cognee/README.md

cognee - Memory for AI Agents in 5 lines of code

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

🚀 We launched Cogwit beta (Fully-hosted AI Memory): Sign up here! 🚀

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

More on use-cases and evals

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Features

• Interconnect and retrieve your past conversations, documents, images and audio transcriptions
• Replaces RAG systems and reduces 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 , Deepnote 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 for more information.

Using cognee

Start with our self-hosted package:

• Get self-serve UI with embedded Python notebooks
• Add custom tasks and pipelines
• Docker images and MCP servers you can deploy
• Use distributed cognee to process a TBs of your data

Ready for production:

• Sync your local data to our hosted solution
• Get a secure API endpoint
• We manage the UI for you

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

uv pip install cognee

Detailed instructions can be found in our docs

💻 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

Via CLI

These commands will show you the basics of cognee CLI

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

Python

This script will run the default pipeline:

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.

Hosted Platform

Get up and running in minutes with automatic updates, analytics, and enterprise security.

1. Sign up on cogwit
2. Add your API key to local UI and sync your data or send your data to Cogwit API

Demos

1. Cogwit Beta demo:

Cogwit Beta

2. Simple GraphRAG demo

Simple GraphRAG demo

3. cognee with Ollama

cognee with local models

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:

@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}, 
}