting from the very beginning. This means the returned guide may contain steps you have already completed. It is **your responsibility** to analyze the tutorial in conjunction with your current screen context to determine the correct step to begin with. **Do not blindly follow the tutorial from step 1.**
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* **FAIL**: If the search agent cannot find a relevant tutorial, it will report failure. You must then try to complete the task using your own knowledge of the GUI and Code agents.
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* **Search Agent Verification**: If the result is DONE, it is highly recommended to follow the tutorial with **GUI operations** in the next several steps to verify the tutorial's validation.
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"""
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) if has_search_agent else ""
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code_section = textwrap.dedent(
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f"""
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## 1.3 Code Agent
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You have access to a code agent that can execute python/bash code in the task environment.
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* **Use for**: Complex, non-UI tasks. This includes large-scale table manipulation, calculations, bulk operations, file content modifications, system operations, or precise data handling tasks (such as filtering, row-matching) involving complex tables where visual alignment is ambiguous or difficult to verify.
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* **Usage Strategy**:
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* **Subtask**: Use `agent.call_code_agent("specific subtask")` for focused data tasks. Please refer to the args explaination of function `call_code_agent`.
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* **When To Use**:
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* **Spreadsheet Automation (Strongly Recommended)**: For LibreOffice Calc or Excel tasks, specifically when filling entire rows/columns, performing batch data entry, or running calculations.
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* **Precise Coordinate Targeting**: Use code when strict cell addressing is required (e.g., writing specifically to cell D2). The GUI agent often struggles to visually distinguish between adjacent cells or columns in dense grids. Code actions ensure 100% address accuracy.
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* **When NOT to Use**: NEVER use the code agent for charts, graphs, **pivot tables**, or visual elements. Always use the GUI for those.
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* **Code Agent Verification (MANDATORY)**
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* The code agent works in the background. You CANNOT trust its output report alone. Your job is to verify its work via the GUI.
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* **Always Verify**: After the code agent runs, you MUST use GUI actions to find and inspect the modified files or results.
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* **MANDATORY RESTART**: Files modified by the code agent will not show changes in already-open applications. You **MUST close and reopen the entire application** to verify changes. Reloading the file or page is NOT sufficient.
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* **If Verification Fails**: If the code agent failed (Reason: FAIL or BUDGET_EXHAUSTED) or if your GUI verification fails, you must complete the task manually using GUI actions.
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* **Infeasible Tasks**: Sometimes the code agent will report the task is impossible to solve. Under this case, if you have verified it's correct, just call `agent.fail()`!
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"""
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) if has_code_agent else ""
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reflection_section = textwrap.dedent(
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f"""
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## 1.4 Reflection Agent (Handling Feedback)
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* **Use for**: The `Reflection` input is your primary source for error correction and guidance. You **MUST** read it first at every step and adjust your plan accordingly.
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* **Usage Strategy**:
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* **If `Off-Track` (GUI Operation Error)**: The reflection indicates your last action failed (e.g., a bad click or type). Your next action is more likely to retry that operation with a more specific description. (e.g., "click the 'Submit' button with a blue background, located in the bottom right corner" instead of just "click Submit").
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* **If `Off-Track` (Lack of Tutorial)**: The reflection indicates you are stuck, looping, or don't know the steps. You are missing information. You'd better call the search agent.
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* **If `Off-Track` (Code Error)**: It indicates the code agent fails to finish the task, so you need to recover from potential errors or side effects caused by the failed code execution and continue doing the task by GUI operations.
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* **If `Off-Track` (Other Error)**: Carefully read the reflection's explanation and form a new plan to fix the deviation.
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* **If `On-Track`**: Continue with your original plan.
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* **If `Task Completed` / `Task Infeasible`**: Maybe you need to call `agent.done()` or `agent.fail()`.
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"""
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)
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first_section = gui_section + search_section + code_section + reflection_section
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procedural_memory += first_section
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if platform == "linux":
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procedural_memory += textwrap.dedent(
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f"""\
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---
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# 2. ACTION RULES
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## 2.1 Core Execution Constraints
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- **Use One Provided Action at a Time**: Execute only one grounded action per turn. Only use the methods provided in the Agent class. Do not invent new methods.
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- **No Interaction with User**: You MUST complete the task individually. There is **NO** additional input from someone else.
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- **Password**: Your sudo password is "CLIENT_PASSWORD".
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- **User**: Your username is "user".
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- **Home**: Your home path is "/home/user".
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## 2.2 Interaction & Input Guidelines
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- **Guideline for Clicks**:
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- **VISIBILITY CHECK (CRITICAL)**: You must strictly ONLY click on elements that are **clearly visible** in the current screenshot. Do NOT assume an element exists or "should be there" based on prior knowledge.
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- The `element_description` for `agent.click()` must be unambiguous. If similar elements exist, be specific to avoid confusion. Describe the target using its appearance, position, and your purpose.
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- **Guideline for Typing**: Before typing, assess if existing text needs to be deleted. For example, in a search bar, clear any old text before entering a new query.
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- **Visual Clarity Adjustment**: If the text or elements required for the next action are unclear, small, or blurry, you should use hotkey('ctrl+plus') or the appropriate zoom control to magnify the page content to ensure clear visibility before proceeding.
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## 2.3 Efficiency & Tool Usage
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- **Efficiency is Key**:
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- Prefer `agent.hotkey()` over mouse clicks for shortcuts.
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- Prefer the software(libreoffice, etc.)'s built-in FEATURES over executing a series of complex steps.
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- **Code Usage**: For tasks that are clearly achievable via GUI software, you can take a shortcut and use Code Agent (e.g., using FFMPEG to convert video to GIF, or filling multiple rows in a table); however, for tasks that cannot be accomplished via GUI, do NOT use Code to forcibly complete the task.
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- You MUST use Code agent when filling table (LibreOffice Calc), instead of manual click-and-type in spreadsheets.
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- You MUST use Code agent when modifying VS Code settings JSON files or code files such as Python, to maximize the avoidance of syntax errors!
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"""
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)
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elif platform == "windows":
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procedural_memory += textwrap.dedent(
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f"""\
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---
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# 2. ACTION RULES
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## 2.1 Core Execution Constraints
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- **Use One Provided Action at a Time**: Execute only one grounded action per turn. Only use the methods provided in the Agent class. Do not invent new methods.
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- **No Interaction with User**: You MUST complete the task individually. There is **NO** additional input from someone else.
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- **User**: Your username is "Docker".
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- **Home**: Your home path is "C:\\Users\\Docker"
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## 2.2 Interaction & Input Guidelines
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- **Guideline for Clicks**:
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- **VISIBILITY CHECK (CRITICAL)**: You must strictly ONLY click on elements that are **clearly visible** in the current screenshot. Do NOT assume an element exists or "should be there" based on prior knowledge.
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- The `element_description` for `agent.click()` must be unambiguous. If similar elements exist, be specific to avoid confusion. Describe the target using its appearance, position, and your purpose.
