Cognee

What is Cognee about?

Cognee enables AI agents to store, retrieve, and reason over past interactions, documents, images, and audio transcriptions. It replaces traditional Retrieval‑Augmented Generation (RAG) workflows with a unified, low‑code solution that builds and queries knowledge graphs and vector databases.

How to use Cognee?

1. Install the package (pip, poetry, or uv). Example with pip:

pip install cognee

2. Configure your LLM API key, e.g., via environment variable or a .env file:

import os
os.environ["LLM_API_KEY"] = "YOUR_OPENAI_API_KEY"

3. Add data and run the default pipeline:

import cognee, asyncio

async def main():
    await cognee.add("Natural language processing (NLP) is ...")
    await cognee.cognify()
    results = await cognee.search("Tell me about NLP")
    for r in results:
        print(r)

asyncio.run(main())

4. Optionally launch the Cognee UI for visual ingestion and querying.

Key Features

• Interconnect and retrieve past conversations, documents, images, and audio transcriptions.
• Fully replaces RAG systems, reducing developer effort and cost.
• Load data into graph (e.g., Neo4j) and vector databases using only Pydantic models.
• Ingest from 30+ sources (files, web pages, APIs, etc.).
• Supports Python 3.10‑3.13; installable via pip, poetry, or UV.
• Hosted "Cogwit" beta offering a managed AI memory service.
• Built‑in UI for visual data cognition and query.

Use Cases

• AI assistants that remember user preferences and conversation context.
• Knowledge‑graph construction for enterprise data and research labs.
• Multimodal retrieval, enabling agents to search across text, images, and audio.
• RAG replacement in LLM‑driven applications, cutting latency and API costs.
• Rapid prototyping of memory‑enabled agents for hackathons or proof‑of‑concepts.

FAQ

Q: Which LLM providers are supported? A: Any provider that offers an API key compatible with the OpenAI request schema; local models via Ollama are also supported.

Q: Do I need a vector database separate from the graph store? A: Cognee can sync embeddings to both a graph database (e.g., Neo4j) and a vector store; you can choose one or both.

Q: How do I set environment variables without editing code? A: Use a .env file based on the provided .env.template or export variables in your shell.

Q: Is there a hosted version? A: Yes, the Cogwit beta offers a fully‑hosted AI memory service at https://platform.cognee.ai/.

Q: Can I extend the ingestion pipeline? A: The ECL architecture is modular; you can add custom extractors, transformers, or loaders via Python plugins.

cognee - Memory for AI Agents in 5 lines of code

🚀 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

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 3.10 to 3.13

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

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.

💫 Contributors

Sponsors

Thanks to the following companies for sponsoring the ongoing development of cognee.

• GitHub's Secure Open Source Fund

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