Agentifact assessment — independently scored, not sponsored.
Cognee
Knowledge graph memory for agents. Stores and retrieves structured agent memories efficiently.
Solid choice for most workflows
Your AI agents forget critical context across sessions, leading to repetitive errors and unreliable retrieval like RAG's 40% failure rate.
90% accuracy vs RAG's 60%; graphs evolve smarter over time with Memify Pipeline; handles text/images/audio but requires LLM for extraction—fast for small datasets, scales with DB choice.
You need to inspect and debug agent memory as a structured map, not a black-box vector store.
Clear network graphs reveal data stories; efficient for large KBs by filtering subnetworks; quirky async projections need tuning for huge graphs.
Cognee crushes RAG with graph-structured memory for 90% accuracy and deeper reasoning vs RAG's flat 60% vector similarity.
Pick Cognee when agents need persistent, self-improving context and relationship-aware retrieval.
Stick to RAG for quick, simple text chunk retrieval without graph overhead.
LLM-dependent graph extraction
Relies on LLMs for entity/relation extraction during cognify, introducing cost and potential hallucination errors in complex data.
Trust Breakdown
What It Actually Does
Cognee gives AI agents a persistent memory system that stores information as a knowledge graph, so they can remember past conversations and retrieve relevant context across multiple interactions instead of starting fresh each time.
Knowledge graph memory for agents. Stores and retrieves structured agent memories efficiently.
Fit Assessment
Best for
- ✓memory-storage
- ✓knowledge-retrieval
- ✓data-analysis
- ✓knowledge-graph
Score Breakdown
Protocol Support
Capabilities
Governance
- permission-scoping
- user-isolation