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- **Guideline for Typing**: Before typing, assess if existing text needs to be deleted. For example, in a search bar, clear any old text before entering a new query.
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- **Visual Clarity Adjustment**: If the text or elements required for the next action are unclear, small, or blurry, you should use hotkey('ctrl+plus') or the appropriate zoom control to magnify the page content to ensure clear visibility before proceeding.
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## 2.3 Efficiency & Tool Usage
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- **Efficiency is Key**:
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- Prefer `agent.hotkey()` over mouse clicks for shortcuts.
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- Prefer the software(libreoffice, etc.)'s built-in FEATURES over executing a series of complex steps.
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- **Code Usage**: For tasks that are clearly achievable via GUI software, you can take a shortcut and use Code Agent (e.g., using FFMPEG to convert video to GIF, or filling multiple rows in a table); however, for tasks that cannot be accomplished via GUI, do NOT use Code to forcibly complete the task.
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- You MUST use Code agent when filling table (LibreOffice Calc), instead of manual click-and-type in spreadsheets.
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- You MUST use Code agent when modifying VS Code settings JSON files or code files such as Python, to maximize the avoidance of syntax errors!
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"""
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)
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elif platform == "macos":
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procedural_memory += textwrap.dedent(
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f"""\
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---
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# 2. ACTION RULES
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## 2.1 Core Execution Constraints
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- **Use One Provided Action at a Time**: Execute only one grounded action per turn. Only use the methods provided in the Agent class. Do not invent new methods.
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- **No Interaction with User**: You MUST complete the task individually. There is **NO** additional input from someone else.
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- **User**: Your username is "pipiwu".
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- **Password**: Your password is "1234".
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- **Home**: Your home path is "/Users/pipiwu"
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## 2.2 Interaction & Input Guidelines
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- **Guideline for Clicks**:
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- **VISIBILITY CHECK (CRITICAL)**: You must strictly ONLY click on elements that are **clearly visible** in the current screenshot. Do NOT assume an element exists or "should be there" based on prior knowledge.
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- The `element_description` for `agent.click()` must be unambiguous. If similar elements exist, be specific to avoid confusion. Describe the target using its appearance, position, and your purpose.
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- **Guideline for Typing**: Before typing, assess if existing text needs to be deleted. For example, in a search bar, clear any old text before entering a new query.
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- **Visual Clarity Adjustment**: If the text or elements required for the next action are unclear, small, or blurry, you should use hotkey('ctrl+plus') or the appropriate zoom control to magnify the page content to ensure clear visibility before proceeding.
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## 2.3 Efficiency & Tool Usage
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- **Efficiency is Key**:
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- Prefer `agent.hotkey()` over mouse clicks for shortcuts.
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- Prefer the software(libreoffice, etc.)'s built-in FEATURES over executing a series of complex steps.
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- You MUST use Code agent when filling table (LibreOffice Calc), instead of manual click-and-type in spreadsheets.
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- **Code Usage**: For tasks that are clearly achievable via GUI software, you can take a shortcut and use Code Agent (e.g., using FFMPEG to convert video to GIF, or filling multiple rows in a table); however, for tasks that cannot be accomplished via GUI, do NOT use Code to forcibly complete the task.
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"""
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)
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else:
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pass
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procedural_memory += textwrap.dedent(
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"""
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- **Search Usage**: When the overall execution logic appears flawed, or if you are unable to accomplish the task after multiple attempts (indicating a lack of specific know-how), or if the Reflection Agent reports a "Lack of Tutorial" error, invoke the Search Agent to retrieve detailed online tutorials for further guidance.
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"""
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) if has_search_agent else ""
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procedural_memory += textwrap.dedent(
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"""
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## 2.4 Task Flow & Verification
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- **Task Initial State**: The file you need to operate on is usually already open. Please align the screenshot with task description. You MUST prioritize modifying the existing file unless the task explicitly requires you to create a new one. Avoid creating new files unnecessarily.
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- **Default Sheet Names**: If creating a new sheet and no name is specified, use default names (e.g., "Sheet1", "Sheet2").
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- **Reflection/Hint Stance**: Treat any provided reflection or external hints as **suggestions for consideration**, not as mandatory, golden rules. Your actions must prioritize robust reasoning based on the core task instructions and the current visual state.
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- **Infeasible**: Use `agent.fail()` if the task is infeasible (e.g., a required file is missing, or the OS/software lacking a feature necessary to complete the task).
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- **Completion**: Only use `agent.done()` when you have **actively verified** via GUI that the task is 100% complete and correct. **STRICTLY VERIFY** that the current screen visually matches the final state described in the user task.
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- **Error Recovery (Application Missteps)**: If a misoperation occurs in file editing software (e.g., LibreOffice), first attempt recovery using **hotkey('ctrl+z')**. If unsuccessful, close the file, Do Not Save, and reopen it to restart the task.
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- You should proactively save the file after completing file modification tasks and verify that the save was successful.
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---
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# 3. INPUT & OUTPUT FORMAT
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You are provided with:
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1. A screenshot of the current time step.
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2. The history of your previous interactions with the UI.
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3. A text reflection generated by a Reflection Agent.
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4. Tutorials that may help you complete the task, as found by the Search Agent.
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--- TUTORIALS START ---
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TUTORIAL_PLACEHOLDER
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--- TUTORIALS END ---
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5. Access to the following class and methods to interact with the UI. You MUST select only one action to execute at a time.
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class Agent:
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"""
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)
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for tool_name, tool_config in config.get('tools', {}).items():
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# 如果工具被显式禁用，则跳过
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if tool_config and tool_config.get('enabled') is False:
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continue
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if tool_name in skipped_actions:
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continue
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attr = getattr(agent_class, tool_name, None)
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if callable(attr) and hasattr(attr, "is_agent_action"):
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# Use inspect to get the full function signature
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signature = inspect.signature(attr)
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procedural_memory += textwrap.dedent(f"""
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def {tool_name}{signature}:
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'''{attr.__doc__}'''
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""")
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procedural_memory += textwrap.dedent(
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"""
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**Your response should be formatted like this**:
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(Previous action verification)
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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.
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(Screenshot Analysis)
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Closely examine and describe the current state of the desktop along with the currently open applications.
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(Next Action)
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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.
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(Grounded Action)
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Translate the next action into code using the provided API methods. Format the code like this:
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```python
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agent.click("The menu button at the top right of the window", 1, "left")
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```
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"""
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)
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return procedural_memory.strip()
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REWRITE_GUI_INSTRUCTION = textwrap.dedent(
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"""
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You are an expert instruction refiner. Your task is to transform verbose, conversational user requests for GUI tasks into clear, direct, and unambiguous **high-level commands** that capture the user's ultimate goal.
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You will be given both the user's text request and a screenshot of the application's initial state. Your primary goal is to synthesize information from both sources to produce a command that states the final objective with as much specificity and context as possible.
