Cognee - Graph and Vector Memory for AI Agents

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Persistent and accurate memory for AI agents. With Cognee, your AI agent understands, reasons, and adapts.

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Why cognee?

## Quickstart - 🚀 Try it now on [Google Colab](https://colab.research.google.com/drive/12Vi9zID-M3fpKpKiaqDBvkk98ElkRPWy?usp=sharing) - 📓 Explore our [Deepnote Notebook](https://deepnote.com/workspace/cognee-382213d0-0444-4c89-8265-13770e333c02/project/cognee-demo-78ffacb9-5832-4611-bb1a-560386068b30/notebook/Notebook-1-75b24cda566d4c24ab348f7150792601?utm_source=share-modal&utm_medium=product-shared-content&utm_campaign=notebook&utm_content=78ffacb9-5832-4611-bb1a-560386068b30) - 🛠️ Clone our [Starter Repo](https://github.com/topoteretes/cognee/tree/main/cognee-starter-kit) ## About Cognee Cognee transforms your data into a living knowledge graph that learns from feedback and auto-tunes to deliver better answers over time. **Run anywhere:** - 🏠 **Self-Hosted**: Runs locally, data stays on your device - ☁️ **Cognee Cloud**: Same open-source Cognee, deployed on Modal for seamless workflows **Self-Hosted Package:** - Unified memory for all your data sources - Domain-smart copilots that learn and adapt over time - Flexible memory architecture for AI agents and devices - Integrates easily with your current technology stack - Pythonic data pipelines supporting 30+ data sources out of the box - Fully extensible: customize tasks, pipelines, and search endpoints **Cognee Cloud:** - Get a managed UI and [Hosted Infrastructure](https://www.cognee.ai) with zero setup ## Self-Hosted (Open Source) Run Cognee on your stack. Cognee integrates easily with your current technologies. See our [integration guides](https://docs.cognee.ai/setup-configuration/overview). ### 📦 Installation Install Cognee with **pip**, **poetry**, **uv**, or your preferred Python package manager. **Requirements:** Python 3.10 to 3.12 #### Using uv ```bash uv pip install cognee ``` For detailed setup instructions, see our [Documentation](https://docs.cognee.ai/getting-started/installation#environment-configuration). ### 💻 Usage #### Configuration ```python import os os.environ["LLM_API_KEY"] = "YOUR OPENAI_API_KEY" ``` Alternatively, create a `.env` file using our [template](https://github.com/topoteretes/cognee/blob/main/.env.template). To integrate other LLM providers, see our [LLM Provider Documentation](https://docs.cognee.ai/setup-configuration/llm-providers). #### Python Example Run the default pipeline with this script: ```python import cognee import asyncio async def main(): # Add text to cognee await cognee.add("Cognee turns documents into AI memory.") # Generate the knowledge graph await cognee.cognify() # Add memory algorithms to the graph await cognee.memify() # Query the knowledge graph results = await cognee.search("What does cognee do?") # Display the results for result in results: print(result) if __name__ == '__main__': asyncio.run(main()) ``` Example output: ``` Cognee turns documents into AI memory. ``` #### CLI Example Get started with these essential commands: ``` cognee-cli add "Cognee turns documents into AI memory." cognee-cli cognify cognee-cli search "What does cognee do?" cognee-cli delete --all ``` Or run: ``` cognee-cli -ui ``` ## Cognee Cloud Cognee is the fastest way to start building reliable AI agent memory. Deploy in minutes with automatic updates, analytics, and enterprise-grade security. - Sign up on [Cognee Cloud](https://www.cognee.ai) - Add your API key to local UI and sync your data to Cognee Cloud - Start building with managed infrastructure and zero configuration ## Trusted in Production From regulated industries to startup stacks, Cognee is deployed in production and delivering value now. Read our [case studies](https://cognee.ai/blog) to learn more. ## Demos & Examples See Cognee in action: ### Cognee Cloud Beta Demo [Watch Demo](https://github.com/user-attachments/assets/fa520cd2-2913-4246-a444-902ea5242cb0) ### Simple GraphRAG Demo [Watch Demo](https://github.com/user-attachments/assets/d80b0776-4eb9-4b8e-aa22-3691e2d44b8f) ### Cognee with Ollama [Watch Demo](https://github.com/user-attachments/assets/8621d3e8-ecb8-4860-afb2-5594f2ee17db) ## Community & Support ### Contributing We welcome contributions from the community! Your input helps make Cognee better for everyone. See [`CONTRIBUTING.md`](CONTRIBUTING.md) to get started. ### Code of Conduct We're committed to fostering an inclusive and respectful community. Read our [Code of Conduct](https://github.com/topoteretes/cognee/blob/main/CODE_OF_CONDUCT.md) for guidelines. ## Research & Citation Cite our research paper on optimizing knowledge graphs for LLM reasoning: ```bibtex @misc{markovic2025optimizinginterfaceknowledgegraphs, title={Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning}, author={Vasilije Markovic and Lazar Obradovic and Laszlo Hajdu and Jovan Pavlovic}, year={2025}, eprint={2505.24478}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2505.24478}, } ```