cognee

Video Review

About

Cognee is an open-source knowledge engine that transforms scattered, multi-format data into persistent, interconnected memory systems for AI agents. Unlike traditional RAG systems that retrieve chunks of text, cognee processes data through a pipeline that builds living knowledge graphs — structures that capture not just content but the relationships, ontologies, and semantic connections between concepts. The core workflow has three operations: Add (ingest data from 38+ supported formats including documents, code, and structured data), Cognify (process and transform raw content into a structured knowledge graph with vector embeddings and graph relationships), and Search (query using combined vector similarity and graph traversal for contextually accurate results). This approach allows the system to retrieve information based on meaning, context, and logical relationships — not just keyword matching. Cognee integrates with 29+ database options spanning vector stores, graph databases, and traditional models, and connects to 12+ agentic frameworks including LangChain, LlamaIndex, and CrewAI. It supports multi-tenant architecture for user and dataset isolation, OTEL-based observability, and audit trails for regulated industries. The platform is self-hosted by default with full local deployment capability, making it suitable for privacy-conscious teams. A hosted cloud option is available starting at $35/month. Key users include engineers at Splunk, Redis, Autodesk, AWS, and Atlassian. Backed by $7.5M seed funding from founders of OpenAI and Facebook AI Research.

Key Features

• Knowledge Graph Construction — automatically builds structured knowledge graphs from raw documents, code, and multi-format data
• Multi-Database Backend — supports 29+ storage backends including Qdrant, Weaviate, Pinecone, Neo4j, and PostgreSQL with pgvector
• 38+ Data Format Ingestion — ingests text, PDFs, markdown, source code, web pages, and more through a unified Add API
• Framework Integrations — native integrations with LangChain, LlamaIndex, CrewAI, AutoGen, and Claude Agent SDK
• Multi-Tenant Isolation — per-user and per-dataset memory boundaries with access control for multi-agent applications
• Observability & Auditability — built-in OTEL collector integration, audit trails, and interaction logging for production deployments

Use Cases

• 1Building persistent memory for AI coding assistants that survives across sessions with full codebase understanding
• 2Enterprise knowledge management — ingesting internal docs, wikis, and SOPs into a searchable knowledge graph
• 3Personalized AI agents that learn from user feedback and interactions over time
• 4Multi-agent platforms where specialized agents share a common knowledge base with tenant isolation
• 5RAG pipeline replacement for applications requiring relationship-aware retrieval rather than simple vector similarity