colbymchenry/codegraph

Pre-indexed code knowledge graph, auto syncs on code changes, for Claude Code, Codex, Gemini, Cursor, OpenCode, AntiGravity, Kiro, CoPilot, and Hermes Agent — fewer tokens, fewer tool calls, 100% local

What it solves

AI agents typically discover code structure by repeatedly calling tools like grep or reading files one by one, which is slow and expensive. CodeGraph solves this by providing a pre-built semantic knowledge graph of every symbol, call edge, and dependency in a codebase, allowing agents to retrieve precise context in a single call rather than crawling through files.

How it works

CodeGraph uses a Rust-powered kernel to perform structural extraction and cross-file resolution across a wide variety of programming languages. It builds a local index (stored in .codegraph/) and provides an MCP (Model Context Protocol) server that AI agents can query. The system includes an auto-sync feature that watches for file changes and updates the graph in real-time, ensuring the index never becomes stale.

Who it’s for

Developers using AI coding agents such as Claude Code, Cursor, GitHub Copilot, Gemini, and others who want to reduce token costs, decrease latency, and improve the accuracy of the agent's understanding of their codebase.

Highlights

  • Broad Language Support: Supports a vast array of languages including TypeScript, Python, Go, Rust, Java, C#, PHP, Ruby, C/C++, Swift, Kotlin, and more.
  • Agent Integration: Seamlessly wires into popular agents via an MCP server.
  • High Performance: Built with a Rust kernel for speed and 100% local operation.
  • Efficiency Gains: Benchmarks show significant reductions in tool calls, tokens processed, and overall cost compared to file-reading agents.
  • Zero-Config Sync: Automatically updates the code graph as files are modified.

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