Cognee

Every time an agent starts cold, it relearns everything. Cognee breaks that cycle by acting as a persistent memory layer: ingest data once via adapters that connect warehouses, documents, chats, and APIs, and Cognee converts that input into a graph-structured memory store. Agents then call a single recall API and get back cited answers — facts tied to their source — rather than unconstrained generation.

The graph memory architecture is the core differentiator. Where a vector store returns similar chunks, Cognee surfaces entity relationships — connections between people, deals, decisions, and documents that a flat embedding index loses. The Knowunity case cited on the vendor page illustrates this directly: SQL queries grew unmanageable and embeddings returned sparse results; a graph-backed POC on 40,000 students shipped in two days.

Cognee fits best when your agent touches a bounded domain repeatedly — coding assistants that need to remember past fixes, support agents that must cite regulation, sales tools that track account history. The friction point is ontology customization: the vendor describes custom data models and permissions controls as available features, but teams building highly specialized knowledge graphs will spend meaningful time configuring graph structure before recall quality matches their domain. Teams that need a simple, fast vector cache without graph overhead are carrying architecture they won’t use.

Integration surface includes first-party connectors for Claude Code, Codex, LangGraph, and an MCP server for any compatible agent, plus a self-hosted path via pip install — no new infrastructure required for local runs. Cognee Cloud adds a free tier capped at 1M tokens and one workspace, with usage-based tiers above that. The project carries 27.7k GitHub stars and reports over 5M SDK runs per month, per the vendor page.