[GitHub Trending] colbymchenry/codegraph
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A pre-indexed code graph reducing token usage for AI coding tools; novel and highly actionable for developers.
CodeGraph pre-indexes codebases into knowledge graphs of symbol relationships and call graphs, enabling AI agents to answer complex queries with 92% fewer tool calls and 71% faster exploration, as benchmarked across six codebases including VS Code and Swift Compiler (25,874 files, 272,898 nodes indexed in <4 minutes, zero file reads). Compatible with Claude Code, Cursor, Codex CLI, and OpenCode, it runs locally, handles cross-language queries (Python+Rust, Swift/C++), and includes features like full-text search (FTS5) and impact analysis, with the Java codebase requiring just one codegraph_explore call to answer a full question.
Integrate CodeGraph into your AI coding agent stack to slash token consumption and exploration latency by an order of magnitude without sacrificing accuracy.
For a solutions architect optimizing AI-assisted development pipelines, CodeGraph directly attacks the token and latency overhead of agentic code exploration by replacing expensive file scanning with instant graph lookups.
CodeGraph Supercharge Claude Code, Cursor, Codex, and OpenCode with Semantic Code Intelligence 94% fewer tool calls · 77% faster exploration · 100% local Get Started npx @colbymchenry/codegraph Interactive installer auto-configures your agent(s) — Claude Code, Cursor, Codex CLI, opencode Initialize Projects cd your-project codegraph init -i Why CodeGraph? When Claude Code explores a codebase, it spawns Explore agents that scan files with grep, glob, and Read — consuming tokens on every tool call. CodeGraph gives those agents a pre-indexed knowledge graph