# cognee **Repository Path**: mirrors/cognee ## Basic Information - **Project Name**: cognee - **Description**: 可靠的AI智能体响应 - **Primary Language**: Python - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: https://www.oschina.net/p/cognee - **GVP Project**: No ## Statistics - **Stars**: 2 - **Forks**: 0 - **Created**: 2025-06-06 - **Last Updated**: 2025-10-04 ## Categories & Tags **Categories**: Artificial-Intelligence, MCP **Tags**: None ## README

cognee - Memory for AI Agents in 6 lines of code

Demo . Learn more · Join Discord · Join r/AIMemory . Docs . cognee community repo

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Build dynamic memory for Agents and replace RAG using scalable, modular ECL (Extract, Cognify, Load) pipelines.

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Why cognee?

## Get Started Get started quickly with a Google Colab notebook , Deepnote notebook or starter repo ## About cognee cognee works locally and stores your data on your device. Our hosted solution is just our deployment of OSS cognee on Modal, with the goal of making development and productionization easier. Self-hosted package: - Interconnects any kind of documents: past conversations, files, images, and audio transcriptions - Replaces RAG systems with a memory layer based on graphs and vectors - Reduces developer effort and cost, while increasing quality and precision - Provides Pythonic data pipelines that manage data ingestion from 30+ data sources - Is highly customizable with custom tasks, pipelines, and a set of built-in search endpoints Hosted platform: - Includes a managed UI and a [hosted solution](https://www.cognee.ai) ## Self-Hosted (Open Source) ### 📦 Installation You can install Cognee using either **pip**, **poetry**, **uv** or any other python package manager. Cognee supports Python 3.10 to 3.12 #### With uv ```bash uv pip install cognee ``` Detailed instructions can be found in our [docs](https://docs.cognee.ai/getting-started/installation#environment-configuration) ### 💻 Basic Usage #### Setup ``` import os os.environ["LLM_API_KEY"] = "YOUR OPENAI_API_KEY" ``` You can also set the variables by creating .env file, using our template. To use different LLM providers, for more info check out our documentation #### Simple example ##### Python This script will run the default pipeline: ```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. ``` ##### Via CLI Let's get the basics covered ``` 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 ``` ### Hosted Platform Get up and running in minutes with automatic updates, analytics, and enterprise security. 1. Sign up on [cogwit](https://www.cognee.ai) 2. Add your API key to local UI and sync your data to Cogwit ## Demos 1. Cogwit Beta demo: [Cogwit Beta](https://github.com/user-attachments/assets/fa520cd2-2913-4246-a444-902ea5242cb0) 2. Simple GraphRAG demo [Simple GraphRAG demo](https://github.com/user-attachments/assets/d80b0776-4eb9-4b8e-aa22-3691e2d44b8f) 3. cognee with Ollama [cognee with local models](https://github.com/user-attachments/assets/8621d3e8-ecb8-4860-afb2-5594f2ee17db) ## Contributing Your contributions are at the core of making this a true open source project. Any contributions you make are **greatly appreciated**. See [`CONTRIBUTING.md`](CONTRIBUTING.md) for more information. ## Code of Conduct We are committed to making open source an enjoyable and respectful experience for our community. See CODE_OF_CONDUCT for more information. ## Citation We now have a paper you can cite: ```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}, } ```