Pydantic MCP Agent with Chainlit

A powerful AI agent implementation using Pydantic and Chainlit, capable of web browsing and interaction through MCP (Multi-Command Protocol).

Features

• Web browsing capabilities with automated interactions
• Integration with Ollama for local LLM support
• Chainlit-based interactive chat interface
• Pydantic models for type-safe data handling
• Configurable MCP server integration

Prerequisites

• Python 3.8+
• Node.js and npm (for MCP server)
• Ollama installed locally
• MCP server access

Installation

1. Clone the repository:

git clone https://github.com/RyanNg1403/pydantic-ai-mcp-agent-with-chainlit.git
cd pydantic-ai-mcp-agent-with-chainlit

2. Install Python dependencies:

pip install -r requirements.txt

3. Install Node.js dependencies:

npm install

Configuration

1. Copy the template configuration file:

cp mcp_config.template.json mcp_config.json

2. Edit mcp_config.json with your configuration settings. The file is ignored by git for security.

Usage

Running the Chainlit Interface

chainlit run pydantic_mcp_chainlit.py

Running the Agent Directly

python pydantic_mcp_agent.py

Project Structure

• pydantic_mcp_agent.py: Core agent implementation
• pydantic_mcp_chainlit.py: Chainlit interface implementation
• mcp_client.py: MCP client implementation
• requirements.txt: Python dependencies
• mcp_config.template.json: Template for configuration
• .gitignore: Specifies which files git should ignore

Environment Variables

The following environment variables can be set in your .env file:

• EXA_API_KEY: Your MCP API key
• OLLAMA_HOST: Ollama host address (default: http://localhost:11434)

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Thanks to the Chainlit team for their excellent chat interface
• Thanks to the Ollama team for their local LLM solution
• Thanks to the MCP team for their browser automation capabilities