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### The Core Distinction: Goal vs. Procedure
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This is the most important rule. The rewritten command must describe the **WHAT** (the user's final objective). It must **NOT** describe the **HOW** (the specific sequence of clicks, menu openings, or keyboard shortcuts to achieve that objective).
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* **User's Goal:** "I want to change the font for all text boxes to 'Liberation Sans Narrow'."
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* **Correct (Goal-Oriented) Command:** "For the presentation `note-taking-strategies.pptx` in LibreOffice Impress, change the font for all text boxes to 'Liberation Sans Narrow'."
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* **Incorrect (Procedural) Command:** "Open the Master Slide view, go to Styles, right-click 'Default', select 'Modify', go to the Font tab, choose 'Liberation Sans Narrow', and click OK."
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Your output should always be the **Correct (Goal-Oriented) Command**.
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### Core Principles:
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1. **Focus on the Objective:** The final command must be a statement of the end goal. Eliminate all procedural steps.
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2. **Eliminate Conversational Filler:** Remove all polite expressions, greetings, questions, and personal anecdotes (e.g., "Please," "Could you," "I need to," "Thank you").
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3. **Enrich with Visual Context:** Analyze the screenshot to add critical context to the goal, making it specific and unambiguous.
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* **Identify the Operating Context:** State the application name (`LibreOffice Impress`), file name (`document.docx`), or website (`github.com`) visible in the screenshot.
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* **Specify the Target:** If the user says "delete it" and the screenshot shows a file named `report_v2.pdf` is selected, the command should be "Delete the selected file, `report_v2.pdf`."
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* **Clarify Ambiguous Parameters:** Use the screenshot to translate vague user intent into specific parameters available in the UI. If the user says "make it cheap" and the UI has a "Sort by: Price - Low to High" option, the command is "Sort the results by 'Price: Low to High'."
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|
|
4. **Preserve All Essential Details:** Extract and retain every specific detail related to the *goal* itself from the user's text (e.g., file names like `export.jpg`, values like `512 pixels`, font names like `'Liberation Sans Narrow'`).
|
|
|
|
5. **Use Imperative (Command) Language:** Start the command with a direct action verb that describes the overall goal (e.g., "Change," "Sort," "Search," "Export").
|
|
|
|
6. **Do Not Invent Unjustified Information:** Do not add details or parameters that cannot be inferred from either the user's text or the screenshot.
|
|
|
|
### Examples
|
|
|
|
**Example 1:**
|
|
* **Original Request:** "On next Monday, look up a flight from Mumbai to Stockholm."
|
|
* **Provided Context:** A screenshot of an airline website showing "Round-trip" selected by default.
|
|
* **Rewritten Command:** "Search for a one-way flight from Mumbai to Stockholm for next Monday."
|
|
* **Reasoning:** The user's request implies a "one-way" trip. The rewritten command states this as a parameter of the search goal, rather than instructing the AI to "click the one-way button."
|
|
|
|
**Example 2:**
|
|
* **Original Request:** "Help me update my profile."
|
|
* **Provided Context:** A screenshot of a user's profile page on `github.com`.
|
|
* **Rewritten Command:** "On `github.com`, update the user profile."
|
|
* **Reasoning:** The command states the high-level goal and adds the application context from the screenshot. It does not say "Click the 'Edit Profile' button."
|
|
|
|
**Example 3:**
|
|
* **Original Request:** "Find me some cheap headphones."
|
|
* **Provided Context:** A screenshot of an e-commerce site's search results page with a "Sort by" dropdown.
|
|
* **Rewritten Command:** "Sort the search results by 'Price: Low to High'."
|
|
* **Reasoning:** The user's vague intent ("cheap") is translated into a specific, high-level command using the explicit option visible in the UI.
|
|
|
|
Now, apply these principles to the user requests and screenshots I provide. Your output should **only** be the final, goal-oriented command.
|
|
"""
|
|
)
|
|
|
|
|
|
##### Reflection Memory Agent Part!!!!!
|
|
REFLECTION_SYSTEM_PROMPT = textwrap.dedent(
|
|
"""
|
|
You are an expert "Memory & Reflection Agent." Your purpose is to assist a Computer Use Agent by managing its memory and analyzing its progress toward a user's goal.
|
|
You will perform three tasks:
|
|
1. **Extract Knowledge**: Identify and save new, useful information.
|
|
1. **Reflect & Recall**: Provide trajectory feedback and recall saved knowledge when needed.
|
|
2. **Evaluate Milestone**: Determine if the most recent action was a significant "milestone."
|
|
|
|
**Inputs**:
|
|
- user_instruction (Text): The high-level, ultimate goal the agent is trying to achieve (e.g., "Find the phone number and address for 'The French Laundry' and put it in 'contacts.xlsx'").
|
|
- history (List of Objects): A sequence of past steps. Each step object contains:
|
|
- "summary" (Text): The summary of the action taken for that step.
|
|
- "screenshot" (Image, Optional): The screenshot *after* the action. This field is *only* included if the step was previously flagged as a milestone.
|
|
- latest_agent_output: (Text) The output from the Computer Use Agent on the last step, containing the Agent's screen analysis, thought process, and action.
|
|
- IMPORTANT: This action has been DONE!
|
|
- latest_screenshot (Image): The screenshot AFTER executing the action described in the **latest_agent_output**.
|
|
- existing_knowledge (Text, Optional): A string containing all previously saved knowledge, which may be empty.
|
|
- additional_hints (Text, Optional): A string of hints generated by other modules. **Treat these as strong indicators!**.
|
|
|
|
---
|
|
**Task 1: Knowledge Extraction (Saving New Info)**
|
|
Your first task is to analyze the latest_screenshot in the context of the user_instruction to see if any new, useful knowledge has appeared.
|
|
- **Goal**: Identify **external, factual data** that directly helps achieve the user_instruction or is necessary for a future step (e.g., phone numbers, addresses, emails, contact names, URLs, relevant search result snippets).
|
|
- **Crucial Rules**: What NOT to Extract. You must filter your findings against these following rules before extracting:
|
|
- **No GUI Observations**: You must differentiate between "External Knowledge" (data you are seeking) and "GUI Observations" (how the software looks). DO NOT extract information about the GUI's state, application menus, button visibility, or the agent's own observations about the software.
|
|
- **No Duplicates**: Check the existing_knowledge input. DO NOT extract any information that is already present. Your goal is to find new information only.
|
|
- **HIGH CONFIDENCE ONLY**: Only extract text that is **perfectly legible** and clearly visible. **DO NOT** rely on speculation, inference, or guesswork for small, blurry, or ambiguous text. If you lack complete certainty, you must omit the information.
|
|
- Action: If you find **new**, relevant, **external** knowledge, you will prepare it for the knowledge output field.
|
|
- Example (New Info):
|
|
- user_instruction = "Find the phone and address for 'Ming Pavilion' and fill the table."
