Cognee
Open-source graph-memory layer that gives AI agents persistent, queryable context across sessions.
Pick Cognee if you're building agents that need durable, structured memory across sessions and a flat vector store is failing you on entity-rich data.
Skip it if you just need basic document retrieval for a single chatbot — a standard vector DB will be simpler and cheaper.
Cognee is an open-source memory platform for AI agents that converts raw context (documents, chats, APIs, warehouses) into a structured knowledge graph the agent can recall across sessions. Instead of bolting a vector store onto an LLM and hoping retrieval works, Cognee builds an ontology-aware graph with adapters for common data sources and exposes it through an SDK and an MCP server so multiple agents can share one memory layer.
It is aimed at developers building agentic systems who have outgrown naive RAG: solo builders, AI researchers, and product teams shipping customer-facing agents that need to remember users, projects, or domain entities. The Hobby tier is free with 1M tokens/month, Growth is $5/workspace/month plus token usage, and Enterprise is custom. The repo (17.5k+ stars) means you can self-host the whole thing if you don't want to touch their cloud.
Cognee is model-agnostic and works with Claude, OpenAI, and other LLM providers for the extraction and embedding steps, and it ships custom ontologies, permissioning, and governance controls for teams that care about who can read what. The trade-off versus a plain vector DB is real complexity — you're maintaining a graph, not a flat index — and the product is still maturing, so expect rough edges.
Cognee is one of the more thoughtful entries in the agent-memory space, treating context as a graph rather than a bag of embeddings. The open-source core plus MCP support make it a serious option for teams who want to own their memory layer, though you'll pay for that power with extra moving parts.
— The AI Tool Bible editorial team
Pros
- ✅ Open source and self-hostable with a sizable GitHub community
- ✅ Graph-based memory beats flat vector RAG for entity-heavy domains
- ✅ MCP server makes it easy to plug into Claude Desktop and agent frameworks
- ✅ Generous free tier (1M tokens/month) for experimentation
- ✅ Adapters for warehouses, docs, chats, and APIs out of the box
Cons
- ⚠️ Graph memory adds operational complexity vs. a plain vector store
- ⚠️ Still a young product; ontologies and governance features are evolving
- ⚠️ Token-based pricing on top of LLM costs can compound at scale
Use cases
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