| Introduction Video | README (chinese) | Documentation | Retrievers | Examples |

FlexRAG is an innovative open-source framework designed to streamline the rapid reproduction, development, and evaluation of Retrieval-Augmented Generation (RAG) systems. It provides comprehensive support for various RAG scenarios, including text-based, multimodal, and web-accessible applications. With an end-to-end pipeline covering everything from data preparation to system evaluation, FlexRAG empowers researchers to efficiently share their work with the community and swiftly develop demonstrative prototypes based on their algorithms.

• 📖 Table of Contents
• ✨ Key Features
• 📢 News
• 🚀 Getting Started
• 🏗️ Architecture
• 📊 Benchmarks
• 🏷️ License
• 🖋️ Citation
• ❤️ Acknowledgements

• 2025-06-17: Our publication FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation is now available!
• 2025-03-24: The Chinese documentation is now available! Please visit the documentation for more details.
• 2025-02-25: FlexRAG's LocalRetriever now supports loading from the HuggingFace Hub.
• 2025-01-22: A new entrypoint run_retriever and four new information retrieval metrics (e.g., RetrievalMAP) are now available. Check out the documentation for more details.
• 2025-01-08: We provide Windows wheels for FlexRAG. You can install FlexRAG via pip on Windows now.
• 2025-01-08: The benchmark of FlexRAG on Single-hop QA tasks is now available. Check out the benchmarks for more details.
• 2025-01-05: Documentation for FlexRAG is now available. Check out the documentation for more details.

To install FlexRAG via pip:

pip install flexrag

Visit our documentation to learn more.

• Installation
• Quickstart
• Tutorial

FlexRAG is designed with a modular architecture, allowing you to easily customize and extend the framework to meet your specific needs. The following diagram illustrates the architecture of FlexRAG:

We have conducted extensive benchmarks using the FlexRAG framework. For more details, please refer to the benchmarks page.

This repository is licensed under the MIT License. See the LICENSE file for details.

If you use FlexRAG in your research, please cite our project:

@software{Zhang_FlexRAG_2025,
author = {Zhang, Zhuocheng and Feng, Yang and Zhang, Min},
doi = {10.5281/zenodo.14593327},
month = jan,
title = {{FlexRAG}},
url = {https://github.com/ictnlp/FlexRAG},
year = {2025}
}
@misc{zhang2025flexragflexiblecomprehensiveframework,
      title={FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation}, 
      author={Zhuocheng Zhang and Yang Feng and Min Zhang},
      year={2025},
      eprint={2506.12494},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.12494}, 
}

This project benefits from the following open-source projects:

• Faiss
• FlashRAG
• LanceDB
• ANN Benchmarks
• Chonkie
• rerankers