Cognee
Knowledge-graph-backed memory control plane that turns raw documents, conversations, and structured data into a queryable graph of entities and relationships, paired with a vector store for semantic similarity.
Description
Cognee is an open-source memory framework that ingests heterogeneous sources (text, code, conversations, structured records) and runs a `.cognify` pipeline that converts plain text into chunks, embeddings, summaries, nodes, and edges. The graph store captures entities and relationships in a knowledge graph, and a parallel vector store holds embeddings for semantic similarity, so agents can search by meaning and by explicit relationships from a single query API. Cognee positions itself as a memory control plane combining embeddings, graphs, and cognitive-science approaches.
Solution
Pipeline-style ingestion plus query API. The application calls cognee.add() on raw sources and cognee.cognify() to run a six-task extraction pipeline; chunks, embeddings, summaries, nodes, and edges are persisted into a graph store and a parallel vector store. At agent turn time the application calls a search API that can combine vector similarity with graph traversal, and injects results into the prompt.
Primary use cases
- knowledge-graph memory for agents
- hybrid graph + vector retrieval
- long-term semantic memory over heterogeneous sources
- entity-relation extraction for agent context
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