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Cognee
Summary
Agents that can't remember what they did last session aren't agents — they're stateless scripts you have to babysit. Cognee exists to fix that: an open-source memory layer that captures what your agents know, structures it as a graph, and makes it recallable across every future run.
The core workflow is three lines: install via pip, point Cognee at a data source, and your agents start recalling cited facts instead of hallucinating from scratch each session. Graph-structured memory means relationships between entities survive retrieval — not just keyword matches. First-party integrations cover Claude Code, Cursor, LangGraph, and an MCP server, so compatible agents read and write memory without custom glue code. The ceiling appears when your ontology needs get specific: custom data models and permissions controls are available, but tuning graph structure for a niche domain requires real configuration work. Teams that need a drop-in vector store with zero graph overhead typically reach for a simpler solution.
Bottom line: Pick Cognee when your agent needs to recall past decisions and cited domain facts across sessions — plan for a configuration investment when your knowledge graph needs domain-specific ontologies that the defaults don't cover.
Hosted & API Pricing
The model is free to self-host. These are the creator's hosted/API options.Free
1 workspace, 1M tokens
- Unlimited users
- Unlimited API calls
- Agentic integrations
Pricing may have changed since last verified. Check the official site for current plans.
Pricing Plans
Usage-Based- Free Tier
- 1 workspace and 1M tokens included
Free
Free forever for starting agents with memory
- 1 workspace
- 1M tokens included
- Unlimited users and API calls
- Agentic integrations
Standard
Usage-based pricing for scaling
Enterprise
Dedicated support, private cloud, SLAs
View full pricing on cognee.ai →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- Graph-structured memory preserves relationships between entities across sessions, so agents recall how a decision connects to a document or account — not just that the document exists.
- Single recall API with cited answers, which means agents stop hallucinating unsourced facts and you get traceable outputs your team can audit.
- Self-hosted via pip with no new infrastructure required, so a solo developer can give a coding agent durable memory in an afternoon without standing up a separate service.
- First-party integrations for Claude Code, Cursor, LangGraph, and an MCP server, so agents that already support MCP read and write Cognee memory without custom adapter code.
- Adapters that unify warehouses, docs, chats, and APIs into one recallable layer, which means you connect a source once and every agent downstream can query it — no per-agent data wiring.
Cons
Sign in to edit- Custom ontologies and domain-specific graph structures require real configuration work before recall quality reflects your domain — teams building specialized knowledge graphs (legal codes, industrial manuals) hit this before their first production deployment and spend days tuning rather than shipping.
- Permissions and multi-workspace controls are present but the docs describe them as configuration-layer features, not zero-setup defaults; teams with strict data isolation requirements between agent instances will need to explicitly model and test access boundaries before they can trust the setup in a customer-facing context.
- Teams that need a fast, flat vector store with sub-second retrieval and no graph overhead are paying the architectural cost of a system built for relationship recall — at that point they switch to a purpose-built vector database like Qdrant or Weaviate and manage session state themselves.
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About
- Platforms
- Python, Docker, self-hosted, on-prem, Cognee Cloud
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-07-13T13:23:41.440Z
Best For
Who it's for
- Developers building agentic applications
- Teams needing self-hosted memory layers
- Vertical agents requiring cited domain knowledge
What it does well
- Journaling and personal second brain
- Sales deal intelligence and account querying
- Technical documentation and knowledge bases
- Long-term memory for coding agents
Integrations
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Frequently Asked Questions
- Is Cognee free?
- Cognee has a permanent free tier alongside paid upgrades. You can keep using a baseline version indefinitely without paying.
- Is Cognee open source?
- Yes. Cognee is open source.
- Does Cognee have an API?
- Yes. Cognee exposes a developer API. See the official documentation at https://cognee.ai for details.
- Can I self-host Cognee?
- Yes. Cognee supports self-hosting on your own infrastructure.
- What platforms does Cognee support?
- Cognee is available on: Python, Docker, self-hosted, on-prem, Cognee Cloud.
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Curated lists that include this category
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.