CogneeAdaptive memory layer that helps AI agents learn from context over time.

4.8 (5)

Reviewed by Daniel Nikulshyn·Updated July 2026

Developer Tools AI Agents Knowledge Graph Memory Infrastructure Open Source Python SDK LLM Ops

Overview

Cognee is an open-source AI memory platform designed for AI agents. It provides a persistent long-term memory across sessions by ingesting data in any format and building a self-hosted knowledge graph. Cognee combines vector embeddings, graph reasoning, and cognitive-science-grounded ontology generation, making documents searchable by meaning and connected by evolving relationships. This platform is suitable for developers and organizations looking to unify data from various sources, enable domain knowledge in agents, and create reliable and trustworthy agents. Cognee offers features such as unified ingestion, graph and vector search, local operation, ontology grounding, multimodal capabilities, learning from feedback, context management, and cross-agent knowledge sharing. It also provides agentic user/tenant isolation, traceability, and audit traits. The platform supports multiple clients, including Python, Rust, and TypeScript, and is available as plugins for OpenClaw and Claude Code.

Key features

Knowledge graph based agent memory
Semantic and structured data ingestion
Python SDK for agent integration
Pluggable LLM and storage providers
Querying across past sessions and documents
Self-hosted or managed deployment options

Pricing

Model: Free
Category: MCP Servers
Rating: 4.8 / 5 (5)

Use cases

Long-term memory for AI agents

Give conversational agents persistent recall across sessions by storing interactions in a knowledge graph and retrieving relevant context on demand.

Context-aware RAG over documents

Ingest documents and structured data, then combine graph relationships with semantic search to deliver richer, more accurate retrieval than vector-only RAG.

Reduce hallucinations in LLM apps

Ground LLM responses in previously captured facts and relationships, cutting repetitive prompting and improving answer reliability over time.

Self-hosted memory layer for custom stacks

Use the Python SDK to plug Cognee into preferred LLMs, vector stores, and graph databases, with self-hosted or managed deployment for full control.

Pros & Cons

Pros

Combines graph and vector retrieval for richer context
Open-source with a flexible Python SDK
Works with multiple LLM and database backends
Helps reduce repetitive prompting and hallucinations

Cons

Requires technical setup and infrastructure knowledge
Graph-based memory adds complexity vs. plain vector DBs
Best results need tuning for each use case

Reviews

4.8

Average from 5 ratings.

Liam O’Connor

May 16, 2026

Does the job

Pretty happy overall. Pluggable LLM and storage providers just works and helps reduce repetitive prompting and hallucinations. but no dealbreakers — I'd recommend it to a friend without hesitating.

Carlos Mendoza

Mar 31, 2026

Does the job

Pretty happy overall. Querying across past sessions and documents just works and combines graph and vector retrieval for richer context. but no dealbreakers — I'd recommend it to a friend without hesitating.

Pierre Dubois

Jan 13, 2026

Years in this space

I've evaluated a lot of these over the years. What stands out here is self-hosted or managed deployment options — handled better than most — and combines graph and vector retrieval for richer context. Worth the time if this is your use case.

Devin Walker

Dec 13, 2025

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on knowledge graph based agent memory, and combines graph and vector retrieval for richer context caught me off guard. still, I'd recommend giving it a real trial.

Grace Okafor

Jul 30, 2025

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on knowledge graph based agent memory, and open-source with a flexible Python SDK caught me off guard. Requires technical setup and infrastructure knowledge is why this isn't a perfect score, still, I'd recommend giving it a real trial.