Update multi_agent.py
Browse files- multi_agent.py +67 -102
multi_agent.py
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@@ -63,116 +63,81 @@ def initialize():
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board_svgs = []
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made_move = False
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def run_multi_agent(
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initialize()
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llm_config_black = {"model": llm_black}
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name="
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player_white = ConversableAgent(
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name="Player White",
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system_message="You are a chess Grandmaster and you play as white. "
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"First call get_legal_moves(), to get a list of legal moves. "
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"Then call make_move(move) to make a legal move. "
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"Analyze the move in 3 bullet points. Respond in format **Analysis:** move in UCI format, unordered list.",
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llm_config=llm_config_white,
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)
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name="
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system_message="
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"
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"
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description="Call this tool to get legal moves.",
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)
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register_function(
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make_move,
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caller=caller,
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executor=board_proxy,
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name="make_move",
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description="Call this tool to make a move.",
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)
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player_white.register_nested_chats(
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trigger=player_black,
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chat_queue=[
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{
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"sender": board_proxy,
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"recipient": player_white,
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"summary_method": "last_msg",
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"silent": False,
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}
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],
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)
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"recipient": player_black,
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"summary_method": "last_msg",
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"silent": False,
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}
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],
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)
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print(f"Error: {e}")
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finally:
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if chat_result != None:
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chat_history = chat_result.chat_history
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result = ""
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num_move = 0
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return
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board_svgs = []
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made_move = False
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def run_multi_agent(llm, task):
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#initialize()
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llm_config = {"model": llm}
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user_proxy = autogen.ConversableAgent(
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name="Admin",
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system_message="Give the task, and send "
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"instructions to writer to refine the blog post.",
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code_execution_config=False,
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llm_config=llm_config,
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human_input_mode="ALWAYS",
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)
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planner = autogen.ConversableAgent(
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name="Planner",
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system_message="Given a task, please determine "
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"what information is needed to complete the task. "
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"Please note that the information will all be retrieved using"
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" Python code. Please only suggest information that can be "
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"retrieved using Python code. "
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"After each step is done by others, check the progress and "
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"instruct the remaining steps. If a step fails, try to "
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"workaround",
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description="Planner. Given a task, determine what "
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"information is needed to complete the task. "
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"After each step is done by others, check the progress and "
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"instruct the remaining steps",
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llm_config=llm_config,
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)
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engineer = autogen.AssistantAgent(
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name="Engineer",
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llm_config=llm_config,
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description="An engineer that writes code based on the plan "
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"provided by the planner.",
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executor = autogen.ConversableAgent(
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name="Executor",
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system_message="Execute the code written by the "
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"engineer and report the result.",
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human_input_mode="NEVER",
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code_execution_config={
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"last_n_messages": 3,
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"work_dir": "coding",
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"use_docker": False,
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},
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)
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writer = autogen.ConversableAgent(
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name="Writer",
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llm_config=llm_config,
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system_message="Writer."
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"Please write blogs in markdown format (with relevant titles)"
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" and put the content in pseudo ```md``` code block. "
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"You take feedback from the admin and refine your blog.",
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description="Writer."
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"Write blogs based on the code execution results and take "
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"feedback from the admin to refine the blog."
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)
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groupchat = autogen.GroupChat(
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agents=[user_proxy, engineer, writer, executor, planner],
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messages=[],
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max_round=10,
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)
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manager = autogen.GroupChatManager(
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groupchat=groupchat, llm_config=llm_config
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)
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groupchat_result = user_proxy.initiate_chat(
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manager,
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message=task,
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)
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return groupchat_result
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