1b.py

import  auto_coder as autogen
from auto_coder import apc
from pprint import pprint as pp
apc.verbose = True
apc.show_create=False
apc.show_code=False
apc.show_reply=False
apc.depth=0
apc.call_id=0
apc.tree={'calling':{ 'name': 'root','calling':{}, 'depth'  : 0}}

llm_config={"model": "gpt-4o-mini"}



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="ALWAYS",
)


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=10,
)

manager = autogen.GroupChatManager(name='manager',
    groupchat=groupchat, llm_config=llm_config
)

task = "Write a blogpost about the stock price performance of "\
"Nvidia in the past month. Today's date is 2024-04-23."

   
if 1:
    try:
        max_turns = None #non-group chat
        groupchat_result = user_proxy.initiate_chat(
            recipient=manager,
            messages=task,
            max_turns=max_turns,    
        )

    except Exception as e:
        print('In except:')
        raise
    finally:
        #pp(apc.tree)
        for cid, call in apc.tree['calling']['calling'].items():
            print( '  '*call['depth'], call['name'])  
else: 
    for cid, call in apc.tree['calling']['calling'].items():
        print( '\t'*call['depth'], call['name'])