cognee

What cognee does

Cognee builds an open-source memory platform for AI agents. It captures context and turns it into graph memory, allowing agents to recall information across sessions. The platform can be started locally with open source and scaled on Cognee Cloud. It integrates with various AI agents and frameworks, providing persistent, self-improving memory. Cognee offers solutions for local development, connecting data sources, and shipping production-ready agents that understand specific domains. The company emphasizes its open-source nature, security, and scalability, offering both cloud-managed and on-premise deployment options.

Products

• Cognee SDKA memory-native API for agents with four core verbs: remember, recall, forget, and improve. It allows for custom graph models and integrates with various agents and frameworks. It can be deployed on a serverless cloud or privately on one's own infrastructure.
• Cognee CloudA serverless knowledge graph for agents that is fully managed. It allows agents to connect tools, share memory across sessions, and improve itself. It offers multi-tenant workspaces and custom deployment options.
• Cognee Rust EngineA ground-up rewrite of the memory pipeline in Rust, delivering lower latency, higher throughput, and a smaller memory footprint. It is designed for faster recall and can be embedded in-process for various deployment environments.

Key capabilities

• Open-source memory platform
• Graph memory for agents
• Cross-session recall
• Local and cloud deployment options
• Adapters for various data sources
• Memory API (remember, recall, forget, improve)
• Self-improving memory
• Custom ontologies and data models
• Permissions control
• Scalable and performant infrastructure
• GDPR-compliant
• Data encryption at rest and in transit
• Cost-effective query pricing
• Session management with full lifecycle
• Multi-scope memory support (company/user/agent)
• Auto-recall and auto-index for memory files
• 14 search types including semantic vector search and graph reasoning
• Embedded Rust engine for in-process deployment
• Automatically generated ontologies
• Human-like correctness and accuracy in answers

Use cases

• Journaling
• Deal intelligence
• Research
• Giving an agent long-term memory
• Searchable notes, decisions, and life
• Sales & deal intelligence for ICPs and accounts
• Technical and industrial knowledge bases
• Memory for coding agents to recall past work and decisions
• AI chatbot memory
• Agentic research memory
• Customer-facing agents for cited facts and domain rules
• Personalized support for customers
• Hypothesis generation from preclinical literature
• Agentic teaching assistants for personalized education programs
• Persistent memory for OpenAI Agents SDK
• Knowledge accumulation by pre-loading documents
• Context-aware assistance across work sessions
• Multi-tenant applications with isolated data
• Multi-agent workflows for knowledge sharing
• Agent handoffs between specialized agents
• Persistent memory for OpenClaw agents
• Persistent memory for Hermes Agent
• Building custom knowledge bases of building codes and regulations

AI approach

Cognee builds an open-source memory platform for AI agents, turning relational data, documents, and system context into graph memory that agents can recall across sessions. It focuses on persistent, self-improving memory for agents, using knowledge graphs and vector search. The platform offers an SDK and APIs for integration with various agent frameworks and LLMs. It also has a Rust engine for improved performance.

Tech named: knowledge graphs, vector search, embeddings, Rust engine, Python pipeline, GPT-5.5, gpt-oss-120b

Industries served

• Software Development
• Education
• Healthcare
• Research & Development

What it says sets it apart

• Open-source core with auditable code
• Memory that improves with use, reinforcing important information and pruning irrelevant data
• Unified memory API across various agents and frameworks
• Cost-effective at scale, with query costs that stay flat compared to LLM token costs
• Flexible deployment options: local, managed cloud, on-premise, and embedded
• Strong focus on data privacy and security, including GDPR compliance and EU-based operations
• Fast recall performance with the Rust engine, offering lower latency and higher throughput
• Automatically generated and continuously updated ontologies
• Multi-scope memory for granular control over shared and private knowledge
• Comprehensive session management and cross-session persistence

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.