Cognee is an open-source memory engine for agentic systems that turns scattered data into a continuously updated knowledge graph. It combines graph-based and vector-based memory so AI agents can retrieve information, connect related concepts, and adapt as new data arrives.
Memory layer for vertical AI agents
Cognee acts as a shared retrieval and reasoning core behind an agent. Instead of maintaining separate vector stores and custom knowledge graphs, teams can use one memory layer that keeps context, tracks relationships, and supports multi-step tasks with explanations.
Living knowledge graph with feedback-driven learning
The engine updates concepts, synonyms, and relationships based on user feedback. Over time, memory becomes more precise:
- Search quality improves
- Errors are reduced
- Important context is less likely to be “forgotten”
This is especially useful for domain-specific copilots and enterprise assistants.
Fits into existing AI infrastructure
Cognee can be added to an existing AI stack as a dedicated memory layer. It helps connect “data islands” into a structure suitable for search, analysis, and building agents that rely on up-to-date knowledge rather than one-off prompts.
