Introduction

CodeGraph is a local-first code-intelligence tool. It parses your codebase with tree-sitter, stores every symbol, edge, and file in a local SQLite database, and exposes the result as a queryable knowledge graph — over the Model Context Protocol (MCP), a CLI, and a TypeScript library.

It exists to make AI coding agents — Claude Code, Cursor, Codex CLI, opencode, Hermes Agent, Gemini CLI, Antigravity IDE, and Kiro — answer structural questions without scanning files. Instead of fanning out across grep, glob, and Read to reconstruct how code fits together, an agent queries a pre-built index and gets the answer in a handful of calls.

Why it matters

When an agent explores a codebase, it spends most of its budget on discovery — finding the right files before it can read them. CodeGraph removes that step: it hands the agent the exact code it needs in one call, so symbol relationships, call graphs, and structure don’t have to be rebuilt file by file.

The universal win is surgical context and speed — fewer tool calls, faster answers, on every codebase. Tested across 7 real-world open-source codebases (median of 4 runs per arm), giving an agent CodeGraph meant, regardless of repo size:

• 58% fewer tool calls
• 22% faster
• file reads cut to ~zero

Token and dollar savings are real too, but they’re the scale-dependent bonus that shows up on large, tangled codebases run at volume — small and noisy on a modest repo, material only once the codebase (and the team) gets big.

What’s in the graph

• Symbols — functions, classes, methods, types, routes, components, and more.
• Edges — calls, imports, inheritance, references, and framework-specific relationships.
• Files — structure plus full-text search (FTS5).

Extraction is deterministic — derived from the AST, never LLM-summarized.

100% local

No data leaves your machine. No API keys, no external services — just a SQLite database in .codegraph/.

Ready to try it? Head to the Quickstart.