Who cognee is for#
Long-term agent memory
Use Cognee when agents need to retain user preferences, project history, documents, and workflow context across sessions.
Skip if:
You only need short-lived chat history inside one conversation.
Private knowledge graph for AI apps
Teams can ingest internal data and give agents relationship-aware retrieval from a self-hosted memory layer.
Skip if:
Your data cannot be placed into an agent-accessible memory system.
Agent framework integration
Use the Python, TypeScript, MCP, and plugin paths to attach memory to existing agent stacks.
Skip if:
You need only a hosted vector database with no graph or agent-memory semantics.
The problem it solves#
AI agents lose continuity when memory is only prompt history or basic vector search. Cognee addresses that by turning data into a self-hosted memory layer with graph relationships, embeddings, and retrieval APIs that agents can use across sessions.
How it solves it#
Persistent agent memory
Cognee gives agents long-term memory across sessions through remember, recall, forget, and improve operations.
Self-hosted knowledge graph
The README says Cognee continuously builds a knowledge graph from ingested data so agents can connect information by relationship.
Hybrid retrieval model
Cognee combines vector embeddings, graph reasoning, and relational search instead of relying on one retrieval method.
Multiple integration paths
Developers can use Python, CLI, Docker, MCP server mode, Claude Code plugin, Cognee Cloud, or the TypeScript client.
Postgres memory layer option
The README describes running the memory layer on Postgres, with dedicated graph and vector backends available for larger workloads.
Strengths and trade-offs#
Strengths
- Purpose-built for agentsCognee focuses on agent memory rather than generic document storage, which makes its API and integrations relevant to agent workflows.
- Self-hosting pathTeams can run the memory layer themselves through Python, Docker, or database-backed deployment options.
- Broader than vector searchGraph reasoning and ontology generation help preserve relationships that pure vector retrieval can flatten.
Trade-offs
- -Infrastructure choices still matterTeams must pick and operate the right backend mode for their workload, from embedded local development to Postgres or dedicated services.
- -Memory quality depends on data hygieneAgents recall better when the ingested data, dataset boundaries, and cleanup routines are maintained carefully.
- -Not just a drop-in vector APITeams looking only for managed vector search may find Cognee's graph and memory model more than they need.
cognee vs alternatives#
Compared to Pinecone
Pinecone is a managed vector database focused on scalable embedding search. Cognee is an open-source agent memory layer that combines vector retrieval with graph relationships, ontology generation, Python APIs, CLI tools, MCP, Docker, and plugin integrations. Use Pinecone when managed vector search is the core need; use Cognee when agents need self-hosted memory with relationship-aware recall.
Install and self-host#
uv pip install cognee
# Optional Postgres extra
pip install "cognee[postgres]"What it's built on#
- Languages
- JavaScriptPythonTypeScript
- Frameworks
- FastAPINext.jsReact
- Databases
- PostgreSQL
- Cache
- Redis
FAQ#
What is Cognee?
Cognee is an open-source AI memory tool that builds a self-hosted knowledge graph for agents.
What license does Cognee use?
GitHub metadata reports Cognee as Apache-2.0 licensed.
How do you install Cognee?
The README shows uv pip install cognee for Python use, plus Docker Compose, Docker image, MCP server, and TypeScript client paths.
Does Cognee require a separate graph database?
No. The README says Cognee can run the memory layer on Postgres, while still supporting dedicated graph and vector backends for workloads that need them.
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