What does Cognee do and what problem does it solve?
Cognee is an open-source agent memory platform that captures context and turns it into graph memory, allowing AI agents to recall information persistently across sessions. It addresses the problem where important information for agents gets forgotten, disconnected, or silently incomplete. The platform uses graph, vector, and relational retrieval methods.
What is Cognee used for and in what situations?
Cognee is used for building persistent memory for AI agents across sessions. Key use cases include journaling, deal intelligence, research, and giving an agent long-term memory. It is also used for technical and industrial knowledge bases, and memory for coding agents that recall past work and decisions.
Who is Cognee for?
Cognee is for developers building AI agents who need persistent memory and retrieval capabilities. The platform is part of the Berkeley Xcelerator and is trusted by engineers. It provides an open-source starting point and scales to Cognee Cloud for production use.
What features does Cognee offer?
Cognee offers graph, vector, and relational retrieval for building agent memory. It can be self-hosted, run in Docker, on-premises, or on Cognee Cloud. The platform integrates with tools like Claude Code, Codex, MCP, OpenClaw, and Hermes. It supports memory that improves with feedback and citations.
How is Cognee priced or packaged?
Cognee is available as an open-source product with a free starting point. The platform also offers a cloud service called Cognee Cloud for scaling production deployments.
What is Cognee?
Cognee is an open-source agent memory platform that enables LLM agents to build persistent memory across sessions using graph, vector, and relational retrieval. It can be self-hosted or run on Cognee Cloud.