Auto Generated Agent Chat: Hierarchy flow using select_speaker¶

import random
from typing import Dict, List

import autogen
from autogen.agentchat.agent import Agent
from autogen.agentchat.assistant_agent import AssistantAgent
from autogen.agentchat.groupchat import GroupChat

print(autogen.__version__)

# The default config list in notebook.
config_list_gpt4 = autogen.config_list_from_json(
    "OAI_CONFIG_LIST",
    filter_dict={
        "model": ["gpt-4", "gpt4", "gpt-4-32k", "gpt-4-32k-0314", "gpt-4-32k-v0314"],
    },
)

# Contributor's config - Please replace with your own, I have replaced mine with an Azure OpenAI endpoint.
config_list_gpt4 = autogen.config_list_from_json(
    "OAI_CONFIG_LIST",
    filter_dict={
        "model": ["gpt-4"],
    },
)

# Initialization
agents_A = [
    AssistantAgent(
        name="A1",
        system_message="You are a team leader A1, your team consists of A2, A3. You can talk to the other team leader B1, whose team member is B2.",
        llm_config=llm_config,
    ),
    AssistantAgent(
        name="A2",
        system_message="You are team member A2, you know the secret value of x but not y, x = 9. Tell others x to cooperate.",
        llm_config=llm_config,
    ),
    AssistantAgent(
        name="A3",
        system_message="You are team member A3, You know the secret value of y but not x, y = 5. Tell others y to cooperate.",
        llm_config=llm_config,
    ),
]

agents_B = [
    AssistantAgent(
        name="B1",
        system_message="You are a team leader B1, your team consists of B2. You can talk to the other team leader A1, whose team member is A2, A3. Use NEXT: A1 to suggest talking to A1.",
        llm_config=llm_config,
    ),
    AssistantAgent(
        name="B2",
        system_message="You are team member B2. Your task is to find out the value of x and y and compute the product. Once you have the answer, say out the answer and append a new line with TERMINATE.",
        llm_config=llm_config,
    ),
]

# Terminates the conversation when TERMINATE is detected.
user_proxy = autogen.UserProxyAgent(
    name="User_proxy",
    system_message="Terminator admin.",
    code_execution_config=False,
    is_termination_msg=is_termination_msg,
    human_input_mode="NEVER",
)

list_of_agents = agents_A + agents_B
list_of_agents.append(user_proxy)

# Create CustomGroupChat
group_chat = CustomGroupChat(
    agents=list_of_agents,  # Include all agents
    messages=[
        'Everyone cooperate and help agent B2 in his task. Team A has A1, A2, A3. Team B has B1, B2. Only members of the same team can talk to one another. Only team leaders (names ending with 1) can talk amongst themselves. You must use "NEXT: B1" to suggest talking to B1 for example; You can suggest only one person, you cannot suggest yourself or the previous speaker; You can also dont suggest anyone.'
    ],
    max_round=30,
)


# Create the manager
llm_config = {
    "config_list": config_list_gpt4,
    "cache_seed": None,
}  # cache_seed is None because we want to observe if there is any communication pattern difference if we reran the group chat.
manager = autogen.GroupChatManager(groupchat=group_chat, llm_config=llm_config)


# Initiates the chat with B2
agents_B[1].initiate_chat(manager, message="Find the product of x and y, the other agents know x and y.")