autogen/python/samples/core_chainlit

• Core ChainLit Integration Sample
• • Overview
  • Requirements
  • Installation
  • Model Configuration
  • Running the Agent Sample
  • Running the Team Sample

Core ChainLit Integration Sample

In this sample, we will demonstrate how to build simple chat interface that
interacts with a Core
agent or a team, using Chainlit,
and support streaming messages.

Overview

The core_chainlit sample is designed to illustrate a simple use case of ChainLit integrated with a single-threaded agent runtime. It includes the following components:

• Single Agent: A single agent that operates within the ChainLit environment.
• Group Chat: A group chat setup featuring two agents:
  • Assistant Agent: This agent responds to user inputs.
  • Critic Agent: This agent reflects on and critiques the responses from the Assistant Agent.
• Closure Agent: Utilizes a closure agent to aggregate output messages into an output queue.
• Token Streaming: Demonstrates how to stream tokens to the user interface.
• Session Management: Manages the runtime and output queue within the ChainLit user session.

Requirements

To run this sample, you will need:

• Python 3.8 or higher
• Installation of necessary Python packages as listed in requirements.txt

Installation

To run this sample, you will need to install the following packages:

pip install -U chainlit autogen-core autogen-ext[openai] pyyaml

To use other model providers, you will need to install a different extra
for the autogen-ext package.
See the Models documentation for more information.

Model Configuration

Create a configuration file named model_config.yaml to configure the model
you want to use. Use model_config_template.yaml as a template.

Running the Agent Sample

The first sample demonstrate how to interact with a single AssistantAgent
from the chat interface.
Note: cd to the sample directory.

chainlit run app_agent.py

Running the Team Sample

The second sample demonstrate how to interact with a team of agents from the
chat interface.

chainlit run app_team.py -h

There are two agents in the team: one is instructed to be generally helpful
and the other one is instructed to be a critic and provide feedback.