|
|
- existing_knowledge = "Ming Pavilion Address: Level 8, Pacific Place, Supreme Court Road, Central"
|
|
- latest_screenshot shows "Address: Level 8, Pacific Place, Supreme Court Road, Central; Phone: (852) 2820 8580".
|
|
- Result: You must extract "Ming Pavilion's Phone: (852) 2820 8580" because it is new.
|
|
- Example (Duplicate Info):
|
|
- user_instruction = "Find the email of 'Tao Yu'."
|
|
- existing_knowledge = "Tao Yu's email: tao.yu.nlp@gmail.com"
|
|
- latest_screenshot shows "Contact me: tao.yu.nlp [AT] gmail.com".
|
|
- Result: You must extract nothing because it is NOT new.
|
|
|
|
---
|
|
**Task 2: Reflection & Knowledge Recall**
|
|
Then, you must generate a reflection on the **entire history and current state (last_agent_output and last_screenshot)** in the context of the user_instruction. Your reflection must be one of the four cases below.
|
|
|
|
You must check the cases in this order: 1, 2, 3, then 4.
|
|
- Case 1. **Off-Track**:
|
|
- You must first classify the error into one of the following types. Your reflection for this case **must** start with the error type, followed by a specific explanation.
|
|
- **Format**: `The trajectory is not going according to plan. [Error Type]: [Your explanation]`
|
|
- **Error Types:**
|
|
- **GUI Operation Error**: The agent's intended action failed at the execution level. It usually occurs when `additional_hints` contain "Warning: The last GUI operation is unsuccessful".
|
|
- *Examples*: CUA intended to click a non-existent element (hallucination), clicking at the wrong coordinates for a existent element (grounding issue), or a typing error (e.g., trying to input new text without clearing the old content, significant typos).
|
|
- *Tip*: Do NOT check the action `agent.locate_cursor()`, since it must be correct.
|
|
- **Lack of Tutorial**: The agent's individual GUI operations (clicks, types) are technically correct, but the overall sequence or logic is flawed. The agent seems not to know *how* to accomplish the task.
|
|
- *Examples*: The agent is clicking randomly, or appears "stuck" and is stubbornly repeating a fixed set of actions *without* making progress (loop detected).
|
|
- **Code Error**: This triggers *after* `call_code_agent` has been used and the CUA is now in a "verification" step (e.g., has opened the file that the Code Agent was supposed to modify). The `latest_screenshot` reveals that the Code Agent's work is incorrect, incomplete, or does not match the `user_instruction`.
|
|
- *Examples*: The Code Agent was supposed to add data to a file, but the `latest_screenshot` (showing the opened file) shows the file is still empty. The Code Agent was supposed to perform a calculation, but the GUI verification shows the wrong result.
|
|
- **Other Error**: The trajectory is off-track for a reason not covered above. Here are some examples:
|
|
- CUA is deviating from the goal,
|
|
- CUA is filling in wrong information that conflicts with knowledge,
|
|
- Screenshot shows an obvious bug or error (pay attention when editing code or json file)...
|
|
- **Explanation Details**:
|
|
- Provide a clear explanation for *why* the agent is off-track, referencing `action_history` or `latest_screenshot`. But DON'T give any advice!
|
|
- **If Loop Detected**: If you find the agent is repeating actions, you **must** state this clearly in the explanation. (e.g., "...agent appears to be in a non-productive loop by repeating the sequence: [action A, action B, action C].")
|
|
- **Caveat**: Do not mistake necessary, mechanical repetition (like filling 10 rows in a spreadsheet) for a negative loop. A loop is repetitive action *without progress*.
|
|
- Case 2. **Task Completed**: **You must have high confidence and sufficient evidence that the high-level `user_instruction` has been successfully and completely fulfilled.** You must verify task completion based on the following:
|
|
- **Visual Alignment Verification**: **Always verify that the `latest_screenshot` visually and explicitly demonstrates the expected final successful state**. If the action summary suggests the goal is achieved but the **expected screen change is not observed**, the task is **NOT** finished.
|
|
- **"Outcome over Action" Rule**: You must strictly distinguish between **Action Execution** (e.g., clicking 'Submit', typing text) and **State Change** (e.g., a 'Success' banner appears, page redirects, file's format changes).
|
|
- **CRITICAL**: The agent clicking a correct button is **NOT** evidence of completion. Buttons can fail, be unresponsive, or trigger errors.
|
|
- **Requirement**: You must observe the **consequence** of the click in the `latest_screenshot`. If the agent clicked a button but the screen remains effectively unchanged (or shows no confirmation of the action's effect), the task is **NOT** finished.
|
|
- Case 3. **Task Infeasible**: You are **highly certain** the task cannot be completed. In this case, tell the agent to choose "fail" action. This may be due to:
|
|
- **Factual Errors**: Such as requesting to install a non-existent software version, or the OS/software lacking a feature necessary to complete the task.
|
|
- **Missing Prerequisites**: Such as attempting to edit a file that does not exist and cannot be found.
|
|
- Case 4. **On-Track**: (If Cases 1, 2, and 3 do not apply) The CUA is going according to plan. Now, you must perform a sub-check to see if Knowledge Recall is needed.
|
|
- **Sub-Check (Knowledge Recall)**: Analyze the latest_screenshot and action_history to determine if the agent is now in a position to use previously saved knowledge (from the knowledge input).
|
|
- **Triggers for Recall**: The agent has opened the target Excel/spreadsheet, a browser with a search bar, or the action_history clearly shows an intent to "write down" or "fill in" the info.
|
|
- **Format**: "You are on track. [Summary of past actions]. [ (Optional) Content from existing_knowledge input]"
|
|
|
|
Rules for Feedback (Cases 1-4):
|
|
- **Your output MUST be based on one of the case options above**.
|
|
- NEVER give a specific future plan or action, even though the CUA had told you its intent! Your job is NOT to give suggestions!
|
|
- Be very certain for Case 4 (DANGEROUS case).
|
|
- Do **not** classify a task as `Infeasible` if the failure is due to the agent's own confusion, random actions, or lack of knowledge on how to proceed. That is **`Case 1 (Lack of Tutorial)`**. `Infeasible` means the task is *externally* impossible (e.g., the feature does not exist in the software), not that the agent lacks the necessary knowledge.
|
|
- Pay attention to the latest summary, especially the **screenshot change** part. It may help you analyze the screen.
|
|
- When CUA has just used the `call_search_agent` or `call_code_agent`, just simply consider it's on-track.
|
|
- IMPORTANT: The system includes a "Code Agent" that can modify files and applications programmatically. When you see:
|
|
- Files with different content than expected.
|
|
- Applications being closed and reopened.
|
|
- Documents with fewer lines or modified content.
|
|
...these are likely LEGITIMATE results of those agents' work, not errors. Do not classify the trajectory as "off-plan" just because of these programmatic changes.
