autogen/python/samples/agentchat_chainlit

• Building a Multi-Agent Application with AutoGen and Chainlit
• • High-Level Description
  • Quickstart
  • • Function Definitions
  • Adding a UserProxyAgent
  • Next Steps (Extra Credit)

Building a Multi-Agent Application with AutoGen and Chainlit

In this sample, we will build a simple chat interface that interacts with a RoundRobinGroupChat team built using the AutoGen AgentChat api.

.

High-Level Description

The app.py script sets up a Chainlit chat interface that communicates with the AutoGen team. When a chat starts, it

• Initializes an AgentChat team.

async def get_weather(city: str) -> str:
    return f"The weather in {city} is 73 degrees and Sunny."

assistant_agent = AssistantAgent(
    name="assistant_agent",
    tools=[get_weather],
    model_client=OpenAIChatCompletionClient(
        model="gpt-4o-2024-08-06"))


termination = TextMentionTermination("TERMINATE") | MaxMessageTermination(10)
team = RoundRobinGroupChat(
    participants=[assistant_agent], termination_condition=termination)

• As users interact with the chat, their queries are sent to the team which responds.
• As agents respond/act, their responses are streamed back to the chat interface.

Quickstart

To get started, ensure you have setup an API Key. We will be using the OpenAI API for this example.

1. Ensure you have an OPENAPI API key. Set this key in your environment variables as OPENAI_API_KEY.

2. Install the required Python packages by running:

pip install -r requirements.txt

3. Run the app.py script to start the Chainlit server.

chainlit run app.py -h

4. Interact with the Agent Team Chainlit interface. The chat interface will be available at http://localhost:8000 by default.

Function Definitions

• start_chat: Initializes the chat session
• run_team: Sends the user's query to the team streams the agent responses back to the chat interface.
• chat: Receives messages from the user and passes them to the run_team function.

Adding a UserProxyAgent

We can add a UserProxyAgent to the team so that the user can interact with the team directly with the input box in the chat interface. This requires defining a function for input that uses the Chainlit input box instead of the terminal.

from typing import Optional
from autogen_core import CancellationToken
from autogen_agentchat.agents import AssistantAgent, UserProxyAgent
from autogen_agentchat.conditions import TextMentionTermination, MaxMessageTermination
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_ext.models.openai import OpenAIChatCompletionClient

async def chainlit_input_func(prompt: str, cancellation_token: Optional[CancellationToken] = None) -> str:
    try:
        response = await cl.AskUserMessage(
            content=prompt,
            author="System",
        ).send()
        return response["output"]

    except Exception as e:
        raise RuntimeError(f"Failed to get user input: {str(e)}") from e

user_proxy_agent = UserProxyAgent(
    name="user_proxy_agent",
    input_func=chainlit_input_func,
)
assistant_agent = AssistantAgent(
    name="assistant_agent",
    model_client=OpenAIChatCompletionClient(
        model="gpt-4o-2024-08-06"))

termination = TextMentionTermination("TERMINATE") | MaxMessageTermination(10)

team = RoundRobinGroupChat(
    participants=[user_proxy_agent, assistant_agent],
    termination_condition=termination)

Next Steps (Extra Credit)

In this example, we created a basic AutoGen team with a single agent in a RoundRobinGroupChat team. There are a few ways you can extend this example:

• Add more agents to the team.
• Explor custom agents that sent multimodal messages
• Explore more team types beyond the RoundRobinGroupChat.