cognee

cognee is a memory management tool for AI Apps and Agents

Qdrant is available as a native built-in vector database to store and retrieve embeddings.

📦 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"
VECTOR_DB_PROVIDER="qdrant"
VECTOR_DB_URL=https://url-to-your-qdrant-cloud-instance.cloud.qdrant.io:6333
VECTOR_DB_KEY=your-qdrant-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.