Auto Generated Agent Chat: Group Chat with GPTAssistantAgent

import autogen
from autogen.agentchat import AssistantAgent
from autogen.agentchat.contrib.gpt_assistant_agent import GPTAssistantAgent

config_list_gpt4 = autogen.config_list_from_json(
    "OAI_CONFIG_LIST",
    filter_dict={
        "model": ["gpt-4", "gpt-4-1106-preview", "gpt-4-32k"],
    },
)

# Define user proxy agent
llm_config = {"config_list": config_list_gpt4, "cache_seed": 45}
user_proxy = autogen.UserProxyAgent(
    name="User_proxy",
    system_message="A human admin.",
    code_execution_config={
        "last_n_messages": 2,
        "work_dir": "groupchat",
        "use_docker": False,
    },  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.
    human_input_mode="TERMINATE",
)

# define two GPTAssistants
coder = GPTAssistantAgent(
    name="Coder",
    llm_config={
        "config_list": config_list_gpt4,
    },
    instructions=AssistantAgent.DEFAULT_SYSTEM_MESSAGE,
)

analyst = GPTAssistantAgent(
    name="Data_analyst",
    instructions="You are a data analyst that offers insight into data.",
    llm_config={
        "config_list": config_list_gpt4,
    },
)
# define group chat
groupchat = autogen.GroupChat(agents=[user_proxy, coder, analyst], messages=[], max_round=10)
manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)

user_proxy.initiate_chat(
    manager,
    message="Get the number of issues and pull requests for the repository 'ag2ai/ag2' over the past three weeks and offer analysis to the data. You should print the data in csv format grouped by weeks.",
)
# type exit to terminate the chat