Cognee is an open-source knowledge engine that creates persistent, structured memory for AI agents by combining vector embeddings with graph databases. Unlike traditional RAG systems that chunk and retrieve text fragments in isolation, Cognee builds a knowledge graph that preserves relationships between entities across documents. Its ECL pipeline handles extraction, cognification, and loading of data from over 38 source formats including text, audio, images, and structured files, making it a comprehensive ingestion layer for enterprise AI applications.
The architecture draws from cognitive science principles to mimic how humans organize and recall information. When new data enters the system, Cognee identifies entities, maps relationships, generates embeddings, and stores everything in a hybrid vector-graph structure. This approach enables queries that combine semantic similarity with relational traversal, answering questions that require connecting information across multiple documents. Benchmarks show this GraphRAG method outperforms standard vector-only retrieval for entity identification and multi-hop queries.
Cognee integrates with major LLM frameworks including LangChain and LlamaIndex, and can run entirely locally without cloud dependencies. The project has grown to 15,100 GitHub stars with over 300 contributors and 6,400 commits. Backed by a seven and a half million euro seed round from Pebblebed and 42CAP, the team is expanding into a managed cloud platform and developing a Rust-based engine for improved performance. Over 70 companies currently use Cognee in production for agent memory and intelligent search.
