Graphmind — AI skill for Claude Code

graphmind

[](https://github.com/aouicher/graphmind/actions/workflows/ci.yml) [](https://opensource.org/licenses/MIT)

Your codebase has a shape. Now your AI can see it. And remember it.

GraphMind turns your codebase into a knowledge graph your AI can query, navigate, and remember. Ask about dead code, dependencies, or blast radius — and get answers grounded in your actual architecture.

Up to **5,700× fewer tokens** than raw search (~10M tokens saved per session). Works with Claude Code, Cursor, Windsurf, Cline, Zed, Continue, and any MCP-compatible AI assistant.

CLI + Claude MCP integration	Desktop app — Mac & Windows

Why GraphMind

Every new AI session starts from zero. Your assistant re-reads the entire codebase, re-discovers architecture, and forgets every decision you explained last time. Across multiple projects, there's zero visibility into shared dependencies.

**graphmind** fixes this with four layers:

1. Structural graph — function-level knowledge graph per repo (AST-based, tree-sitter, 30+ languages)
2. Semantic embeddings — vector search over symbols (local ONNX, OpenAI, or Voyage AI)
3. Persistent memory — declarative store for decisions, patterns, conventions — survives across sessions
4. Cross-project links — dependencies and relationships between registered repos

Everything runs locally. No cloud. No open ports by default. No telemetry.

Benchmark


Comparison of token usage: `grep -r` (raw file search) vs `graphmind search` for the same query on a ~100k LO