Understanding and working on a large codebase is a big hassle for coding agents (like Google Gemini, Cursor, Microsoft Copilot, Claude etc.) and humans alike. Normal RAG systems often dump too much or irrelevant context, making it harder, not easier, to work with large repositories.

What if we could feed coding agents with only the precise, relationship-aware context they need - so they truly understand the codebase? That’s what led me to build CodeGraphContext - an open-source project to make AI coding tools truly context-aware using Graph RAG.

What it does Unlike traditional RAG, Graph RAG understands and serves the relationships in your codebase: 1. Builds code graphs & architecture maps for accurate context 2. Keeps documentation & references always in sync 3. Powers smarter AI-assisted navigation, completions, and debugging

Plug & Play with MCP CodeGraphContext runs as an MCP (Model Context Protocol) server that works seamlessly with:VS Code, Gemini CLI, Cursor and other MCP-compatible clients

GitHub Repo → https://github.com/CodeGraphContext/CodeGraphContext v0.2.1 released ~500 GitHub stars, ~300 forks 25k+ downloads 65+ contributors