One Open Source Project a Day (No. 71): CodeGraph — Pre-Index Your Codebase for AI Agents, Save 35% Cost and 70% Tool Calls

Introduction

"~35% cheaper · ~70% fewer tool calls · 100% local"

This is the No.71 article in the "One Open Source Project a Day" series. Today we are exploring CodeGraph.

Start with a scenario: you ask Claude Code "How is AuthService being called?" Without any assistance, Claude's approach is: glob-scan directories, run multiple greps, read several files — then finally answer. The whole process might trigger 10–15 tool calls and consume hundreds of thousands of tokens.

CodeGraph's insight is to front-load this work: before you start, it has already parsed your codebase with tree-sitter into a semantic graph stored in a local SQLite database, then exposes 8 query tools to AI agents via MCP. When the agent needs to understand code, a single codegraph_context call returns entry points, related symbols, and code snippets — no file reading required.