letta-server/memgpt/autogen/examples/agent_groupchat.py

"""Example of how to add MemGPT into an AutoGen groupchat

Based on the official AutoGen example here: https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb

Begin by doing:
  pip install "pyautogen[teachable]"
  pip install pymemgpt
  or
  pip install -e . (inside the MemGPT home directory)
"""


import os
import autogen
from memgpt.autogen.memgpt_agent import create_autogen_memgpt_agent, create_memgpt_autogen_agent_from_config

# This config is for autogen agents that are not powered by MemGPT
config_list = [
    {
        "model": "gpt-4",
        "api_key": os.getenv("OPENAI_API_KEY"),
    }
]

# This config is for autogen agents that powered by MemGPT
config_list_memgpt = [
    {
        "model": "gpt-4",
    },
]

# Uncomment and fill in the following for local LLM deployment:
# # This config is for autogen agents that are not powered by MemGPT
# # See https://github.com/oobabooga/text-generation-webui/tree/main/extensions/openai
config_list = [
    {
        "model": "YOUR_MODEL",  # ex. This is the model name, not the wrapper
        "api_base": "YOUR_URL",  # ex. "http://127.0.0.1:5001/v1" if you are using webui, "http://localhost:1234/v1/" if you are using LM Studio
        "api_key": "NULL",  # this is a placeholder
        "api_type": "open_ai",
    },
]

# # This config is for autogen agents that powered by MemGPT
# # For this to work, you need to have your environment variables set correctly, e.g.
# # For web UI:
# #   OPENAI_API_BASE=http://127.0.0.1:5000
# #   BACKEND_TYPE=webui
# # For LM Studio:
# #   OPENAI_API_BASE=http://127.0.0.1:1234
# #   BACKEND_TYPE=lmstudio
# # "model" here specifies the "wrapper" that will be used, setting it to "gpt-4" uses the default
config_list_memgpt = [
    {"model": "airoboros-l2-70b-2.1"},  # if you set this to gpt-4, it will fall back to the default wrapper
]


# If USE_MEMGPT is False, then this example will be the same as the official AutoGen repo
# (https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb)
# If USE_MEMGPT is True, then we swap out the "coder" agent with a MemGPT agent
USE_MEMGPT = True

USE_AUTOGEN_WORKFLOW = True

# Set to True if you want to print MemGPT's inner workings.
DEBUG = False

interface_kwargs = {
    "debug": DEBUG,
    "show_inner_thoughts": DEBUG,
    "show_function_outputs": DEBUG,
}

llm_config = {"config_list": config_list, "seed": 42}
llm_config_memgpt = {"config_list": config_list_memgpt, "seed": 42}

# The user agent
user_proxy = autogen.UserProxyAgent(
    name="User_proxy",
    system_message="A human admin.",
    code_execution_config={"last_n_messages": 2, "work_dir": "groupchat"},
    human_input_mode="TERMINATE",  # needed?
    default_auto_reply="...",  # Set a default auto-reply message here (non-empty auto-reply is required for LM Studio)
)

# The agent playing the role of the product manager (PM)
pm = autogen.AssistantAgent(
    name="Product_manager",
    system_message="Creative in software product ideas.",
    llm_config=llm_config,
    default_auto_reply="...",  # Set a default auto-reply message here (non-empty auto-reply is required for LM Studio)
)

if not USE_MEMGPT:
    # In the AutoGen example, we create an AssistantAgent to play the role of the coder
    coder = autogen.AssistantAgent(
        name="Coder",
        llm_config=llm_config,
    )

else:
    # In our example, we swap this AutoGen agent with a MemGPT agent
    # This MemGPT agent will have all the benefits of MemGPT, ie persistent memory, etc.
    if not USE_AUTOGEN_WORKFLOW:
        coder = create_autogen_memgpt_agent(
            "MemGPT_coder",
            persona_description="I am a 10x engineer, trained in Python. I was the first engineer at Uber "
            "(which I make sure to tell everyone I work with).",
            user_description=f"You are participating in a group chat with a user ({user_proxy.name}) "
            f"and a product manager ({pm.name}).",
            model=config_list_memgpt[0]["model"],
            interface_kwargs=interface_kwargs,
        )
    else:
        coder = create_memgpt_autogen_agent_from_config(
            "MemGPT_coder",
            llm_config=llm_config_memgpt,
            system_message=f"I am a 10x engineer, trained in Python. I was the first engineer at Uber "
            f"(which I make sure to tell everyone I work with).\n"
            f"You are participating in a group chat with a user ({user_proxy.name}) "
            f"and a product manager ({pm.name}).",
            interface_kwargs=interface_kwargs,
        )

# Initialize the group chat between the user and two LLM agents (PM and coder)
groupchat = autogen.GroupChat(agents=[user_proxy, pm, coder], messages=[], max_round=12)
manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)

# Begin the group chat with a message from the user
user_proxy.initiate_chat(
    manager,
    message="I want to design an app to make me one million dollars in one month. " "Yes, your heard that right.",
)