cognee
Reliable LLM Memory for AI Applications and AI Agents
🛡️ AgentReady threat assessment
MAESTRO 7-layer threat model + OWASP AIVSS risk score for cognee, derived from its capabilities.
AIVSS 7.7 · High
View MAESTRO 7-layer threat model →These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.
Overview
Cognee implements scalable, modular ECL (Extract, Cognify, Load) pipelines that allow you to interconnect and retrieve past conversations, documents, and audio transcriptions while reducing hallucinations, developer effort, and cost.
Key features
- Modular: Cognee is modular by nature, using tasks grouped into pipelines
- Local Setup: By default, LanceDB runs locally with NetworkX and OpenAI.
- Vector Stores: Cognee supports LanceDB, Qdrant, PGVector and Weaviate for vector storage.
- Language Models (LLMs): You can use either Anyscale or Ollama as your LLM provider.
- Graph Stores: In addition to NetworkX, Neo4j is also supported for graph storage.
Use cases
- Memory for AI Agents
- Ontology definition
- Entity resolution
- Chatbot memory