Fundamentals

Long term memory AI is more than chat history or larger context windows. Learn what agents should keep, retrieve, update, and forget.

Cognee is a memory engine for AI agents that builds a knowledge graph from data and makes it searchable. It's a simple, yet powerful way to build AI agents that can remember and use information over time.

An AI agent without memory starts every task from zero. Here's how working, short-term, and long-term memory fit together, and how a real memory layer writes, manages, and reads it in production.

Dumping docs into a vector store isn't enough. An AI agent knowledge base needs three layers — reference, operational, feedback — plus an ingestion pipeline that keeps them current. What actually goes in, with code.

Vector databases find data by meaning; graph databases find it by relationship. Compare how each works, their strengths, and when to use each or a hybrid.

Pair a vector database for semantic recall with a graph database for explainable paths and provenance in GraphRAG pipelines. See trade-offs and tips - read now.

Explore how AI memory evolves from base LLM to Graph-aware RAG using Cognee’s open-source memory engine. Learn the transformation—read now on cognee!

Learn how graph-based AI memory links concepts for smarter retrieval in Cognee. Watch our demo and start building AI that truly remembers and connects ideas.

GPT-5 is here, bringing major advances in AI memory and context engineering. Explore confirmed features, real-world limits, and what it means for workflows.