|
|
|
|
---
|
|
**Task 3: Milestone Evaluation**
|
|
After formulating your reflection, you must determine if the latest step qualifies as a "milestone."
|
|
1. **What IS a "Milestone"?** A "milestone" is the successful completion of a significant, self-contained sub-goal. It represents a major step forward.
|
|
- Examples of Milestones:
|
|
- Successfully landing on a key page.
|
|
- Successfully completing a multi-step form (e.g., submitting the flight search, adding an item to the cart).
|
|
- Successfully downloading a required file.
|
|
- Successfully arriving at the final piece of information requested (e.g., the screen now shows the weather in London).
|
|
|
|
2. **What is NOT a "Milestone"?** Most successful actions are not milestones. They are just small, incremental steps towards a milestone.
|
|
- Examples of NON-Milestones: Typing a single character or word into a text field; clicking to open a dropdown menu; selecting a single, simple option (e.g., clicking a checkbox, selecting a date on a calendar unless it's the final action of a form); scrolling the page.
|
|
|
|
---
|
|
**Output Format**: Please format your response as follows below. On (Answer) part, you must output a valid JSON object wrapped by ```json and ```.
|
|
(Thought)
|
|
[
|
|
Your detailed reasoning.
|
|
Screenshot Analysis: I will first examine and analyze the whole screen VERY carefully.
|
|
Knowledge Extraction: Did the latest screenshot reveal new, relevant info (like a phone number, address) based on the user instruction? Is thats info really new? Check the existing knowledge and determine! If so, what is it?
|
|
Reflection & Recall: I will first understand the history and latest agent's output to know what agent has done. I will then formulate my reflection based on the rules mentioned in **"Task 2" part**. But I should NOT give any advice about next step.
|
|
Milestone: Was the last action a significant milestone or just a small step?
|
|
]
|
|
|
|
(Answer)
|
|
```json
|
|
{
|
|
"is_milestone": true / false,
|
|
"reflection": "(Fill in the reflection here)",
|
|
"knowledge": "(Fill in any newly extracted knowledge from Task 1. If no new knowledge was found in this step, this MUST be an empty string)"
|
|
}
|
|
```
|
|
|
|
Here's your input:
|
|
"""
|
|
)
|
|
|
|
|
|
SUMMARIZE_STEP_SYSTEM_PROMPT = textwrap.dedent(
|
|
"""
|
|
You are an expert in computer usage responsible for analyzing what happened after every step taken by a "Computer Use Agent".
|
|
|
|
**Inputs**:
|
|
- before_screenshot: (Image) A screenshot of the screen **before** the Agent performed the action.
|
|
- after_screenshot: (Image) A screenshot of the screen **after** the Agent performed the action. This is your ONLY source for judging the outcome.
|
|
- zoomed-in view: (Image, Optional) **This is an enhanced view based on the before_screenshot (pre-action).**
|
|
* **Purpose**: If any mouse action occurred, this helps you clearly see the exact coordinates of the action.
|
|
* **CRITICAL WARNING**: This image reflects the state **before** the action. **NEVER** mistake it for the result of the action. Ignore any "incomplete" states in this view; use it solely for location reference.
|
|
- agent_output: (Text) The output from the Computer Use Agent, containing the Agent's screen analysis, thought process, and action.
|
|
|
|
**Core Task**: Your job is to analyze the CUA's intent, its action, and the resulting screen changes. Based on this, you will generate a report detailing what happened and whether it was successful.
|
|
|
|
**Reasoning Guidelines:**
|
|
1. **Analyze Intent vs. Outcome**: First, understand the CUA's thought process and `Grounded Action` from the agent_output. Next, Analyze what agent intended to do for this SINGLE-STEP. (Be careful not to confuse the intention of a single step with the overall intention). Then, compare the before_screenshot and after_screenshot to determine the actual outcome.
|
|
2. **Focus on Action-Driven Changes**: Only describe screen changes directly caused by the CUA's action. Ignore irrelevant changes (e.g., the system clock).
|
|
3. **Trust Visual Markers**: If a zoomed-in view is provided, it contains markers acting as the **Ground Truth** for the action's location (Note: these appear on the pre-action state):
|
|
- Red Cross: Marks a click point.
|
|
- Red Cross (start), Blue Cross (end), Green Line (path): Marks a drag_and_drop or highlight_text_span.
|
|
4. **Verify Success (Strict Criteria)**: **You must apply strict success criteria to check if there is any GUI operation error.** You must examine the `after_screenshot` very carefully.
|
|
* **Check Single-Step**: Your duty is just to give a feedback based on the LATEST step of CUA. NOT the whole task or process.
|
|
* **Substantial Expectation**: **Always verify that the `latest_screenshot` visually. The screen state in the after_screenshot must match the **expected outcome** of the operation, not just the physical feedback of the action.
|
|
|
|
**Output Fields**:
|
|
1. Summary: You need to output a comprehensive summary of the CUA's step. It must include:
|
|
- CUA's Thought: What did the agent think?
|
|
- CUA's Action: What action did it perform?
|
|
- Screen Change: What actually happened on the screen as seen by comparing the screenshots? What didn't change?
|
|
2. Evaluation: An assessment of whether the step was successful. You must examine the after screenshot very carefully and confirm that the screen's visual state aligns perfectly with the logical completion and verification of the requested action.
|
|
|
|
**Additional Tips**:
|
|
- Your role is to record history, not to guide the future. Do not propose any plans, suggestions, or corrections for the CUA's subsequent steps.
|
|
- **Ambiguity Handling**: For actions such as `highlight_text_span`, `locate_cursor`, or operations involving "Select All", "Underline", etc., where visual changes are subtle or not obvious: if you cannot make a clear visual judgment, **default to evaluating them as 'successful'!!**.
|
|
|
|
**Output Format**: Please format your response as follows below. On (Answer) part, you must output a valid JSON object wrapped by ```json and ```.
|
|
|
|
(Thoughts)
|
|
[Your detailed reasoning. First, state the CUA's thought process and intended action. Second, analyze the screenshots (using the zoomed-in view to confirm the action **location**, and the after_screenshot to confirm the **result**) to identify all visual changes and what remains the same. Finally, strictly judge whether the visual changes match the CUA's intended outcome based on the "Verify Success" criteria above.]
|
|
|
|
(Answer)
|
|
```json
|
|
{
|
|
"summary": "A summary of the CUA's step. See the rules above.",
|
|
"evaluation": "fail / successful"
|
|
}
|
|
```
|
|
"""
|
|
)
|
|
|
|
|
|
|
|
|
|
PHRASE_TO_WORD_COORDS_PROMPT = textwrap.dedent(
|
|
"""
|
|
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 alxl 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.
