Cognee
What is Cognee?
Cognee is a memory layer for AI agents that ingests warehouses, vector stores, files, and APIs, then parses and organizes them into a managed world model for reusable context. Its Ingest, Reason, and Act flow includes Ontology, Managed store, and Permissions control, and it integrates with Claude Code, Cursor, LangGraph, and CrewAI. Plans run Free, Developer $35/month, Cloud (Team) $200/month, and On-Prem (Enterprise) custom.
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At a glance
- Cognee is best for AI teams who need persistent agent memory across messy, changing data sources.
- Free; Developer $35/mo; Cloud (Team) $200/mo; On-Prem (Enterprise) Custom
What does Cognee do?
Cognee turns scattered sources into a structured memory layer for agents by ingesting warehouses, vector stores, files, and APIs, then parsing, chunking, embedding, and organizing them into auto-extracted ontologies and a managed world model. Its Ingest, Reason, and Act flow is designed to keep memory usable across sessions, while recall tuning, permissions, and self-improvement help the system compound with each run. The result is a company brain that agents can query and reuse instead of rebuilding context from scratch. At scale, Cognee reports 5M+ SDK runs per month and 17.3k GitHub stars, and its benchmark page says it was tested on 24 HotPotQA multi-hop questions with 45 repeated runs per system. The product page shows millisecond responses, autoscaling compute, distributed graphs, and GDPR-compliant encryption at rest and in transit. Cognee is deployed on your own systems for full data control, and customers cited on the site include Bayer and DeepMetis.
Why use Cognee?
- It combines graph and vector memory, so agents can retrieve context with structure instead of keyword overlap.
- The same feature set can run in hosted cloud or on private infrastructure, which helps teams match deployment to policy.
- Auto-generated ontologies reduce manual modeling work and keep knowledge structures current as data changes.
- Fine-grained permissions and grouped memories support multi-user, multi-field setups without flattening everything into one store.
Who is Cognee for?
- AI engineers who want agents to remember context across sessions and tools.
- Data teams who need to turn warehouses, files, and APIs into structured knowledge.
- Platform teams who need scalable, permissioned memory with deployment flexibility.
- Enterprise builders who need data control, isolation, and private infrastructure options.
- Research teams who need reproducible evaluation and multi-hop reasoning support.
What are Cognee's key features?
Ingest
Load data from more than 28 sources and supported systems like Snowflake, Postgres, Slack, and Google Calendar to build memory workflows faster.
Reason
Use graph extraction, summarization, and vector search to turn raw data into structured memory that supports multi-hop reasoning and retrieval.
Act
Connect memory workflows to tools like Claude Code, Cursor, LangGraph, CrewAI, and Airflow so agents can trigger actions from stored context.
Ontology
Automatically generate ontologies and custom data models from your data, helping teams organize knowledge structures for consistent agent behavior.
Managed store
Store memories in a managed system with AWS, GCP, and Azure hosting, plus self-hosting options for private cloud or on-prem deployment.
Permissions control
Group memories by user and field with multi-tenant architecture and permissions controls, which helps teams isolate sensitive context across shared workflows.
Self-improvement
Run integrated evaluations and recall tuning to improve memory quality over time, backed by production metrics like 92.5% answer relevance.
Scalable
Handle larger workloads with automated scaling, parallel processing, and 10,000 API calls included, supporting teams that need steady throughput.
What does Cognee integrate with?
- Claude Code
- Codex
- OpenClaw
- Snowflake
- Postgres
- Slack
- LangGraph
- CrewAI
- Continue
- Hermes
- Cursor
- Google Calendar
- dbt
- Airflow
- Terraform
- AWS
- Google Cloud
- Qdrant
- dlt
- Neo4j
- Weaviate
- Modal
What are Cognee's use cases?
Agent memory for AI engineers
AI engineers use Cognee to give agents durable memory across sessions and tools, using Ingest to pull in prior chats, files, and app data, then Reason to surface the right context when a user returns. Act helps the agent turn that memory into follow-up actions instead of repeating questions.
