Cognee is an open-source AI memory platform designed to give AI agents persistent long-term memory across sessions through a self-hosted knowledge graph engine. Written in Python, the platform enables developers to ingest data in any format and automatically build a knowledge graph that agents can query to recall information, understand relationships, and act with full context. The project combines vector embeddings, graph reasoning, and cognitive-science-grounded ontology generation to make documents both searchable by semantic meaning and connected through evolving relationships.
The core functionality revolves around four primary operations: remember, recall, forget, and improve. The remember operation ingests data and builds the knowledge graph, recall retrieves relevant information for agent decision-making, forget removes data when needed, and improve refines the knowledge structure over time. Cognee's architecture emphasizes unified knowledge infrastructure with local execution, multimodal support, and ontology grounding. The platform supports persistent and learning agents that can benefit from feedback and cross-agent knowledge sharing while maintaining reliability through agentic user and tenant isolation, traceability via OpenTelemetry collectors, and audit trails.
A significant architectural innovation in Cognee 1.0 is the ability to run the entire memory layer on a single Postgres instance rather than requiring a traditional stack of separate services. This unified approach consolidates graph relationships, vector embeddings via pgvector, session management, and metadata into one database, eliminating the operational complexity of deploying and maintaining Neo4j, Redis, dedicated vector databases, and relational databases separately. Benchmarks show this Postgres-backed approach runs approximately 10 percent faster than separate graph-plus-vector setups, though the platform remains flexible enough to swap in dedicated backends like Neo4j, Neptune, Qdrant, ChromaDB, Weaviate, and Milvus when specific workloads demand them.
The project maintains active development with strong community engagement.
Cognee provides multiple integration pathways including a TypeScript client, Rust client, and plugins for Claude Code and OpenClaw. The platform supports deployment across multiple cloud providers including Cognee Cloud as a managed service, Modal for serverless workloads, Railway, Fly.io, Render, and Daytona for cloud sandboxes. Local development is fully embedded with SQLite, LanceDB, and Kuzudb requiring no external services. The project includes comprehensive documentation, a Colab walkthrough demonstrating core features, and example implementations for use cases like customer support agents and expert knowledge distillation. A research paper titled "Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning" by Markovic et al. from 2025 provides theoretical grounding for the platform's approach.
