The Cognee Blog

Learn why AI agents lose context and how to fix agent memory with better state, retrieval, long-term storage, and cognee's memory lifecycle.

Agentic RAG puts an agent in control of retrieval — planning, choosing tools, and retrieving again when evidence is missing. Learn how it works and when it's worth it.

Learn what Claude Code already remembers between sessions, where the gaps are, and how to set up and verify cognee's plugin for persistent, searchable memory.

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.