The Cognee Blog

Cognee raises $7.5M seed led by Pebblebed to build the open-source memory layer for AI agents—backed by founders of OpenAI and Facebook AI Research.

Learn how OpenClaw's Markdown memory works, where it falls short, and how a Cognee plugin adds knowledge-graph recall, indexing, and smarter retrieval.

Decision traces aren't enough for agent memory. Learn how world models and behavioral validation create AI that predicts outcomes. Build smarter agents.

An AI agent without memory starts every task from zero. Here's how working, short-term, and long-term memory fit together, and how a real memory layer writes, manages, and reads it in production.

Memory stores what was said. Knowledge captures what it means. The difference is structure — typed entities, relationships, versioned facts — and it's the gap most agents still fall into. Three lines of code show what fixes it.

Two benchmark exercises across HotPotQA, TwoWikiMultiHop, and MuSiQue, plus a head-to-head against Mem0, Graphiti, and LightRAG. Full numbers, methodology, honest caveats, and reproduction code — read the evidence behind the marketing.

Dumping docs into a vector store isn't enough. An AI agent knowledge base needs three layers — reference, operational, feedback — plus an ingestion pipeline that keeps them current. What actually goes in, with code.

Add persistent memory to Claude Agent SDK using Model Context Protocol (MCP) + cognee tools—store once, retrieve anytime without prompt bloat. Try it now!

Add persistent AI memory to n8n automation with the cognee node—cognify data into a graph and search context instantly. Install now and build smarter flows!