Cognee is built for AI systems that need persistent memory and relationship-aware retrieval, not just flat vector similarity lookups.
Key Features
- Knowledge Graph Construction: Turn raw content into linked entities and relationships.
- Semantic Retrieval Layer: Combine embeddings with contextual retrieval patterns.
- Model Provider Flexibility: Integrate different LLM and embedding stacks.
- MCP Ecosystem Support: Connect with assistant workflows using MCP patterns.
- API-First Design: Ingest, process, and query through service APIs.
- Storage Backends: Support multiple persistence strategies for different use cases.
Why Choose Cognee?
- You are building RAG apps with memory requirements.
- You need better context structure than a plain vector DB.
- You want private data ownership for AI workloads.
Docker Deployment
Cognee is commonly deployed with PostgreSQL/pgvector and related service components. Use persistent volumes and secure API key management.
Getting Started
- Deploy Cognee and its data services.
- Configure model provider credentials.
- Ingest documents or datasets.
- Validate retrieval and graph quality on real queries.
Full Setup Guide
Follow this full walkthrough: Cognee Self-Host Guide.
