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Pydantic MCP Agent with Chainlit

Pydantic MCP Agent with Chainlit

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github.com/RyanNg1403AI & Agents5Updated 75w ago
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About Pydantic MCP Agent with Chainlit

Pydantic MCP Agent with Chainlit is a Python agent project for web browsing and interaction, using Pydantic, Chainlit, and optional local Ollama support. It includes both a chat interface and a direct agent runner.

Key features

  1. 01Browse and interact with the web
  2. 02Use a Chainlit chat interface
  3. 03Support local LLMs through Ollama
  4. 04Handle data with Pydantic models
  5. 05Configure MCP server connections

Use cases

  1. 01Run a web-browsing agent in chat
  2. 02Use a local Ollama model with the agent
  3. 03Configure MCP servers for agent tools

MCP support

The project includes an MCP client and configurable MCP server integration for connecting tools to the agent.

#pydantic#mcp-agent#chainlit#ai-agent

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

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

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.