The Cognee Blog

GraphRAG adds a knowledge graph to the RAG pipeline so retrieval can follow relationships instead of returning isolated chunks. Learn how the pipeline works, when to use local vs global search, and where GraphRAG earns its complexity over standard RAG.

RAG pairs retrieval with generation so an LLM can answer from external knowledge instead of just its training data. Learn how RAG works, what it solves, and where chunk-based retrieval starts to hit its limits.

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.

LLM hallucinations are fluent, confident-sounding answers that are false or unsupported by any source. Learn what causes them and the detection methods — groundedness checks, self-consistency, LLM judges — that catch them before users act on them.

cognee 1.0 is the first open-source memory platform built around a memory-native API — remember, recall, improve, forget — with full data ownership and deployment flexibility from managed cloud to edge.

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.