
cognee
Memory for AI Agents in 5 lines of code
Stars: 6915

Cognee is an open-source framework designed for creating self-improving deterministic outputs for Large Language Models (LLMs) using graphs, LLMs, and vector retrieval. It provides a platform for AI engineers to enhance their models and generate more accurate results. Users can leverage Cognee to add new information, utilize LLMs for knowledge creation, and query the system for relevant knowledge. The tool supports various LLM providers and offers flexibility in adding different data types, such as text files or directories. Cognee aims to streamline the process of working with LLMs and improving AI models for better performance and efficiency.
README:

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.
🌐 Available Languages : Deutsch | Español | français | 日本語 | 한국어 | Português | Русский | 中文
- 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 quickly with a Google Colab notebook , Deepnote notebook or starter repo
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.
You can install Cognee using either pip, poetry, uv or any other python package manager.
Cognee supports Python 3.10 to 3.13
pip install cognee
You can install the local Cognee repo using uv, pip and poetry. For local pip installation please make sure your pip version is above version 21.3.
uv sync --all-extras
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
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
You can also cognify your files and query using cognee UI.
Try cognee UI out locally here.
- Cogwit Beta demo:
- Simple GraphRAG demo
- cognee with Ollama
We are committed to making open source an enjoyable and respectful experience for our community. See CODE_OF_CONDUCT
for more information.
Thanks to the following companies for sponsoring the ongoing development of cognee.
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