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local-ai-mcp-chainlit

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local-ai-mcp-chainlit is a client example repository demonstrating how to connect Local AI models to any MCP using Chainlit. It provides a practical integration pattern for developers to run local language models with MCP servers, enabling real-time interaction and tool calling through a customizable chat interface. The repo includes setup instructions, a tutorial video, and leverages the Chainlit SDK for extensibility, making it ideal for developers building AI-enhanced workflows with local models and MCP.

Use This MCP client To

Connect local AI models to MCP servers for real-time interaction Run and test local language models with MCP tool calls Build custom chat interfaces using Chainlit SDK Integrate local AI workflows with MCP-enabled environments Experiment with local model APIs supporting tool calls Develop AI applications combining local models and MCP clients

README

Chainlit MCP Integration

In this example repo you will learn how to use any MCP together with Chainlit. It is highly recommend to first watch the accompanying tutorial video

Development Environment

  1. Ensure Python 3.x is installed

  2. It's recommended to create a virtual environment:

    # Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

  3. Install dependencies:

    pip install -r requirements.txt

  4. Run Chainlit:

chainlit run app.py -w

  1. Start LM Studio's dev server with a model of your choice that supports tool calls (https://lmstudio.ai/docs/app/api/tools)

  2. Connect an MCP server and try it out in the Chainlit UI

  3. Extend the chat app whichever way you like with the Chainlit SDK (https://docs.chainlit.io/get-started/overview)

local-ai-mcp-chainlit FAQ

How do I set up the local-ai-mcp-chainlit client?
Install Python 3.x, create a virtual environment, install dependencies with pip, then run Chainlit using 'chainlit run app.py -w'.
Can I use any local AI model with this client?
Yes, as long as the model supports tool calls and can be served via LM Studio or a compatible API.
What is the role of Chainlit in this MCP client?
Chainlit provides the UI and SDK to build interactive chat applications that connect local AI models to MCP servers.
Is there a tutorial to help me get started?
Yes, the GitHub repo includes a recommended tutorial video linked in the README for step-by-step guidance.
Can I extend the chat app functionality?
Absolutely, the Chainlit SDK allows you to customize and extend the chat interface and interactions.
Does this client support multiple MCP servers?
Yes, it is designed to connect to any MCP server, enabling flexible integration with various data sources and tools.
What development environment is recommended?
Python 3.x with a virtual environment is recommended for dependency management and isolation.
How do I connect the client to an MCP server?
Start your MCP server (e.g., LM Studio dev server) and configure the client to connect via the Chainlit UI as described in the README.