Open-source memory layer for AI agents — turns your data into a queryable knowledge graph.

Cognee is the open-source memory layer that fixes the most common failure mode of AI agents: forgetting. While naive RAG treats your data as disconnected chunks, Cognee builds a real knowledge graph with entities, relationships, and temporal validity. Released in 2024, it became the default memory layer for serious AI agent builders — the kind that need to remember what a user said three sessions ago.

Who it's for: Engineers building AI agents that need persistent, queryable memory. Drop-in replacement for naive RAG that adds knowledge-graph relationships, temporal reasoning, and entity resolution on top of vector search.

Key features

Graph Knowledge graph layer

On top of vector embeddings, Cognee builds a real knowledge graph (using NetworkX under the hood). You get entity-level relationships, not just similarity chunks. 'Who founded Anthropic' actually returns a real entity graph.

Pipelines ETL for LLM memory

Structured add() and cognify() pipelines extract entities, build relationships, and store both. Then search() returns graph-aware results, not just cosine similarity.

Temporal Time-aware

Tracks when facts were valid. 'What was the CEO of X in 2024' works correctly even if the answer changed in 2025. Naive RAG always returns the latest chunk.

Sources Multi-source

Pull from text files, PDFs, Notion, Slack, GitHub, databases, and 30+ other connectors. Unified query interface across all of them.

The honest take

✓ What works

Knowledge-graph layer fixes the biggest weakness of naive RAG (entity confusion)
Open source and self-hostable — no vendor lock-in, no per-query costs
Temporal reasoning is genuinely useful for long-running agents
Active development, frequent releases, responsive maintainers
Plays nicely with LangChain, LlamaIndex, and direct OpenAI/Anthropic clients

✗ What doesn't

Graph queries are slower than pure vector search for simple lookups
Documentation is improving but still rough in places
Setup requires more thought than just 'chuck everything into a vector DB'
Smaller community than LangChain or LlamaIndex (though growing fast)

Verdict

If you're building a serious AI agent with memory — not just a chatbot — Cognee is the open-source default in 2026. Naive RAG works for FAQ bots; Cognee works for agents that actually need to reason across data they've seen. Start with the quickstart, then layer in temporal queries once you're comfortable.

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