Cognee

Tier 2: Persistent (knowledge-scoped)

Knowledge graphs for AI that needs to connect the dots

cognee.ai ↗ · ~12,000 GitHub stars · $7.5M seed (Feb 2026)

Our take

Cognee is what you reach for when you actually need AI to understand relationships between things, not just retrieve similar text. Their HotPotQA benchmark score of 0.93 (human-level) for multi-hop reasoning is real. The modular pipeline design means you can plug in your own extractors and ontologies. The catch: the "6 lines of code" demo gets you a default pipeline that works for generic use cases. Your actual domain (legal contracts, medical records, codebases) will need custom ontology work. Budget time for that.

How it works

Knowledge graph + vector hybrid with modular ECL (Extract-Cognify-Load) pipelines. Supports 30+ data source connectors. Builds structured knowledge representations from unstructured data.

When to use Cognee

  • + Applications that need multi-hop reasoning, connecting facts across multiple documents to answer complex questions
  • + Enterprise knowledge management with diverse data types (PDFs, audio, conversations, code)
  • + Teams that want full control over the knowledge pipeline and can invest in customization

When to skip it

  • − Simple chatbot memory or user preferences (too much machinery)
  • − Teams that need a plug-and-play SaaS (Cloud offering is new)
  • − Projects where latency under 200ms is critical

What it does better than everything else

Multi-hop reasoning across connected knowledge. When you need to answer "given what we know from documents A, B, and C, what's the implication for X?", Cognee produces the most accurate results in the space.

MCP support

Supported. Full native MCP server (cognee-mcp) supporting Streamable HTTP, SSE, and stdio transports. Multiple AI agents can share a single memory endpoint.

Why MCP matters: 5 MCP servers every engineering team should run →

Pricing

Free

Fully open source (Apache 2.0, Community edition)

Paid

Core/Enterprise: contact sales. On-prem deployment available.

The gotcha nobody mentions

The default pipeline works great for demos but won't capture domain-specific relationships your application needs. Developers often build an impressive proof-of-concept with the defaults, then discover they need significant custom work to get production-quality results for their specific domain.

"Early SDK versions showed the idea worked but revealed areas needing improvement — the system wasn't parallelized, making performance slow, updates were fragile, and customization was limited."

Frequently asked

How does Cognee compare to Mem0? +
Different tools for different jobs. Mem0 is fastest for simple per-user memory (148ms latency). Cognee is strongest at multi-hop reasoning across complex, connected knowledge. Cognee scores 0.93 on HotPotQA; Mem0 excels at latency and throughput.
Is Cognee production-ready? +
Getting there. 70+ companies are running it in production, and pipeline volume grew 500x in 2025. The Cloud offering is new. The open-source version is mature enough for teams willing to self-host.
Do I need to build my own knowledge graph schema? +
Not initially. The default ECL pipeline handles common use cases. But for domain-specific applications (legal, medical, engineering), custom ontology work significantly improves quality.

Related reading

→ The 3 types of AI memory → The amnesia problem

Also in this space

Mem0

The fastest path from zero to deployed memory

Zep

Temporal knowledge graph for agents that need to know when

Letta

Agents that manage their own memory (formerly MemGPT)

Cognee is Tier 2 — persistent (knowledge-scoped) memory.

It solves real problems for individual agents and users. But if your team needs shared, always-current memory that works across Cursor, Claude, and every other AI tool simultaneously — that's a different architecture entirely.

We're building that with Knowledge Plane. Join the beta →