#66th of 12 in Memory

Cognee

Memory engine that turns documents and code into a knowledge graph

Stars
32k
License
Apache-2.0
Last commit
Oct 2026
Last release
Oct 2026
Language
Python

Overview

Cognee builds persistent agent memory by extracting entities, relationships and chunks from text, code and sessions into a graph and vector index with hybrid recall. Without an LLM key it uses local GLiNER extraction and embeddings; adding a key enables generated answers via OpenAI, Ollama or other providers. It runs as a library, CLI, REST API (port 8000), UI (3000) and MCP server (8001).

Who it is for: Developers building graph-backed memory for agents and company knowledge

Strengths

  • Keyless mode: local GLiNER extraction and embeddings, no cloud LLM required
  • Claude Code and Codex plugins, OpenClaw plugin, MCP server, Python, TypeScript and Rust SDKs
  • Imports memory from Mem0, Letta, Zep or Graphiti via the COGX format
  • Apache-2.0; research paper and BEAM evaluation published

Weaknesses

  • Single-Postgres graph store is a demo; production version is a licensed product
  • API defaults to multi-tenant mode; local use needs ENABLE_BACKEND_ACCESS_CONTROL=false
  • Bundled GLiNER extractor is a demo; higher-accuracy version requires contacting the vendor
  • UI launcher needs Node.js/npm and Docker for its MCP service

What it needs

  • no GPU
  • Docker + Compose
  • Compose runs Neo4j, PostgreSQL, Redis
  • Models: local GLiNER + embeddings (keyless), OpenAI, Ollama, other providers per docs
  • port 8000

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