Build AI agents with persistent, structured graph memory
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Tracked since2026
0 reviews trackedThe Bottom Line
Entry price
Free plan available, paid tiers above
Biggest pro
Provides durable, shared memory for AI agents, improving their consistency and intelligence.
Biggest con
Requires integration and configuration with existing agent systems.
TL;DR - Cognee
- Provides open-source, persistent memory for AI agents.
- Transforms diverse data sources into structured graph memory.
- Enables agents to recall context and learn across sessions.
Pricing: Free plan available
Best for: Growing teams
What is Cognee?
Cognee is an open-source memory platform designed for AI agents, enabling them to capture context, transform it into graph memory, and recall it consistently across multiple sessions. It addresses the challenge of providing agents with durable, structured knowledge beyond immediate prompts by integrating various data sources like files, databases, APIs, and chat logs. Cognee parses, chunks, embeds, and tracks provenance for all ingested data.
The platform models raw data into graph memory by extracting entities, relationships, and domain rules, allowing agents to search by meaning rather than just keyword matching. This memory layer is compatible with various agent frameworks and custom runtimes, ensuring agents can read and write to a shared, persistent knowledge base. Cognee is suitable for individual developers, data and platform teams, and product engineers looking to build intelligent, context-aware agents that improve over time.
Pros & Cons
Pros
- Provides durable, shared memory for AI agents, improving their consistency and intelligence.
- Supports a wide range of data sources and agent frameworks, offering flexibility.
- Offers both open-source local development and scalable cloud/on-premise deployment options.
- Automatically generates and updates ontologies, keeping knowledge structures fresh.
- Ensures data security and compliance with options for private cloud or on-premise deployment.
Cons
- Requires integration and configuration with existing agent systems.
- The complexity of graph memory and ontology management may have a learning curve for new users.
- Scalability and advanced features are primarily available in paid tiers.
Preview
Key Features
Context Capture from diverse sources (files, databases, APIs, chat logs)Automated Parsing, Chunking, and EmbeddingProvenance Tracking for all ingested dataGraph Memory Modeling (entities, relationships, ontologies)Recall Tuning and Permissions ControlCompatibility with leading agent frameworks (e.g., LangGraph, OpenClaw)Local Development EnvironmentAutomated Ontology Generation and Updates
Pricing Plans
Pricing checked Jun 6, 2026
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
- 1 user
- Fully hosted on AWS, GCP, and Azure
- Comprehensive API endpoints
- Automated scaling and parallel processing
- Automatic updates
- 10,000 API calls included
Cloud (Team)
$200 / per month
- 2,500 documents or 2 GB of data included
- Everything in Developer
- 10 users
- Multi-tenant architecture
- Ability to group memories per user and domain
- Dedicated Slack channel
- 10,000 API calls included
On-Prem (Enterprise)
Custom
- Everything in Cloud
- On-prem or private cloud deployment
- Security, data isolation, and optimal latency
- Dedicated architecture review
- Premium Support Plan / SLA
- Access to AI FDE Engineers
- Roadmap prioritization
How Cognee's pricing compares
At $35/mo, Cognee is the most premium of its 4 direct competitors.
Entry paid plan, monthly. Pricing checked Jun 6, 2026.
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Cognee FAQ
How does Cognee transform raw data into a usable memory for AI agents?
Cognee captures context from various sources, then processes this raw data by parsing, chunking, and embedding it. It subsequently models this data into a graph memory by extracting entities, relationships, and domain rules, allowing agents to search and recall information based on meaning and context rather than just keyword matches.
What kind of AI agent frameworks or runtimes can integrate with Cognee's memory layer?
Cognee is compatible with various agent frameworks, including Claude Code, LangGraph, OpenClaw, and MCP-compatible clients. It also supports custom runtimes, allowing a broad range of agents to read and write to the same durable memory across sessions.
Can Cognee automatically update its knowledge structures as new data becomes available?
Yes, Cognee continuously updates its ontologies as data changes. This feature ensures that the knowledge structure remains fresh and aligned with the latest information without requiring manual rebuilds or taxonomy updates.
What are the deployment options for Cognee, and what are their primary benefits?
Cognee offers flexible deployment options: users can start locally with the open-source version, scale on Cognee Cloud for serverless operation, or deploy on-premise for dedicated infrastructure, full data control, and enhanced security. The cloud option provides scalability and performance, while on-premise ensures data isolation and compliance.
How does Cognee ensure the security and privacy of the data it manages?
Cognee ensures data security and privacy by encrypting data at rest and in transit. For enterprise clients, it supports air-gapped deployments on private clouds or on-premise, giving organizations complete control over their data and aiding in regulatory compliance like GDPR.
What is the purpose of the 'provenance' feature in Cognee's context capture?
The provenance feature in Cognee tracks the origin and history of captured context. This allows agents to inspect and reuse cited memory, ensuring transparency and enabling them to verify the source of information, which is crucial for building trustworthy and reliable AI systems.
How does Cognee's graph memory approach differ from traditional vector store searches for agents?
Unlike traditional vector stores that primarily rely on nearest chunk searches, Cognee's graph memory extracts entities, relationships, and domain rules. This enables agents to search by meaning and understand the connections between pieces of information, leading to more accurate and contextually relevant recall.
Can I customize the data model or ontologies within Cognee for specific domain needs?
Yes, Cognee allows for customization of both custom ontologies and custom data models. This flexibility enables users to tailor the memory structure to their specific domain rules and business needs, ensuring the agent's knowledge base is highly relevant and accurate.
Source: cognee.ai