Fundamentals

An AI agent without memory starts every task from zero. The complete guide to the four kinds of memory — context windows, vector recall, knowledge graphs, feedback — what they solve, how they combine, and where each one breaks.

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.

Master context engineering and AI memory to craft personalized LLM outputs, reduce token costs, and future-proof your AI agents—read the full guide now!

Learn vector databases, how vector stores like Pinecone power semantic search and AI applications by indexing embeddings. Maximize their benefits with cognee now!