Last updated 09-01-2026
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Cognee
Cognee turns documents, chats, tickets, and API data into graph memory language model agents can recall across sessions. It builds linked entity graphs from that context so agents stop restarting from scratch each run. You can connect Slack, GitHub, or Linear so coding agents and support bots share one permission-aware company brain.
Plain RAG returns similar text chunks. Cognee pairs vector search with knowledge graphs and auto-generated ontologies, so recall pulls connected entities and cited facts rather than isolated snippets. The SDK centers on four verbs, remember, recall, forget, and improve, and the same surface ships over HTTP and MCP for Claude Code, Codex, and OpenClaw.
Platform teams use it for coding agent memory, GraphRAG pipelines, deal intelligence, and customer-facing agents that need grounded answers. Run it with pip locally, self-host in Docker or on-prem, or move to Cognee Cloud when you want managed scale. The project reports 30.4k GitHub stars and 5M+ SDK runs per month, with production deployments at Bayer and Knowunity.
pip install cognee connects Claude Code, Codex, or any MCP client in minutes
remember, recall, forget, and improve as the core API across SDK, HTTP, and MCP
30.4k GitHub stars and 5M+ SDK runs per month listed on the homepage
Hybrid graph and vector memory links entities across long conversations and sources
Free Cognee Cloud tier includes 1M tokens and one workspace at $0 per month
Ingest from Slack, Notion, Linear, Google Drive, S3, and code repos into one recall layer
BEAM 100K benchmark shows Cognee scoring 0.79 on the SDK results page
Open-source core lets you run the full memory engine locally or self-hosted without vendor lock-in.
Graph plus vector memory with auto-generated ontologies retrieves connected context instead of isolated chunks.
Strong adoption signals with 30.4k GitHub stars and a 0.79 BEAM 100K benchmark score on the SDK page.
MCP and SDK integrations fit existing agent workflows like Claude Code, Codex, and OpenClaw.
Free cloud tier includes 1M tokens and one workspace with no credit card required.
Standard plan workspace add-ons cost each per month on top of token usage.
Enterprise BYOC engagements require a sales conversation for dedicated support and SLAs.
Self-hosting means operating the memory engine and connectors on your own infrastructure.
Is Cognee open source?
Yes. Cognee publishes an open-source SDK on GitHub under topoteretes/cognee, and you can run the full memory engine locally or on your own infrastructure for free. Cognee Cloud is a separate hosted option with usage-based pricing.
How is Cognee different from a vector database?
A vector database returns similar chunks. Cognee combines vectors with graph relationships, auto-generated ontologies, and memory operations so agents retrieve connected context across sources rather than isolated snippets.
Can I run Cognee locally or self-host it?
Yes. Cognee installs with pip for local development and supports self-hosted deployments in Docker or private infrastructure. The SDK page also highlights air-gapped enterprise deployment with encryption at rest and in transit.
Which agent tools integrate with Cognee?
Cognee integrates with MCP clients including Claude Code and Codex, plus OpenClaw, Hermes, Cursor, and LangGraph as listed on the homepage and SDK page. The same memory API is exposed through the SDK, HTTP, and MCP.
How does Cognee Cloud pricing work?
Cognee Cloud has a Free plan with one workspace and 1M tokens included at per month. The Standard plan charges .50 per 1M tokens processed plus per additional workspace per month. Enterprise BYOC engagements are available by contacting the team.
Is Cognee GDPR compliant?
Cognee states on its SDK page that the platform is fully GDPR compliant, with data encrypted at rest and in transit for air-gapped enterprise deployment. The site footer also displays heyData EU AI Act and GDPR compliance seals.
What is a company brain in Cognee?
In Cognee, a company brain is a permission-aware memory layer that unifies docs, chats, tickets, code, and agent runs. Teams and agents query the same recall surface instead of hunting across disconnected tools.