multi_agent.py

import autogen

def run_multi_agent(llm, task):
    llm_config = {"model": llm}
    
    user_proxy = autogen.ConversableAgent(
        name="Admin",
        system_message="Give the task, and send "
        "instructions to writer to refine the blog post.",
        code_execution_config=False,
        llm_config=llm_config,
        human_input_mode="NEVER",
    )
    
    planner = autogen.ConversableAgent(
        name="Planner",
        system_message="Given a task, please determine "
        "what information is needed to complete the task. "
        "Please note that the information will all be retrieved using"
        " Python code. Please only suggest information that can be "
        "retrieved using Python code. "
        "After each step is done by others, check the progress and "
        "instruct the remaining steps. If a step fails, try to "
        "workaround",
        description="Planner. Given a task, determine what "
        "information is needed to complete the task. "
        "After each step is done by others, check the progress and "
        "instruct the remaining steps",
        llm_config=llm_config,
    )
    
    engineer = autogen.AssistantAgent(
        name="Engineer",
        llm_config=llm_config,
        description="An engineer that writes code based on the plan "
        "provided by the planner.",
    )
    
    executor = autogen.ConversableAgent(
        name="Executor",
        system_message="Execute the code written by the "
        "engineer and report the result.",
        human_input_mode="NEVER",
        code_execution_config={
            "last_n_messages": 3,
            "work_dir": "coding",
            "use_docker": False,
        },
    )
    
    writer = autogen.ConversableAgent(
        name="Writer",
        llm_config=llm_config,
        system_message="Writer."
        "Please write blogs in markdown format (with relevant titles)"
        " and put the content in pseudo ```md``` code block. "
        "You take feedback from the admin and refine your blog.",
        description="Writer."
        "Write blogs based on the code execution results and take "
        "feedback from the admin to refine the blog."
    )
    
    groupchat = autogen.GroupChat(
        agents=[user_proxy, engineer, writer, executor, planner],
        messages=[],
        max_round=25,
    )
    
    manager = autogen.GroupChatManager(
        groupchat=groupchat, llm_config=llm_config
    )
    
    groupchat_result = user_proxy.initiate_chat(
        manager,
        message=task,
    )
    
    return groupchat_result