CoACT initialize (#292)
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Linxin Song
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mm_agents/coact/autogen/agentchat/user_proxy_agent.py
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mm_agents/coact/autogen/agentchat/user_proxy_agent.py
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# Copyright (c) 2023 - 2025, AG2ai, Inc., AG2ai open-source projects maintainers and core contributors
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#
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# SPDX-License-Identifier: Apache-2.0
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#
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# Portions derived from https://github.com/microsoft/autogen are under the MIT License.
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# SPDX-License-Identifier: MIT
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from typing import Any, Callable, Literal, Optional, Union
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from ..doc_utils import export_module
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from ..llm_config import LLMConfig
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from ..runtime_logging import log_new_agent, logging_enabled
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from .conversable_agent import ConversableAgent
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@export_module("autogen")
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class UserProxyAgent(ConversableAgent):
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"""(In preview) A proxy agent for the user, that can execute code and provide feedback to the other agents.
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UserProxyAgent is a subclass of ConversableAgent configured with `human_input_mode` to ALWAYS
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and `llm_config` to False. By default, the agent will prompt for human input every time a message is received.
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Code execution is enabled by default. LLM-based auto reply is disabled by default.
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To modify auto reply, register a method with [`register_reply`](../ConversableAgent#register-reply).
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To modify the way to get human input, override `get_human_input` method.
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To modify the way to execute code blocks, single code block, or function call, override `execute_code_blocks`,
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`run_code`, and `execute_function` methods respectively.
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"""
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# Default UserProxyAgent.description values, based on human_input_mode
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DEFAULT_USER_PROXY_AGENT_DESCRIPTIONS = {
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"ALWAYS": "An attentive HUMAN user who can answer questions about the task, and can perform tasks such as running Python code or inputting command line commands at a Linux terminal and reporting back the execution results.",
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"TERMINATE": "A user that can run Python code or input command line commands at a Linux terminal and report back the execution results.",
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"NEVER": "A computer terminal that performs no other action than running Python scripts (provided to it quoted in ```python code blocks), or sh shell scripts (provided to it quoted in ```sh code blocks).",
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}
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def __init__(
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self,
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name: str,
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is_termination_msg: Optional[Callable[[dict[str, Any]], bool]] = None,
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max_consecutive_auto_reply: Optional[int] = None,
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human_input_mode: Literal["ALWAYS", "TERMINATE", "NEVER"] = "ALWAYS",
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function_map: Optional[dict[str, Callable[..., Any]]] = None,
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code_execution_config: Union[dict[str, Any], Literal[False]] = {},
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default_auto_reply: Optional[Union[str, dict[str, Any]]] = "",
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llm_config: Optional[Union[LLMConfig, dict[str, Any], Literal[False]]] = False,
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system_message: Optional[Union[str, list[str]]] = "",
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description: Optional[str] = None,
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**kwargs: Any,
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):
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"""Args:
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name (str): name of the agent.
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is_termination_msg (function): a function that takes a message in the form of a dictionary
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and returns a boolean value indicating if this received message is a termination message.
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The dict can contain the following keys: "content", "role", "name", "function_call".
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max_consecutive_auto_reply (int): the maximum number of consecutive auto replies.
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default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case).
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The limit only plays a role when human_input_mode is not "ALWAYS".
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human_input_mode (str): whether to ask for human inputs every time a message is received.
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Possible values are "ALWAYS", "TERMINATE", "NEVER".
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(1) When "ALWAYS", the agent prompts for human input every time a message is received.
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Under this mode, the conversation stops when the human input is "exit",
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or when is_termination_msg is True and there is no human input.
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(2) When "TERMINATE", the agent only prompts for human input only when a termination message is received or
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the number of auto reply reaches the max_consecutive_auto_reply.
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(3) When "NEVER", the agent will never prompt for human input. Under this mode, the conversation stops
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when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True.
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function_map (dict[str, callable]): Mapping function names (passed to openai) to callable functions.
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code_execution_config (dict or False): config for the code execution.
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To disable code execution, set to False. Otherwise, set to a dictionary with the following keys:
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- work_dir (Optional, str): The working directory for the code execution.
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If None, a default working directory will be used.
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The default working directory is the "extensions" directory under
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"path_to_autogen".
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- use_docker (Optional, list, str or bool): The docker image to use for code execution.
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Default is True, which means the code will be executed in a docker container. A default list of images will be used.
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If a list or a str of image name(s) is provided, the code will be executed in a docker container
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with the first image successfully pulled.
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If False, the code will be executed in the current environment.
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We strongly recommend using docker for code execution.
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- timeout (Optional, int): The maximum execution time in seconds.
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- last_n_messages (Experimental, Optional, int): The number of messages to look back for code execution. Default to 1.
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default_auto_reply (str or dict or None): the default auto reply message when no code execution or llm based reply is generated.
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llm_config (LLMConfig or dict or False or None): llm inference configuration.
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Please refer to [OpenAIWrapper.create](https://docs.ag2.ai/latest/docs/api-reference/autogen/OpenAIWrapper/#autogen.OpenAIWrapper.create)
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for available options.
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Default to False, which disables llm-based auto reply.
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When set to None, will use self.DEFAULT_CONFIG, which defaults to False.
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system_message (str or List): system message for ChatCompletion inference.
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Only used when llm_config is not False. Use it to reprogram the agent.
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description (str): a short description of the agent. This description is used by other agents
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(e.g. the GroupChatManager) to decide when to call upon this agent. (Default: system_message)
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**kwargs (dict): Please refer to other kwargs in
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[ConversableAgent](https://docs.ag2.ai/latest/docs/api-reference/autogen/ConversableAgent).
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"""
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super().__init__(
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name=name,
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system_message=system_message,
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is_termination_msg=is_termination_msg,
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max_consecutive_auto_reply=max_consecutive_auto_reply,
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human_input_mode=human_input_mode,
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function_map=function_map,
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code_execution_config=code_execution_config,
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llm_config=llm_config,
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default_auto_reply=default_auto_reply,
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description=(
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description if description is not None else self.DEFAULT_USER_PROXY_AGENT_DESCRIPTIONS[human_input_mode]
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),
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**kwargs,
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)
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if logging_enabled():
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log_new_agent(self, locals())
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