Knowledge pipelines for data teams
Data teams use Cognee to turn warehouses, files, and APIs into structured knowledge, using Ontology to shape the data model and Managed store to keep it organized for downstream retrieval. They can then connect more than 28 data sources without stitching together a custom memory layer.
Permissioned memory for platform teams
Platform teams use Cognee to provide scalable, permissioned memory for internal apps, relying on Permissions control to separate access by user or field and Scalable to support growing workloads. This lets them ship shared memory infrastructure without sacrificing deployment flexibility.
Reproducible reasoning for research teams
Research teams use Cognee to support multi-hop reasoning and repeatable evaluation, combining Self-improvement with Reason to test whether outputs get better over time. That makes it easier to compare runs, validate hypotheses, and keep research memory consistent across experiments.
How does Cognee work?
- Connect your first data source in Ingest, then bring in documents, APIs, or warehouse tables so Cognee can start building memory from real inputs.
- Shape the knowledge layer with Ontology or Custom ontologies, and use the generated structure to organize entities, relationships, and retrieval paths.
- Store and govern the memory in Managed store, then apply Permissions control so teams, domains, or applications only see what they should.
- Run Reason and Act inside your agent workflow, using the API endpoints to retrieve context, answer with memory, and trigger the next step.
- Tune Recall tuning and Self-improvement with evaluations, then monitor results as Cognee scales across more data, users, and deployments.
How much does Cognee cost?
Free
Free- Build and run memory workflows with tasks and pipelines
- Auto-generate knowledge structures from your data
- Integrated evaluations
- More than 28 data sources supported
- Community support
Developer
$35/per month- 1,000 documents or 1 GB of data included
- Everything in Free, plus.
- 1 user
- Fully hosted on AWS, GCP and Azure
- API endpoints
- Automated scaling and parallel processing
- Automatic updates
- 10,000 API calls included
- Top-up packs available
- +1,000 docs (~1 GB), $35
- +15,000 docs (~15 GB), $750
Cloud (Team)
$200/per month- 2,500 documents or 2 GB of data included
- Everything in Developer, plus.
- 10 users
- Multi-tenant architecture
- Ability to group memories per user and field
- Dedicated Slack channel
- 10,000 API calls included
- Top-up packs available
- +1,000 docs (~1 GB), $35
- +15,000 docs (~15 GB), $750
On-Prem (Enterprise)
Custom- Everything in Cloud, plus.
- On-prem or private cloud deployment
- Security, data isolation and optimal latency
- Dedicated architecture review
- Support Plan / SLA
- Access to AI FDE Engineers
- Roadmap prioritization
Frequently asked questions
What is Cognee?
Cognee is a memory layer for AI agents that ingests warehouses, vector stores, files, and APIs, then parses and organizes them into a managed world model for reusable context. Its Ingest, Reason, and Act flow includes Ontology, Managed store, and Permissions control, and it integrates with Claude Code, Cursor, LangGraph, and CrewAI. Plans run Free, Developer $35/month, Cloud (Team) $200/month, and On-Prem (Enterprise) custom.
How much does Cognee cost? Is it free?
Cognee has a free plan, with paid tiers including Developer at $35/per month, Cloud (Team) at $200/per month, On-Prem (Enterprise) at Custom.
What is Cognee used for? Who is it for?
Cognee is used for Ingest, Reason, and Act. It's built for AI engineers, Data teams, and Platform teams.
Does Cognee have an API and what does it integrate with?
Cognee doesn't publish a public API. It integrates with Claude Code, Codex, OpenClaw, Snowflake, Postgres, and 17 more.
Editor's read
Check the data-volume ceiling on Developer and Cloud (Team): 1,000 documents or 1 GB on Developer, 2,500 documents or 2 GB on Cloud. If your memory corpus grows past those limits, you'll need top-up packs or the Enterprise deployment path.
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