# cognee 



####  Deterministic LLMs Outputs for AI Engineers



_Open-source framework for loading and structuring LLM context to create accurate and explainable AI solutions using knowledge graphs and vector stores_



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### Let's learn about cogneeHub!


cogneeHub is a free, open-source learning platform for those interested in creating deterministic LLM outputs. We help developers by using graphs, LLMs, and adding vector retrieval to their Machine Learning stack.


- **Get started** — [Get started with cognee quickly and try it out for yourself.](quickstart.md)

- **Conceptual Overview** — Learn about the [core concepts](conceptual_overview.md) of cognee and how it fits into your projects.

- **Data Engineering and LLMOps** — Learn about some [data engineering and llmops](data_engineering_llm_ops.md) core concepts that will help you build better AI apps.

- **RAGs** — We provide easy-to-follow [learning materials](rags.md) to help you learn about RAGs.

- **Research** — A list of resources to help you learn more about [cognee and LLM memory research](research.md)

- **Blog** — A blog where you can read about the [latest news and updates](blog/index.md) about cognee.

- **Support** — [Book time](https://www.cognee.ai/#bookTime) with our team.


[//]: # (- **Case Studies** — Read about [case studies](case_studies.md) that show how cognee can be used in real-world applications.)


### Vision


![Vision](img/roadmap.png)



### Architecture


![Architecture](img/architecture.png)

### Why use cognee?


The question of using cognee is fundamentally a question of why to have deterministic outputs for your llm workflows.


1. **Cost-effective** — cognee extends the capabilities of your LLMs without the need for expensive data processing tools.


2. **Self-contained** — cognee runs as a library and is simple to use


3. **Interpretable** — Navigate graphs instead of embeddings to understand your data.


4. **User Guided** —  cognee lets you control your input and provide your own Pydantic data models 




## License


This project is licensed under the terms of the Apache License 2.0.


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[//]: # (# New to cognee?)

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[//]: # (The getting started guide covers adding a GraphRAG data store to your AI app, sending events, identifying users, extracting actions and insights, and interconnecting separate datasets.)

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[//]: # (    <h2>Ingest Data</h2>)

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[//]: # (    <p>Learn how to manage ingestion of events, customer data or third party data for use with cognee.</p>)

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[//]: # (    <a href="#">Explore</a>)

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[//]: # (    <h2>Templates</h2>)

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[//]: # (    <p>Analyze and enrich your data and improve LLM answers with a series of templates using cognee tasks and pipelines.</p>)

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