Deep Dives

Grounding is the most effective LLM hallucination solution: make the model answer from retrieved, verifiable facts instead of training memory. Retrieval quality, structured knowledge, verification layers, and feedback loops all determine how many wrong answers survive to production.

cognee beat SOTA on BEAM's 100k-token setting by 6.5% and matched SOTA at 10M tokens using only default open-source features — no custom benchmark-specific architecture.

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.

Most support teams don't have a support problem — they have a context problem. Here's how we built a support agent on top of cognee using user, agent, and organization memory.

Adding memory to agentic workflows used to mean restructuring your stack. One decorator changes that. We ran 198 simulated sales conversations — and the results make a strong case for structured memory.