Structural graph memory for AI coding assistants navigate any codebase by structure, not guesswork.
Runs in an isolated sandbox · 5 free calls per hour · keys never stored
repo-graph gives AI coding assistants a structural map of your codebase — entities, relationships, and end-to-end feature flows — so they navigate straight to the files that matter instead of grepping and reading everything first. It scans your repo once and builds a lightweight graph of what exists (modules, classes, functions, routes, services, components), how things connect (imports, calls, handles, cross-stack HTTP), and where each feature begins and ends. The assistant queries that graph through 11 MCP tools (status, flow, trace, impact, activate, find, dense_text, graph_view, …), finds the minimal set of files for the task, and reads only those. Works across 20+ languages and frameworks (Go, Rust, TypeScript/React/Angular/Vue, Python, Java, C#, and more), with cross-stack linking between frontend calls and backend routes. The engine is native Rust shipped as a prebuilt wheel, so scans are fast — a 12,000-node repo maps in a couple of seconds. Controlled before/after on a Go+Angular bug fix, same model and prompt: 2.5× fewer tokens and ~9× faster with repo-graph installed.
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