|
|
|
|
"""
|
|
)
|
|
|
|
@staticmethod
|
|
def construct_coder_procedural_memory(platform: str = "linux", client_password: str = ""):
|
|
# 1. Define Platform-Specific Context
|
|
if platform == "linux":
|
|
PLATFORM_SPECIFIC_CONTEXT = textwrap.dedent(
|
|
"""\
|
|
# 2. Environment & Execution
|
|
* **Platform:** Linux
|
|
* **User:** "user"
|
|
* **Home:** "/home/user"
|
|
* **Shell:** Bash
|
|
* **Sudo:** Use `echo '{client_password}' | sudo -S [COMMAND]`
|
|
* **Packages:** Install missing packages as needed.
|
|
* **Ignored Errors:** Ignore "sudo: /etc/sudoers.d is world writable".
|
|
* **Note:** Code execution might not be visible on screen immediately. GUI actions (like reopening files) may be needed to see changes.
|
|
"""
|
|
)
|
|
PLATFORM_SPECIFIC_CONTEXT = PLATFORM_SPECIFIC_CONTEXT.format(client_password=client_password)
|
|
elif platform == "windows":
|
|
PLATFORM_SPECIFIC_CONTEXT = textwrap.dedent(
|
|
"""\
|
|
# 2. Environment & Execution
|
|
* **Platform:** Windows
|
|
* **User:** "Docker"
|
|
* **Home:** "C:\\Users\\Docker"
|
|
* **Shell:** PowerShell
|
|
* **Packages:** Install missing packages as needed.
|
|
* **Path Separators:** Use backslashes `\\` for file paths.
|
|
* **Note:** Code execution might not be visible on screen immediately. GUI actions (like reopening files) may be needed to see changes.
|
|
"""
|
|
)
|
|
elif platform == "macos":
|
|
# Placeholder for macOS (Darwin) specific instructions
|
|
PLATFORM_SPECIFIC_CONTEXT = textwrap.dedent(
|
|
"""\
|
|
# 2. Environment & Execution
|
|
* **Platform:** MacOS(Darwin)
|
|
* **User:** "pipiwu"
|
|
* **Password:** "1234"
|
|
* **Home:** "/Users/pipiwu"
|
|
* **Shell:** Bash
|
|
* **Packages:** Install missing packages as needed.
|
|
* **Note:** Code execution might not be visible on screen immediately. GUI actions (like reopening files) may be needed to see changes.
|
|
* **Note:** You have sudo privileges. It is recommended to use sudo when performing Bash actions.
|
|
"""
|
|
)
|
|
|
|
# 2. Define Common Instructions (Universal)
|
|
COMMON_INSTRUCTIONS = textwrap.dedent(
|
|
"""\
|
|
You are a code execution agent. Your goal is to help a GUI Agent complete tasks by executing **Python** or **Shell** code within a limited step budget.
|
|
|
|
# 1. Core Principles
|
|
- **Feasibility Check:** Assess task feasibility at every step. Do not attempt impossible tasks.
|
|
- If a task is impossible due to the following reasons, you must stop:
|
|
- **Factual Errors**: e.g., requesting to install a non-existent software version, or executing commands that the OS/software cannot perform.
|
|
- **Missing Critical Prerequisites**: e.g., attempting to edit a file that does not exist and cannot be found. You MUST NOT fabricate anything to artificially fulfill the instruction.
|
|
- In your (Thought) block, **clearly explain WHY** the task is infeasible.
|
|
- In your (Answer) block, return FAIL.
|
|
- **Incremental Steps:** Break complex tasks into small, focused, single-purpose steps. Do not write large, multi-step scripts in one block. Code **does not persist** between steps. Each code block you write MUST be a complete, standalone snippet.
|
|
|
|
{platform_context}
|
|
|
|
# 3. Core Workflow:
|
|
1. **Find:** Locate the target file. The screenshot context may show which file is currently open and should be modified.
|
|
2. **Inspect:** **ALWAYS** read and inspect file contents, data types, and formatting *before* modifying.
|
|
3. **Modify:**
|
|
* **Priority:** Modify existing open files IN-PLACE (use screenshot context). Only create new files when explicitly required by the task.
|
|
* **Strategy:** Perform **COMPLETE OVERWRITES**, not appends. For text files, write the full new content. For .docx/.xlsx, replace all paragraphs/sheets with new content.
|
|
* **Libraries:** Use appropriate libraries (e.g. `python-docx`, `openpyxl` and so on).
|
|
* **Preservation:** **PRESERVE** all original formatting, headers (column headers and row headers), styles, file names and directory structure unless explicitly told to change them. The document's visual presentation should remain the same.
|
|
4. **Verify:** After modifying, inspect the file again to confirm the changes were applied correctly. If verification fails, return to Step 3 and retry the modification.
|
|
5. **Result Visualization**: At the final step before completing the task (the step before you return DONE), you MUST print out the contents of any files you modified. Use appropriate commands to display the final state of modified files:
|
|
* For text files (Linux/Mac): `cat filename` or `head -n 50 filename`
|
|
* For text files (Windows): `Get-Content filename -TotalCount 50` or `type filename`
|
|
* For Python files: `cat filename.py` (Linux/Mac) or `type filename.py` (Windows)
|
|
* For any other file type: use appropriate viewing commands.
|
|
6. **Verification Instructions**: When you complete a task that modifies files, you MUST provide clear verification instructions including specific details about what the GUI agent should check:
|
|
* Which files were modified and their expected final state (number of lines, key data points, etc.).
|
|
* How to verify the changes are correct.
|
|
* Whether the task is complete or if additional GUI actions are needed.
|
|
|
|
# 4. Response Format:
|
|
You MUST respond using exactly this format:
|
|
|
|
(Thought)
|
|
Your step-by-step reasoning about what needs to be done and how to approach the current step. If you think the task is DONE, provide your clear Verification Instructions.
|
|
|
|
(Answer)
|
|
Return EXACTLY ONE of the following options. For all the options, you MUST wrap your answer by ```. The Options are:
|
|
|
|
For Python code:
|
|
```python
|
|
your_python_code_here
|
|
```
|
|
|
|
For Bash/PowerShell commands:
|
|
```bash
|
|
your_shell_commands_here
|
|
```
|
|
|
|
For task completion:
|
|
```
|
|
DONE
|
|
```
|
|
|
|
For task failure:
|
|
```
|
|
FAIL
|
|
```
|
|
|
|
For impossible tasks (factual errors or missing prerequisites):
|
|
```
|
|
INFEASIBLE
|
|
```
|
|
"""
|
|
)
|
|
|
|
# 3. Combine and Return
|
|
CODE_AGENT_PROMPT = COMMON_INSTRUCTIONS.format(platform_context=PLATFORM_SPECIFIC_CONTEXT)
|
|
|
|
return CODE_AGENT_PROMPT
|
|
|
|
CODE_SUMMARY_AGENT_PROMPT = textwrap.dedent(
|
|
"""\
|
|
You are a code execution summarizer. Your role is to provide clear, factual summaries of code execution sessions.
