Cognee is the open-source memory platform designed to solve the problem of fragmented and forgotten context in AI agents. While many agents possess large context windows, they often lack a persistent way to retain, connect, and evolve information across multiple sessions. Created to bridge this gap, Cognee serves as an essential memory layer that allows agents to remember, recall, forget, and improve based on their interactions, effectively transforming raw data into a structured, queryable knowledge base. By utilizing graph, vector, and relational retrieval, it enables agents to maintain a consistent 'second brain' that scales with organizational needs.

Functionality of the platform revolves around a memory-native API that replaces complex data pipelines with four simple verbs: remember, recall, forget, and improve. It ingest various data sources—such as documents, manuals, chats, and APIs—and processes them into a unified knowledge graph. This structure ensures that facts remain connected, cited, and accurate over time, regardless of how many sessions or agents access the underlying data.

Some of the key features are:

• Persistent Knowledge Graph: Models data as a graph to represent complex relationships and improve retrieval accuracy beyond standard vector search.
• Memory-Native API: Provides four core verbs (remember, recall, forget, improve) to simplify how agents interact with their memory layer.
• Deployment Flexibility: Offers a managed Cognee Cloud solution, Docker-based containers, on-premise installation, and lightweight edge support via a Rust-based core.
• Self-Improving Memory: Learns from agent feedback and interactions to re-weight facts, ignore outdated information, and refine query relevance over time.
• Framework Integrations: Supports seamless connectivity with Claude Code, Cursor, LangGraph, OpenClaw, CrewAI, and other MCP-compatible agents.
• Data Privacy & Ownership: Fully GDPR-compliant with options for air-gapped enterprise deployments, ensuring businesses retain complete control over their proprietary agent knowledge.

Operationally, Cognee functions as a middleware layer that sits between the agent framework and the storage infrastructure. When an agent is initialized with Cognee, it gains the ability to store and retrieve contextual information via an MCP server or SDK. The system handles the heavy lifting of chunking data, extracting entities, inducing ontologies, and storing metadata, while the developer uses high-level API calls to inject memory directly into the agent’s execution lifecycle. This composition of graph and session memory ensures that every agent in a stack can access the same shared pool of knowledge.

Some common use cases include:

• Coding Agents: Enabling developer tools to recall past project decisions, fixes, and architectural constraints across different working sessions instead of relearning them.
• Sales & Deal Intelligence: Helping teams query complex account relationships, ICPs, and sales histories within a single, unified interface.
• Corporate Knowledge Bases: Managing technical documentation and industrial manuals, ensuring that customer-facing agents provide accurate, cited facts based on the latest internal data.
• Scientific Research: Supporting researchers at organizations like Bayer by providing a system to connect knowledge and generate hypotheses from vast, complex information sources.