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Give your AI a permanent memory of your business. A course for people who use ChatGPT or Claude daily.Compound Context
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Cognee

Free · From $25/mo

Cognee is an open-source AI memory engine that gives AI agents a smart, persistent memory. It turns raw data into a living knowledge graph, helping AI understand, reason, and adapt. This makes AI tools more accurate and trustworthy, reducing errors by providing factual and verifiable information.

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About Cognee

Who It's For

Cognee helps developers create smart AI agents and copilots that need good memory and can learn. It's for businesses, even in strict industries, who need AI to give accurate, fact-checked answers from lots of complex data, like many documents.

What You Get

You get an open-source AI memory system that uses knowledge graphs and vector search. It learns from feedback, updates itself, and helps AI understand data connections. This gives precise answers, reducing AI mistakes and creating reliable, specialized AI tools.

How It Works

Cognee turns your raw data into a "living knowledge graph." It takes in your information, then smartly breaks it down, building connections between concepts. When AI asks a question, Cognee searches this memory to find the most accurate and relevant answers.

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Compound Context

Give your AI a memory

Build one knowledge base all your AI tools draw from, kept current with your review.

Course

Features & Capabilities

🧠 Core AI Memory & Reasoning

Persistent AI Memory

Provides agents with graph and vector memory for understanding, reasoning, and adaptation.

Retrieval & Reasoning Core

Serves as the central component for an agent's data retrieval and complex reasoning capabilities.

Dynamic Knowledge Graph Creation

Automatically transforms raw data into a living, interconnected knowledge graph.

⚙️ Adaptive Intelligence

Self-Learning & Auto-Tuning

Continuously learns from feedback, updates concepts and synonyms, and refines answers over time.

Multi-Step Task Execution

Enables agents to perform complex multi-step tasks with clear, auditable explanations.

Adaptive Copilot Enablement

Empowers the creation of domain-specific copilots that learn and adapt based on interactions.

🔗 Platform & Integration

Unified Knowledge Platform

Replaces multiple custom knowledge graphs and vector stores with a single, consolidated platform.

Seamless Integration

Designed for easy integration with existing technology stacks and current solutions.

Use Cases

Empowering Personalized Customer Experiences

Businesses often struggle to provide truly personalized support due to fragmented customer data and a lack of contextual understanding. Cognee unifies diverse customer information into a dynamic knowledge graph, enabling AI agents to offer precise, context-aware, and highly personalized interactions that proactively address individual customer needs.

B2C, E-commerce, SaaS, Education TechFor: Customer Support Teams, Product Managers, CRM Administrators

Accurate and Compliant Document Intelligence for Regulated Sectors

Organizations in regulated industries face the challenge of extracting trustworthy and cited answers from vast, complex document repositories for compliance and decision-making. Cognee transforms these documents into a verifiable knowledge graph, allowing AI systems to provide precise, fact-based responses that significantly reduce inaccuracies and support regulatory adherence.

Financial Services, Government, Legal, HealthcareFor: Compliance Officers, Legal Teams, Risk Management Analysts, Policy Analysts

Developing Self-Improving, Domain-Specific AI Copilots

Building AI copilots that deeply understand specific domains, learn from feedback, and adapt to evolving information requires a sophisticated memory architecture beyond simple vector stores. Cognee provides a persistent, hybrid graph-vector memory that enables AI agents to continuously learn, update their knowledge, and execute complex, multi-step tasks with explainable reasoning, acting as a powerful reasoning core.

AI/ML Development, Enterprise Software, Technology ConsultingFor: AI Engineers, Machine Learning Developers, Product Owners, Enterprise Architects

Frequently asked questions

Cognee is an open-source AI memory engine that transforms unstructured raw data into a structured, persistent, and dynamic AI memory. It combines vector search and knowledge graphs to enable AI agents to understand, reason, and adapt through contextual memory storage and retrieval.

Cognee processes data through multiple key steps. These steps include Add, for ingesting raw data asynchronously; Cognify, for intelligently chunking documents, creating vector embeddings, extracting entities and relationships, and building a knowledge graph representing concepts and their interconnections; Search, for performing contextual queries that combine vector similarity and graph traversal to retrieve precise answers, often supported by large language models (LLMs); and Memify (coming soon), for further semantic enrichment of the knowledge graph for deeper context.

Unlike typical RAG assistants that rely mostly on keyword search and simple vector retrieval, Cognee builds a knowledge graph that maps conceptual relationships and dependencies across data. This allows it to answer complex, multi-hop queries across related entities, provide verifiable, context-grounded responses, and reduce hallucinations by grounding LLM output in fact-based memory stored externally.

The main components of Cognee’s memory system include a hybrid memory layer combining vector embeddings and graph database storage, persistent memory capable of learning and updating from user feedback and interactions, and flexible querying supporting semantic search, graph-based queries, and combined approaches.

Cognee acts as an external, verifiable memory layer that supplies LLMs with domain-specific, factual data rather than relying solely on the model’s internal probabilistic knowledge. This hybrid graph-vector memory supports precise retrieval of data relevant for a given query, significantly reducing hallucinations and improving trustworthiness.

Typical use cases or applications include building smart domain-specific copilots that learn and adapt over time, creating intelligent FAQ assistants that understand documentation context and relationships between concepts rather than just keyword matching, and enhancing AI agent memory in multi-agent frameworks or enterprise data environments.

Developers can get started with Cognee by ingesting and cleaning data, ideally removing noise like cookie banners and navigation for better graph quality. Then, use the .add() method to input data and run .cognify() to build the knowledge graph. Developers should perform searches with cognee.search() configured for appropriate query types such as graph completion, similarity, and insights. Cognee supports integrations with multiple vector and graph databases, as well as various LLM providers.

Cognee provides advanced features such as multi-type search including graph-based completion, semantic similarity, and combined insights, persistent memory of workflows and rules that can learn from interactions, and scalability from simple chatbots to complex multi-agent AI systems.

Yes, Cognee is open-source and available for developers to deploy, customize, and contribute.

Tags

agent memorydata transformationdata retrievalknowledge graphpersistent memory

Specifications

Deployment
API
Self-hosted
Target Audience
Startup
Business
Enterprise
Complexity
Expert

Pricing

Basic

Per monthly

Free
  • License to use Cognee open source
  • Cognee tasks and pipelines
  • Custom schema and ontology generation
  • Integrated evaluations
  • More than 28 data sources supported

On-Prem Subscription for SMBs

Per monthly

$3500
  • Includes all features from the Basic plan
  • License to use Cognee open source and Cognee Platform
  • 1 day SLA
  • On-prem deployment
  • Hands-on support
  • Architecture review
  • Roadmap prioritization
  • Knowledge transfer

Cloud Subscription

Per monthly

$25
  • Includes all features from the Basic plan
  • Fully hosted cloud platform
  • Multi-tenant architecture
  • Comprehensive API endpoints
  • Automated scaling and parallel processing
  • Ability to group memories per user and domain
  • Automatic updates and priority support
  • 1 GB ingestion + 10,000 API calls

✓ Free plan • ✓ Plans from $25 / monthly

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