Cognee
Knowledge graph memory layer for AI agents, with 28+ source connectors built in
What is Cognee?
Cognee is a memory layer for AI agents that ingests data from 28+ sources and auto-builds a knowledge graph agents can query. It plugs into existing runtimes like Claude Code, LangGraph, and MCP without requiring data migration. Bought by engineering teams who need structured agent memory but don't want to assemble a graph DB + vector store + ETL pipeline themselves.
Tools for building, hosting, testing, observing, connecting, and giving memory or computer access to AI agents.
See the full Agent Infrastructure guide to compare more tools, buyer criteria, and related workflows.
Use cases to evaluate
Building a company-wide agent that answers across Notion, Slack, and warehouse data
Adding persistent memory to a Claude Code or LangGraph agent
Replacing a hand-rolled RAG + graph stack with one managed service
Shipping domain-specific agents (legal, support, sales) that improve with use
Fit to evaluate
Solo developers prototyping agents with MCP
Platform teams unifying scattered data sources for agents
Product engineers shipping vertical AI products
Founders who need agent memory without standing up a graph DB
Business fit
Right for you if you're building agents that need to recall across structured warehouse data, APIs, and documents, and you want a graph-shaped memory rather than just vector similarity. Skip if a single vector store like Pinecone or pgvector already covers your retrieval needs, or if you have under a few hundred documents. The MCP/Claude Code integration is the strongest reason to pick this over rolling your own.
How to evaluate Cognee
Use this category when a business wants agents that do work across tools, APIs, browsers, and data sources.
Confirm the exact workflow
Map Cognee to one concrete workflow first, such as building a company-wide agent that answers across notion, slack, and warehouse data. Avoid buying before the owner, trigger, output, and success metric are clear.
Check category fit
Compare tool-calling, memory, browser automation, evals, observability, and deployment controls.
Compare practical alternatives
Shortlist Cognee against Orgo, Browser Use, Browserbase so the decision is based on fit, effort, and workflow ownership rather than brand recognition alone.
Validate cost and rollout effort
Free plan available. Developer $35/mo (1,000 docs or 1 GB, 10,000 API calls). Cloud Team $200/mo (2,500 docs or 2 GB, up to 10 users). Document add-ons from $35/+1,000 docs to $750/+15,000 docs. On-Prem Enterprise is custom. Also confirm implementation time, support needs, and whether the technical setup matches your team.
Compare Cognee with alternatives
Use this quick comparison before booking demos or moving data into a new system.
| Primary workflow | Building a company-wide agent that answers across Notion, Slack, and warehouse data, Adding persistent memory to a Claude Code or LangGraph agent |
|---|---|
| Best-fit team | Solo developers prototyping agents with MCP, Platform teams unifying scattered data sources for agents |
| Implementation effort | Technical setup and maintenance profile |
| Pricing check | Free plan + paid plans |
| Closest alternatives | OrgoBrowser UseBrowserbaseHyperbrowser |
Cognee pricing
| Model | Free plan + paid plans |
|---|---|
| Snapshot | Free plan available. Developer $35/mo (1,000 docs or 1 GB, 10,000 API calls). Cloud Team $200/mo (2,500 docs or 2 GB, up to 10 users). Document add-ons from $35/+1,000 docs to $750/+15,000 docs. On-Prem Enterprise is custom. |
| Checked |
Common questions about Cognee
What is Cognee?
Cognee is a memory layer for AI agents that ingests data from 28+ sources and auto-builds a knowledge graph agents can query. It plugs into existing runtimes like Claude Code, LangGraph, and MCP without requiring data migration. Bought by engineering teams who need structured agent memory but don't want to assemble a graph DB + vector store + ETL pipeline themselves.
What is Cognee used for?
Common use cases: Building a company-wide agent that answers across Notion, Slack, and warehouse data; Adding persistent memory to a Claude Code or LangGraph agent; Replacing a hand-rolled RAG + graph stack with one managed service; Shipping domain-specific agents (legal, support, sales) that improve with use.
How much does Cognee cost?
Free plan available. Developer $35/mo (1,000 docs or 1 GB, 10,000 API calls). Cloud Team $200/mo (2,500 docs or 2 GB, up to 10 users). Document add-ons from $35/+1,000 docs to $750/+15,000 docs. On-Prem Enterprise is custom.
Who is Cognee best for?
Cognee fits Solo developers prototyping agents with MCP, Platform teams unifying scattered data sources for agents, Product engineers shipping vertical AI products, Founders who need agent memory without standing up a graph DB. Right for you if you're building agents that need to recall across structured warehouse data, APIs, and documents, and you want a graph-shaped memory rather than just vector similarity. Skip if a single vector store like Pinecone or pgvector already covers your retrieval needs, or if you have under a few hundred documents. The MCP/Claude Code integration is the strongest reason to pick this over rolling your own.
What are alternatives to Cognee?
Common alternatives to Cognee include Orgo, Browser Use, Browserbase, Hyperbrowser, Steel, Anchor Browser.