Cognee works seamlessly with AI Agent frameworks and popular tools in the AI ecosystem. These integrations help you observe, evaluate, use your AI Memory.
All integrations are designed to be lightweight and easy to configure, requiring minimal setup to get started.

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Observability & Monitoring

Track performance, debug issues, and monitor your knowledge graph operations in production.

Langfuse

Open-source LLM observabilityDistributed tracing and metrics for every Cognee task with granular pipeline performance insights.

Keywords AI

LLM application tracingSpan-level tracing across tasks and workflows with minimal code using Cognee’s observe abstraction.

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Evaluation & Testing

Measure and improve the quality of your knowledge graph outputs with comprehensive evaluation frameworks.

DeepEval

Comprehensive RAG evaluationRun QA & RAG metrics including Contextual Relevancy, Precision/Recall, and Coverage using LLM-as-a-judge workflows.

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Cloud LLM Providers

Connect to enterprise-grade LLM services through Cognee’s flexible integration layer.

AWS Bedrock

Enterprise LLM accessUse AWS Bedrock models including Claude, Titan, and Cohere through LiteLLM proxy integration.

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Agent Frameworks & IDEs

Integrate Cognee directly into your development workflow with MCP-compatible tools and AI assistants.

MCP Integrations

Cognee’s Model Context Protocol (MCP) adapter enables seamless integration with AI assistants, providing direct access to your knowledge graphs for in-context assistance.

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Contributing Integrations

Don’t see your favorite tool? Cognee is open and extensible—help us grow the ecosystem!
All community database integrations are maintained in the cognee-community repository, keeping the core Cognee package lean while providing extensibility.