cognee-mcp

What is Cognee?

Cognee is an open-source project that provides a dynamic memory solution for AI agents with just a few lines of code. It aims to replace traditional RAG (Retrieval Augmented Generation) systems by offering a scalable and modular approach to data ingestion, processing, and search, leveraging graph and vector databases.

How to use Cognee?

Cognee can be installed via pip, poetry, or uv. It supports Python versions 3.8 to 3.12. Basic usage involves setting an LLM API key (e.g., OpenAI API key), adding text data to Cognee, generating a knowledge graph using the cognify() function, and then querying the knowledge graph with the search() function. Cognee also offers a UI for local use.

Key Features of Cognee

Dynamic Memory for AI Agents: Provides a robust memory system for AI agents.
ECL Pipelines: Utilizes scalable, modular Extract, Cognify, Load (ECL) pipelines for data processing.
Replaces RAG Systems: Offers an alternative to traditional RAG systems, aiming to reduce developer effort and cost.
Graph and Vector Database Integration: Easily loads data to graph and vector databases using Pydantic.
Data Manipulation: Allows manipulation of data during ingestion from over 30 data sources.
Interconnect and Retrieve: Enables interconnecting and retrieving past conversations, documents, images, and audio transcriptions.

Use Cases of Cognee

Building dynamic memory for AI agents.
Replacing RAG systems in AI applications.
Creating knowledge graphs from various data sources.
Querying interconnected data for AI agent responses.
Integrating with different LLM providers.

FAQ about Cognee

What is the core idea behind Cognee? Cognee provides a dynamic memory for AI agents, aiming to replace RAG systems with a more scalable and modular approach using ECL pipelines and graph/vector databases.
How many lines of code are needed to use Cognee? Cognee is designed to be used with as few as 5 lines of code for basic functionality.
What kind of data can Cognee process? Cognee can interconnect and retrieve past conversations, documents, images, and audio transcriptions.
Does Cognee support different LLM providers? Yes, Cognee allows setting up different LLM providers, with documentation available for more information.
Is there a UI for Cognee? Yes, Cognee offers a UI for cognifying files and querying data locally.
Where can I find more information or get started? You can find demos, documentation, and starter repositories on their GitHub page, and join their Discord or Reddit communities.

cognee - Memory for AI Agents in 5 lines of code

🚀 We are launching Cognee SaaS: Sign up here for the hosted beta!

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

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.

📦 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

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

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:

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.

Our paper is out! Read here

Cognee UI

You can also cognify your files and query using cognee UI.

Try cognee UI out locally here.

Understand our architecture

Demos

What is AI memory:

Learn about cognee

Simple GraphRAG demo

Simple GraphRAG demo

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.

cognee-mcp Overview