Your AI coding agent has a dirty secret:
It doesn't understand your codebase. It greps it. Over and over. Burning your tokens on every search.
Watch Claude Code on a big repo — it spawns Explore agents that grep, glob, and Read files in loops just to figure out what calls what. You're paying for an LLM to do what a database does for free.
CodeGraph fixes this at the root.
It builds a local knowledge graph of your entire repo — every symbol, call graph, route, and dependency, across 20 languages — and plugs it into your agent as an MCP server. The agent stops searching and starts querying.
One tool call. Instant answer. Even across language boundaries grep can't follow — Swift ↔ ObjC bridges, React Native modules, dynamic dispatch.
Benchmarked on real codebases — VS Code, Django, Tokio, OkHttp, Excalidraw:
→ Up to 81% fewer tool calls (VS Code) → Up to 64% fewer tokens → Up to 40% cheaper (Alamofire) → Faster on every single repo tested
Average across 7 languages: 47% fewer tokens, 58% fewer tool calls, 22% faster.
Works with Claude Code, Cursor, Codex, OpenCode, Gemini CLI, Antigravity, Kiro, Hermes.
Setup:
npx @colbymchenry/codegraph
→ 100% local. SQLite only. → No API keys. No cloud. Nothing leaves your machine. → Auto-syncs as you code. Zero config — literally no config file exists.
47k stars and climbing. MIT licensed.
The next leap in AI coding isn't a smarter model.
It's stopping the model from wasting its intelligence on file search.
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