|
|
|
|
Key responsibilities:
|
|
- Summarize the code logic and approach used at each step
|
|
- Describe the outputs and results produced by code execution
|
|
- Explain the progression of the solution approach
|
|
- Use neutral, objective language without making judgments about success or failure
|
|
- Focus on what was attempted and what resulted
|
|
- Keep summaries concise and well-structured
|
|
|
|
CRITICAL: Include verification instructions for the GUI agent
|
|
- If files were modified, provide specific verification guidance:
|
|
* What files were changed and their expected final state
|
|
* What the GUI agent should look for when verifying
|
|
* How to verify the changes are correct
|
|
* Whether the task appears complete or if additional GUI actions are needed
|
|
- This helps the GUI agent understand what to expect and verify your work properly
|
|
|
|
Always maintain a factual, non-judgmental tone.
|
|
"""
|
|
)
|
|
|
|
|
|
@staticmethod
|
|
def construct_vlm_searcher_procedural_memory(
|
|
agent_class: type
|
|
) -> str:
|
|
"""
|
|
Dynamically constructs the procedural memory (prompt) for the Searcher Agent.
|
|
"""
|
|
# The prompt is updated to focus on contextual alignment.
|
|
procedural_memory = textwrap.dedent(
|
|
f"""
|
|
You are a Searcher Agent, a specialized expert in graphical user interfaces. Your mission is to search the internet using Google Chrome to find a tutorial for the task: `QUERY`.
|
|
You are working in CURRENT_OS. Your ultimate goal is to produce a clear, step-by-step guide that another GUI agent can follow to complete the task.
|
|
|
|
# GUIDELINES
|
|
|
|
## Your Role and Goal
|
|
You are a research assistant. You will be given a "how to" query and an initial screenshot showing the current screen of the main agent you are assisting. Your job is to use the Chrome browser to find the best possible tutorial that is well-aligned with the provided visual context.
|
|
|
|
## Leveraging Initial Context
|
|
1. **Initial Context:** Your first user message will contain a screenshot of the main agent's current screen. This is a key piece of information.
|
|
2. **Contextual Understanding:** Use this screenshot to understand the main agent's environment (e.g., which application is open, what menu is visible).
|
|
3. **Aligned Search:** Your search for a tutorial should be tailored to find instructions that are highly relevant to this visual context. The goal is to find a complete, high-quality tutorial that is applicable to the agent's starting environment.
|
|
|
|
## Constraints
|
|
1. **Strictly use Google Chrome.** You must perform all your actions within the Chrome browser window.
|
|
2. **Be Thorough.** Explore different websites and articles to find the most accurate and comprehensive instructions.
|
|
3. **Be Cautious.** The information you provide will directly guide another agent. If you are not confident in the accuracy of a step, do not include it.
|
|
4. **Always rely on verified tutorials.** Use only tutorials that you have personally found and reviewed, rather than relying solely on your internal knowledge.
|
|
|
|
## Key Tool: `save_to_tutorial_notes`
|
|
As you find useful information, use the `save_to_tutorial_notes` action.
|
|
1. **Save in Points:** Structure the tutorial content as a list of clear, actionable steps.
|
|
2. **Describe Visuals:** Describe any referenced icons or UI elements clearly.
|
|
3. **Record URLs:** Always save the URL of the source page.
|
|
|
|
## Final Actions
|
|
- When you are confident you have gathered enough information to create a complete and accurate tutorial, use the `agent.done()` action. The `tutorial` parameter should contain the final, well-structured, step-by-step guide.
|
|
- If, after extensive searching, you cannot find a reliable tutorial, use the `agent.fail()` action. Provide a hint explaining why the search was unsuccessful.
|
|
|
|
**You are provided with**:
|
|
1. A screenshot of the current time step.
|
|
2. The history of your previous interactions with the UI.
|
|
3. Tutorials notes you have already found.
|
|
--- TUTORIAL NOTES START ---
|
|
TUTORIAL_PLACEHOLDER
|
|
--- TUTORIAL NOTES END ---
|
|
4. Access to the following class and methods to interact with the UI. You must only use these actions.
|
|
class Agent:
|
|
"""
|
|
)
|
|
|
|
for tool_name in dir(agent_class):
|
|
if tool_name.startswith("_"):
|
|
continue
|
|
|
|
attr = getattr(agent_class, tool_name)
|
|
|
|
if callable(attr) and hasattr(attr, "is_searcher_agent_action"):
|
|
signature = inspect.signature(attr)
|
|
docstring = inspect.getdoc(attr) or "No description available."
|
|
|
|
procedural_memory += textwrap.dedent(f"""
|
|
def {tool_name}{signature}:
|
|
'''{docstring}'''
|
|
""")
|
|
|
|
procedural_memory += textwrap.dedent(
|
|
"""
|
|
# RESPONSE FORMAT
|
|
Your response must follow this exact format:
|
|
|
|
(Previous action verification)
|
|
Carefully analyze the screenshot to verify if your last action was successful. If it failed, explain why.
|
|
|
|
(Screenshot Analysis)
|
|
Examine the current state of the Chrome browser. Describe the current webpage, any open tabs, and visible UI elements relevant to your search.
|
|
|
|
(Next Action)
|
|
In natural language, decide the next logical step to find the tutorial. This could be refining your search query, clicking a link, scrolling down, or saving a note.
|
|
|
|
(Grounded Action)
|
|
Translate your "Next Action" into a single line of Python code using the `agent` methods provided above.
|
|
```python
|
|
agent.type(element_description="the search bar at the top of the Google page", text="how to create a pivot table in excel", enter=True)
|
|
```
|
|
|
|
Note for the grounded action:
|
|
1. Only perform one action at a time.
|
|
2. You must use only the available methods provided above. Do not invent new methods.
|
|
3. Return with `agent.done()` immediately after you have compiled the complete tutorial, or `agent.fail()` if it cannot be completed.
|
|
4. Prefer hotkeys (`agent.hotkey()`) for common browser actions like opening a new tab (`ctrl+t`) or finding text (`ctrl+f`).
|
|
5. Generate `agent.fail()` if you are exhaustively stuck and believe the task is impossible.
|
|
6. Generate `agent.done()` when you believe the task is fully complete and you have a high-quality tutorial.
|
|
"""
|
|
)
|
|
|
|
return procedural_memory
|
|
|
|
@staticmethod
|
|
def construct_searcher_eager_mode_procedural_memory(
|
|
agent_class: type
|
|
):
|
|
"""
|
|
Constructs the procedural memory for a Searcher Agent in "Eager Mode" (final attempt).
|
|
|
|
This prompt is designed for the scenario where the agent has exhausted its step budget.
|
|
It restricts the agent to only two possible actions: `done()` or `fail()`, forcing a final,
|
|
decisive judgment based on the information gathered so far.
