Pydantic MCP Agent with Chainlit

Pydantic MCP Agent with Chainlit

By RyanNg1403 GitHub

This repo makes use of MCP servers to seamlessly integrate multiple tools for the agent.

pydantic mcp-agent
Overview

What is Pydantic MCP Agent with Chainlit?

Pydantic MCP Agent with Chainlit is a powerful AI agent implementation that utilizes Pydantic and Chainlit to enable web browsing and interaction through the Multi-Command Protocol (MCP).

How to use Pydantic MCP Agent with Chainlit?

To use the agent, clone the repository, install the required dependencies, configure the MCP settings, and run the Chainlit interface or the agent directly.

Key features of Pydantic MCP Agent with Chainlit?

  • 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

Use cases of Pydantic MCP Agent with Chainlit?

  1. Automating web interactions for data retrieval
  2. Building interactive chatbots that leverage local LLMs
  3. Creating applications that require type-safe data handling with Pydantic

FAQ from Pydantic MCP Agent with Chainlit?

  • What are the prerequisites for using this project?

You need Python 3.8+, Node.js, npm, and access to an MCP server.

  • Is there a specific way to configure the MCP server?

Yes, you need to edit the mcp_config.json file with your specific settings.

  • Can I contribute to this project?

Yes! You can fork the repository and submit a pull request with your changes.

Content

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
  1. Install Python dependencies:
pip install -r requirements.txt
  1. Install Node.js dependencies:
npm install

Configuration

  1. Copy the template configuration file:
cp mcp_config.template.json mcp_config.json
  1. 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:

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
No tools information available.
No content found.