🧩cognee
topoteretes/cognee · homepage ↗
Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory across sessions with a self-hosted knowledge graph engine.
⭐ Very popular: 30k stars, gaining about 415 a week
View on GitHub ↗repo profile
momentum
durability
bus factor = how many people it takes to cover more than half the commits (6 months). 1 is a solo project; higher means the work is spread across a team. top-author share is the single busiest author's slice of those commits.
since we covered it
why it's a big deal
- It unifies vector search and graph databases into a single memory layer, so documents are both searchable by meaning and connected by relationships - not one or the other.
- Graduated from GitHub’s Secure Open Source Program, which matters when you’re building the memory layer that production AI systems depend on.
- Ships an MCP server out of the box, which means any MCP-compatible agent (Claude Code, OpenClaw, Cursor) can use cognee as its memory backend without custom integration work.
under the hood
- Python framework with modular pipelines: add data, cognify it (extract entities, build graphs, generate embeddings), then search across the resulting knowledge structure.
- Supports Neo4j, Kuzu, and NetworkX for graph storage and PostgreSQL, Qdrant, or Weaviate for vectors - swap backends without changing application code.
- CLI, FastAPI server, and a Next.js frontend for browsing the knowledge graph visually, plus full Docker support for self-hosted deployment.
our take from PR#27, 2026-02-11
star history
curve is sampled from GitHub's star history, plus our own daily readings since we covered it; the dashed stretch is before we first covered it, the solid line since. figures at coverage are the numbers we printed then (approx.), current count is live.
understory
Quietly building: more output than attention, for now.
- output, commits & releases
- clout, star velocity
output = commits & releases; clout = star velocity, both 0 to 100 monthly indices; the gap where output runs above clout is the understory. The understory →
covered in
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Memory infrastructure for AI agents
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Visual memory for local AI assistants
similar projects
compare these →- 📑 PageIndex
📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
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