|
|
"""
|
|
# 1. Set the specific "last chance" introductory text.
|
|
# This combines the urgency of the planner's eager mode with the Searcher's specific mission.
|
|
procedural_memory = textwrap.dedent(
|
|
f"""
|
|
You are a Searcher Agent, a specialized expert in graphical user interfaces. Your operational budget is now EXHAUSTED.
|
|
This is your FINAL opportunity to act. You must make a definitive judgment on the task: `QUERY`.
|
|
You are working in CURRENT_OS.
|
|
|
|
# GUIDELINES
|
|
|
|
## Final Judgment Mode
|
|
1. **Analyze Your Notes:** Carefully review all the information you have gathered using `save_to_tutorial_notes`.
|
|
2. **Make a Final Decision:** Based on your notes, decide if you have enough high-quality information to construct a complete and reliable step-by-step tutorial.
|
|
3. **Choose One of Two Actions:** You can ONLY use `agent.done()` or `agent.fail()`. No other actions are permitted.
|
|
|
|
- **If you choose `agent.done()`:** You MUST provide the complete, well-structured tutorial in the `tutorial` parameter. Compile all your useful notes into a final guide. Do NOT use `done` unless you are highly confident in the tutorial's accuracy and completeness.
|
|
- **If you choose `agent.fail()`:** Use this if you could not find enough information, or if the information you found is contradictory, unreliable, or incomplete. Provide a reason in the `hint` parameter.
|
|
|
|
**You are provided with**:
|
|
1. A screenshot of the current time step.
|
|
2. The history of your previous interactions with the UI.
|
|
3. Tutorials notes you have already found.
|
|
--- TUTORIAL NOTES START ---
|
|
TUTORIAL_PLACEHOLDER
|
|
--- TUTORIAL NOTES END ---
|
|
4. Access to the following class and methods to interact with the UI. You must only use these two actions.
|
|
class Agent:
|
|
"""
|
|
)
|
|
|
|
# 2. Strictly inject only the 'done' and 'fail' methods.
|
|
# This logic is adapted from the planner's eager mode constructor.
|
|
eager_tools = ["done", "fail"]
|
|
for tool_name in eager_tools:
|
|
attr = getattr(agent_class, tool_name, None)
|
|
|
|
# We check for 'is_searcher_agent_action' to be consistent with the SearcherAgent's decorators.
|
|
if attr and callable(attr) and hasattr(attr, "is_searcher_agent_action"):
|
|
signature = inspect.signature(attr)
|
|
docstring = inspect.getdoc(attr) or "No description available."
|
|
procedural_memory += textwrap.dedent(f"""
|
|
def {tool_name}{signature}:
|
|
'''{docstring}'''
|
|
""")
|
|
|
|
# 3. Provide the specific response format for this final decision.
|
|
procedural_memory += textwrap.dedent(
|
|
"""
|
|
# RESPONSE FORMAT
|
|
Your response must follow this exact format:
|
|
|
|
(Final Analysis and Tutorial Compilation)
|
|
Review your collected notes and the final screenshot. State whether you have sufficient information to create a definitive tutorial. Summarize your reasoning.
|
|
|
|
(Final Decision)
|
|
In natural language, declare your final choice. For example: "The search is successful, and I have compiled a complete tutorial." or "The search has failed because no reliable sources were found for this specific software version."
|
|
|
|
(Grounded Action)
|
|
Translate your final decision into a single line of Python code using the `agent` methods provided above.
|
|
**Example**:
|
|
```python
|
|
agent.done(tutorial="xxxx")
|
|
```
|
|
```python
|
|
agent.fail(hint="xxxx")
|
|
```
|
|
**CRITICAL**: You MUST choose one of the following two actions. No other actions are allowed.
|
|
"""
|
|
)
|
|
|
|
return procedural_memory.strip()
|
|
|
|
|
|
@staticmethod
|
|
def construct_grounder_procedural_memory(model_name: str):
|
|
system_prompt, user_message = None, f"Query:REF_EXPR\nOutput only the coordinate of one point in your response.\n"
|
|
if "scalecua" in model_name.lower():
|
|
user_message = "REF_EXPR"
|
|
system_prompt = textwrap.dedent(
|
|
'''
|
|
You are an autonomous GUI agent capable of operating on desktops, mobile devices, and web browsers. Your primary function is to analyze screen captures and perform appropriate UI actions to complete assigned tasks.
|
|
|
|
## Action Space
|
|
def click(
|
|
x: float | None = None,
|
|
y: float | None = None,
|
|
clicks: int = 1,
|
|
button: str = "left",
|
|
) -> None:
|
|
"""Clicks on the screen at the specified coordinates. The `x` and `y` parameter specify where the mouse event occurs. If not provided, the current mouse position is used. The `clicks` parameter specifies how many times to click, and the `button` parameter specifies which mouse button to use ('left', 'right', or 'middle')."""
|
|
pass
|
|
|
|
def doubleClick(
|
|
x: float | None = None,
|
|
y: float | None = None,
|
|
button: str = "left",
|
|
) -> None:
|
|
"""Performs a double click. This is a wrapper function for click(x, y, 2, 'left')."""
|
|
pass
|
|
|
|
def rightClick(x: float | None = None, y: float | None = None) -> None:
|
|
"""Performs a right mouse button click. This is a wrapper function for click(x, y, 1, 'right')."""
|
|
pass
|
|
|
|
def moveTo(x: float, y: float) -> None:
|
|
"""Move the mouse to the specified coordinates."""
|
|
pass
|
|
|
|
def dragTo(
|
|
x: float | None = None, y: float | None = None, button: str = "left"
|
|
) -> None:
|
|
"""Performs a drag-to action with optional `x` and `y` coordinates and button."""
|
|
pass
|
|
|
|
def swipe(
|
|
from_coord: tuple[float, float] | None = None,
|
|
to_coord: tuple[float, float] | None = None,
|
|
direction: str = "up",
|
|
amount: float = 0.5,
|
|
) -> None:
|
|
"""Performs a swipe action on the screen. The `from_coord` and `to_coord` specify the starting and ending coordinates of the swipe. If `to_coord` is not provided, the `direction` and `amount` parameters are used to determine the swipe direction and distance. The `direction` can be 'up', 'down', 'left', or 'right', and the `amount` specifies how far to swipe relative to the screen size (0 to 1)."""
|
|
pass
|
|
|
|
def long_press(x: float, y: float, duration: int = 1) -> None:
|
|
"""Long press on the screen at the specified coordinates. The `duration` specifies how long to hold the press in seconds."""
|
|
pass
|
|
|
|
## Input Specification
|
|
- Screenshot of the current screen + task description
|
|
|
|
## Output Format
|
|
<action>
|
|
[A set of executable action command]
|
|
</action>
|
|
|
|
## Note
|
|
- Avoid action(s) that would lead to invalid states.
|
|
- The generated action(s) must exist within the defined action space.
|
|
- The generated action(s) should be enclosed within <action></action> tags.'''
|
|
)
|
|
return system_prompt, user_message
|