Cognee

The Panel's Take

Cognee is an open-source knowledge engine that gives AI agents persistent, learning memory without requiring you to architect a graph database from scratch. Under the hood it combines a vector store, a graph database (Neo4j), and semantic indexing into a single interface backed by four simple operations: remember, recall, forget, and improve. The magic is in the auto-routing recall layer. Rather than forcing developers to choose between similarity search and structured graph traversal, Cognee analyzes the query and picks the optimal strategy automatically. Session memory syncs to permanent graphs in the background, so agents accumulate knowledge across runs without any manual persistence logic. At 15k stars and growing fast, Cognee is quietly becoming the memory layer developers reach for when building agents that need to reference past work — think support bots, research pipelines, coding agents that shouldn't forget what a codebase looks like. It deploys on PostgreSQL with pgvector, integrates with OpenAI and Claude, and ships with Docker configs for Railway, Fly.io, and Render.

Cognee verdict: SHIP 🚀

3 ships · 1 skip from the expert panel

Full review: shiporskip.io/tool/cognee-knowledge-engine-ai-agent-memory-graph-vector-python-2026

Compare Cognee with Others

Builder

Ship

“Six lines of code for persistent knowledge graph memory across agent sessions? That's a genuinely useful abstraction. The auto-routing recall that picks the right search strategy (vector vs. graph) without manual tuning removes a real pain point. PostgreSQL + pgvector backend means you're not locked into a proprietary store. I'm integrating this into my next agent project.”

Skeptic

Skip

“Another 'knowledge graph for AI' library in a space already crowded with Mem0, LlamaIndex memory, LangChain's entity store, and MemGPT. The 'six lines of code' promise falls apart when you need custom ingestion pipelines or production-grade tenant isolation. PostgreSQL + Neo4j + vector store is three moving parts for what often just needs a good retrieval strategy. Wait for the ecosystem to consolidate.”

Futurist

Ship

“Memory is the missing layer in the agent stack. Cognee's cognitive science-inspired architecture — remember, recall, forget, improve — maps remarkably well to how useful agents should work. The feedback loop that improves future responses is the critical piece. As agents run longer and longer tasks, systems like this become the connective tissue that makes them actually reliable.”

Creator

Ship

“If I'm building a research assistant or a content pipeline that needs to reference past projects, having persistent memory that actually understands relationships (not just semantic similarity) changes the game. The fact it supports multimodal ingestion means I can throw PDFs, notes, and transcripts at it without preprocessing gymnastics.”