Give Cognee your documents, and it creates a graph of raw information, extracted concepts, and meaningful relationships you can query.

Why AI memory matters

When you call an LLM, each request is stateless: it doesn’t remember what happened in the last call, and it doesn’t know about the rest of your documents. That makes it hard to build applications that actually use your documents and carry context forward. You need a memory layer that can link your documents together and create the right context for every LLM call.

How Cognee works

When it comes to your data, Cognee knows what matters. There are three key operations in Cognee:
  • .add — Prepare for cognification
    Send in your data asynchronously. Cognee cleans and prepares your data so that the memory layer can be created.
  • .cognify — Build a knowledge graph with embeddings
    Cognee splits your documents into chunks, extract entities, relations, and links it all into a queryable graph, that serves as the basis for the memory layer.
  • .search — Query with context
    Queries combine vector similarity with graph traversal. Depending on the mode, cognee can fetch raw nodes, explore relationships, or generate natural-language answers through RAG. It always creates the right context for the LLM.

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