The Cognee Blog

Compare GraphRAG vs RAG across retrieval, data structure, cost, use cases, and limitations. Learn when vector RAG is enough and when graph retrieval is worth the added complexity.

Learn how AI knowledge graphs connect entities and relationships to improve search, question answering, generative AI grounding, reasoning, and agent memory.

cognee has been selected for Berkeley RDI's Xcelerator 2026 Spring Cohort, a non-dilutive program for agentic AI startups, alongside Narada AI, RELAI, and Headroom.

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.