Chat Chainlit App with Multi-Agent Orchestrator

This example demonstrates how to build a chat application using Chainlit and the Multi-Agent Orchestrator. It showcases a system with three specialized agents (Tech, Travel, and Health) working together through a streaming-enabled chat interface.

Key Features

• Streaming responses using Chainlit’s real-time capabilities
• Integration with multiple agent types (Bedrock and Ollama)
• Custom classifier configuration using Claude 3 Haiku
• Session management for user interactions
• Complete chat history handling

Quick Start

# Clone the repository
git clone https://github.com/awslabs/multi-agent-orchestrator.git
cd multi-agent-orchestrator/examples/chat-chainlit-app

# Install dependencies
pip install -r requirements.txt

# Run the application
python app.py

Implementation Details

Components

1. Main Application (app.py)

   • Orchestrator setup with custom Bedrock classifier
   • Chainlit event handlers for chat management
   • Streaming response handling

2. Agent Configuration (agents.py)

   • Tech Agent: Uses Claude 3 Sonnet via Bedrock
   • Travel Agent: Uses Claude 3 Sonnet via Bedrock
   • Health Agent: Uses Ollama with Llama 3.1

3. Custom Integration (ollamaAgent.py)

   • Custom implementation for Ollama integration
   • Streaming support for real-time responses

Usage Notes

• The application creates unique user and session IDs for each chat session
• Responses are streamed in real-time using Chainlit’s streaming capabilities
• The system automatically routes queries to the most appropriate agent
• Complete chat history is maintained throughout the session

Example Interaction

User: "What are the latest trends in AI?"
→ Routed to Tech Agent

User: "Plan a trip to Paris"
→ Routed to Travel Agent

User: "Recommend a workout routine"
→ Routed to Health Agent

Ready to build your own multi-agent chat application? Check out the complete source code in our GitHub repository.