The Cognee Blog

cognee 1.0 runs the full agent memory layer — graph, vectors, sessions, and metadata — on a single Postgres instance, eliminating the need for separate graph database, vector store, and Redis deployments.

Persistent memory trades an upfront ingestion cost for cheaper queries. We measure where the tokens go in cognee, model the trade-off, and find break-even at roughly 23–26 repeated queries — after which the gap keeps widening.

Our LinkedIn and X videos put two numbers on screen — 7x cheaper than chat and 145% better than the best alternative. Here's exactly where each one came from, linked to our BEAM report.

The cognee core rebuilt in Rust, bringing the full agent memory pipeline — graph construction, embeddings, and retrieval — to phones, robots, and offline environments without a server-side stack.

cognee 1.0 ships four memory verbs — remember, recall, improve, forget — with a self-improving feedback loop, hybrid retrieval with evidence references, a TypeScript SDK, and migration tools for Mem0, Zep, and Letta.

An AI knowledge base needs more than stored documents. Source context, entity relationships, and plural retrieval strategies are what turn stored data into reliable, agent-ready knowledge.

A knowledge base is a centralized system for storing reusable information — but most fail because of ownership gaps, drift, and no clear sense of what actually belongs in them.

Generative AI and LLMs are not the same thing. Learn the real difference, why architecture matters more than model size, and what memory and retrieval actually do.

There's no single best vector database — the right choice depends on your retrieval workload, deployment model, and whether you need search, RAG, or full AI memory.