cognee-mcp

<div align="center"> <a href="https://github.com/topoteretes/cognee"> <img src="https://raw.githubusercontent.com/topoteretes/cognee/refs/heads/dev/assets/cognee-logo-transparent.png" alt="Cognee Logo" height="60"> </a>

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cognee - memory layer for AI apps and Agents

<p align="center"> <a href="https://www.youtube.com/watch?v=1bezuvLwJmw&t=2s">Demo</a> . <a href="https://cognee.ai">Learn more</a> · <a href="https://discord.gg/NQPKmU5CCg">Join Discord</a> </p>

AI Agent responses you can rely on.

Build dynamic Agent memory using scalable, modular ECL (Extract, Cognify, Load) pipelines.

More on use-cases.

<div style="text-align: center"> <img src="https://raw.githubusercontent.com/topoteretes/cognee/refs/heads/dev/assets/cognee_benefits.png" alt="Why cognee?" width="100%" /> </div>

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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 <a href="https://colab.research.google.com/drive/1g-Qnx6l_ecHZi0IOw23rg0qC4TYvEvWZ?usp=sharing">notebook</a> or <a href="https://github.com/topoteretes/cognee-starter">starter repo</a>

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.

📦 Installation

You can install Cognee using either pip, poetry, uv or any other python package manager.

With pip

pip install cognee

💻 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 <a href="https://github.com/topoteretes/cognee/blob/main/.env.template">template.</a> To use different LLM providers, for more info check out our <a href="https://docs.cognee.ai">documentation</a>

Simple example

This script will run the default pipeline:

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.
  

Graph visualization: <a href="https://rawcdn.githack.com/topoteretes/cognee/refs/heads/add-visualization-readme/assets/graph_visualization.html"><img src="assets/graph_visualization.png" width="100%" alt="Graph Visualization"></a> Open in browser.

For more advanced usage, have a look at our <a href="https://docs.cognee.ai"> documentation</a>.

Understand our architecture

<div style="text-align: center"> <img src="assets/cognee_diagram.png" alt="cognee concept diagram" width="100%" /> </div>

Demos

1. What is AI memory:

Learn about cognee

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 <a href="https://github.com/topoteretes/cognee/blob/main/CODE_OF_CONDUCT.md"><code>CODE_OF_CONDUCT</code></a> for more information.

💫 Contributors

<a href="https://github.com/topoteretes/cognee/graphs/contributors"> <img alt="contributors" src="https://contrib.rocks/image?repo=topoteretes/cognee"/> </a>

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