cognee - Memory for AI Agents in 5 lines of code

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Build dynamic Agent memory using scalable, modular ECL (Extract, Cognify, Load) pipelines. More on [use-cases](https://docs.cognee.ai/use-cases) and [evals](https://github.com/topoteretes/cognee/tree/main/evals)

🌐 Available Languages : 🇵🇹 Português · 🇨🇳 [中文] · 🇷🇺 Русский

Why cognee?

## Features - Interconnect and retrieve your past conversations, documents, images and audio transcriptions - Reduce hallucinations, developer effort, and cost. - Load data to graph and vector databases using only Pydantic - Manipulate your data while ingesting from 30+ data sources ## Get Started Get started quickly with a Google Colab notebook or starter repo ## 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. ## 📦 Installation You can install Cognee using either **pip**, **poetry**, **uv** or any other python package manager. Cognee supports Python 3.8 to 3.12 ### With pip ```bash pip install cognee ``` ## Local Cognee installation You can install the local Cognee repo using **pip**, **poetry** and **uv**. For local pip installation please make sure your pip version is above version 21.3. ### with UV with all optional dependencies ```bash uv sync --all-extras ``` ## 💻 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 This script will run the default pipeline: ```python import cognee import asyncio async def main(): # Add text to cognee await cognee.add("Natural language processing (NLP) is an interdisciplinary subfield of computer science and information retrieval.") # Generate the knowledge graph await cognee.cognify() # Query the knowledge graph results = await cognee.search("Tell me about NLP") # Display the results for result in results: print(result) if __name__ == '__main__': asyncio.run(main()) ``` Example output: ``` Natural Language Processing (NLP) is a cross-disciplinary and interdisciplinary field that involves computer science and information retrieval. It focuses on the interaction between computers and human language, enabling machines to understand and process natural language. ``` ### cognee UI You can also cognify your files and query using cognee UI. Cognee UI 2

Cognee UI 2

Try cognee UI out locally [here](https://docs.cognee.ai/how-to-guides/cognee-ui). ## Understand our architecture

cognee concept diagram

## Demos 1. What is AI memory: [Learn about cognee](https://github.com/user-attachments/assets/8b2a0050-5ec4-424c-b417-8269971503f0) 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) ## 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. ## 💫 Contributors

contributors

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