MemoGraph 🧠

MemoGraph turns a folder of markdown notes into a queryable, AI-ready knowledge graph. It solves the LLM memory problem — your AI assistants forget last Tuesday's decision, can't find a related note across two projects, and re-derive the same insight again and again — by giving them a persistent, navigable, attribution-friendly memory layer that lives in plain markdown files you control.

You write notes the way you already do. MemoGraph indexes them, builds a graph from [[wikilinks]], ranks them by salience, and serves them back to your LLM (or your team) on demand.

⚡ Try it in 60 seconds

pip install memograph
memograph quickstart

That's it. The quickstart command drops a small, interconnected sample vault on your disk (15 notes about Python development, with real wikilinks between them), ingests it, and runs three live demo queries so you can see the graph + hybrid retrieval working before you decide whether to commit. Try this query in particular:

memograph --vault ~/memograph-quickstart search "FastAPI dependency injection"

The vault contains a note titled FastAPI dependencies (about Depends(...)) — the words "dependency" and "injection" never appear in any note's title. MemoGraph still finds it, because hybrid retrieval understands "dependency injection" semantically and the wikilink graph stitches related notes together. That's the product, demonstrated in one query.

Re-run memograph quickstart --force any time to reset to a fresh demo. When you're ready, point MemoGraph at your real notes: memograph --vault ~/your-notes ingest.

What you get

As a solo user / knowledge worker

• A vault of human-readable markdown files — nothing proprietary, no lock-in. Your notes outlive any tool.
• Hybrid retrieval that combines keyword search, semantic similarity, and graph traversal so you find the right note even when you don't remember the exact words.
• AI-assisted tagging, link suggestions, and gap detection that grow your knowledge base instead of letting it rot.
• A CLI and a web UI for browsing, editing, and visualizing the graph.

As an AI agent / IDE user

• A first-class Model Context Protocol (MCP) server with 30+ tools, working out of the box with Claude Desktop, Claude Code, Cursor, Cline, Windsurf, Continue, Zed, VS Code, Goose, Gemini CLI, OpenAI Codex CLI, and others.
• Autonomous "auto-save" hooks that capture decisions and context from your AI conversations into the vault automatically.
• Per-conversation memory recall — your assistant can pull "what did we decide last week about X" without you copy-pasting context every time.

As an enterprise / SaaS operator

• Multi-tenant deployment with filesystem-level isolation per tenant,

... View full README on GitHub