Cognee
Open-source AI memory framework that builds knowledge graphs from unstructured data (documents, conversations, web pages) for agent retrieval. Cognee extracts entities, relationships, and facts from text and constructs a queryable knowledge graph. Enables agents to reason over structured knowledge rather than just semantic similarity search — combining graph traversal with vector search.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Open source enables auditing. Self-hosted keeps data in your infrastructure. LLM API keys for extraction need secure management. Early-stage project — security posture still maturing.
⚡ Reliability
Best When
Your agent needs to reason over interconnected facts and relationships — not just similarity search — particularly for knowledge bases with complex entity relationships.
Avoid When
You need simple document retrieval without relationship reasoning — standard RAG with vector search is faster to implement and cheaper to operate.
Use Cases
- • Build agent knowledge bases from documents and conversations with automatic entity extraction and relationship linking
- • Enable agents to reason over connected facts rather than simple similarity search — 'what caused X?' or 'what is related to Y?'
- • Process agent conversation history into a persistent knowledge graph that grows and connects over time
- • Ingest multiple data sources (PDFs, URLs, databases) into a unified knowledge graph for agent cross-document reasoning
- • Implement graph-augmented RAG pipelines where agents traverse entity relationships for more precise retrieval
Not For
- • Simple document Q&A without relationship reasoning — LlamaIndex or LangChain with vector search is simpler and sufficient
- • Real-time event processing — Cognee is designed for knowledge ingestion and retrieval, not event streaming
- • Teams without graph database infrastructure — Cognee requires Neo4j, FalkorDB, or NetworkX as the graph backend
Interface
Authentication
Cognee Cloud uses API key authentication. Self-hosted has configurable auth. SDK initializes with API key or self-hosted URL. LLM API keys (OpenAI, Anthropic) required separately for entity extraction.
Pricing
Open source framework (Apache 2.0) with self-hosting option. Cognee Cloud managed service pricing not yet fully public. Self-hosted costs include LLM API calls (for extraction), graph DB, and vector DB.
Agent Metadata
Known Gotchas
- ⚠ Requires separate graph database (Neo4j, FalkorDB, or in-memory NetworkX) AND vector database — infrastructure setup is non-trivial
- ⚠ Entity extraction uses LLM calls — extraction quality depends heavily on LLM model choice and prompt configuration
- ⚠ Knowledge graph ingestion is slower than vector-only RAG — expect seconds to minutes per document depending on length
- ⚠ Graph schema evolves during ingestion — agents must handle schema migration when upgrading Cognee versions
- ⚠ Early-stage project with rapid API changes — pin version carefully and check changelog on upgrades
- ⚠ Query reasoning quality is higher than vector search but less predictable — complex graph queries may produce unexpected traversal paths
Alternatives
Full Evaluation Report
Detailed scoring breakdown, competitive positioning, security analysis, and improvement recommendations for Cognee.
Scores are editorial opinions as of 2026-03-06.