Chat With Your Data - A Local MCP AI Agent

Build a secure, local-first AI agent that can chat with your files. This tutorial uses the Model Context Protocol (MCP), LangGraph, and Streamlit to create a powerful personal knowledge manager.

Updated Jun 20, 202510 min read

You've built agents that can reason and plan, but to be truly useful, they must interact with the world around them—and for many applications, that world is your local file system. The challenge is building a clean, robust bridge between an AI's abstract reasoning and the concrete actions of reading, searching, and managing your personal documents. Simply hard-coding file operations into your agent leads to brittle, unmaintainable systems.

This tutorial addresses this integration challenge head-on by building CogVault, a local-first AI agent that acts as your personal knowledge manager. We will use the Model Context Protocol (MCP) to create a standardized, decoupled interface between our agent and a set of custom file system tools. You will learn to build an MCP server that exposes capabilities like listing, reading, and semantically searching your documents. Then, you will construct a sophisticated agent using LangGraph that intelligently uses these tools to answer your questions. Finally, we'll wrap it all in an interactive Streamlit user interface.

By the end of this tutorial, you will have a fully functional, private AI assistant that can chat with your local data, demonstrating a powerful, modular, and scalable pattern for building real-world AI applications.