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<h4 align="center">
<img src="docs/_static/logo.png" alt="RA-Agent logo" style="width:70%; ">
<a href="https://rdagent.azurewebsites.net" target="_blank">🖥️ Live Demo</a> | <a href="https://rdagent.azurewebsites.net/factor_loop" target="_blank">🎥 Demo Video</a> <a href="https://www.youtube.com/watch?v=JJ4JYO3HscM&list=PLALmKB0_N3_i52fhUmPQiL4jsO354uopR" target="_blank">▶️YouTube</a> | <a href="https://rdagent.readthedocs.io/en/latest/index.html" target="_blank">📖 Documentation</a> | <a href="#-paperwork-list"> 📃 Papers </a>
<a href="https://rdagent.azurewebsites.net" target="_blank">🖥️ Live Demo</a> |
<a href="https://rdagent.azurewebsites.net/factor_loop" target="_blank">🎥 Demo Video</a> <a href="https://www.youtube.com/watch?v=JJ4JYO3HscM&list=PLALmKB0_N3_i52fhUmPQiL4jsO354uopR" target="_blank">▶️YouTube</a> |
<a href="https://rdagent.readthedocs.io/en/latest/index.html" target="_blank">📖 Documentation</a> |
<a href="https://aka.ms/RD-Agent-Tech-Report" target="_blank">📄 Tech Report</a> |
<a href="#-paperwork-list"> 📃 Papers </a>
</h3>
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[![Chat](https://img.shields.io/badge/chat-discord-blue)](https://discord.gg/ybQ97B6Jjy)
[![Documentation Status](https://readthedocs.org/projects/rdagent/badge/?version=latest)](https://rdagent.readthedocs.io/en/latest/?badge=latest)
[![Readthedocs Preview](https://github.com/microsoft/RD-Agent/actions/workflows/readthedocs-preview.yml/badge.svg)](https://github.com/microsoft/RD-Agent/actions/workflows/readthedocs-preview.yml) <!-- this badge is too long, please place it in the last one to make it pretty -->
[![arXiv](https://img.shields.io/badge/arXiv-2505.14738-00ff00.svg)](https://arxiv.org/abs/2505.14738)
# 🏆 The Best Machine Learning Engineering Agent!
@@ -49,7 +55,9 @@ You can inspect the detailed runs of the above results online.
# 📰 News
| 🗞️ News | 📝 Description |
| -- | ------ |
| MLE-Bench Results Released | R&D-Agent currently leads as the [top-performing machine learning engineering agent](#-the-best-machine-learning-engineering-agent) on MLE-bench |
| [Technical Report Release](#overall-technical-report) | Overall framework description and results on MLE-bench |
| [R&D-Agent-Quant Release](#deep-application-in-diverse-scenarios) | Apply R&D-Agent to quant trading |
| MLE-Bench Results Released | R&D-Agent currently leads as the [top-performing machine learning engineering agent](#-the-best-machine-learning-engineering-agent) on MLE-bench |
| Support LiteLLM Backend | We now fully support **[LiteLLM](https://github.com/BerriAI/litellm)** as a backend for integration with multiple LLM providers. |
| More General Data Science Agent | 🚀Coming soon! |
| Kaggle Scenario release | We release **[Kaggle Agent](https://rdagent.readthedocs.io/en/latest/scens/kaggle_agent.html)**, try the new features! |
@@ -316,6 +324,21 @@ More documents can be found in the **[📖 readthedocs](https://rdagent.readthed
# 📃 Paper/Work list
## Overall Technical Report
- [R&D-Agent: Automating Data-Driven AI Solution Building Through LLM-Powered Automated Research, Development, and Evolution](https://arxiv.org/abs/2505.14738)
```BibTeX
@misc{yang2024rdagent,
title={R\&D-Agent: Automating Data-Driven AI Solution Building Through LLM-Powered Automated Research, Development, and Evolution},
author={Xu Yang and Xiao Yang and Shikai Fang and Bowen Xian and Yuante Li and Jian Wang and Minrui Xu and Haoran Pan and Xinpeng Hong and Weiqing Liu and Yelong Shen and Weizhu Chen and Jiang Bian},
year={2025},
eprint={2505.14738},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2505.14738}
}
```
![image](https://github.com/user-attachments/assets/28b0488d-a546-4fef-8dc5-563ed64a9b4d)
## 📊 Benchmark
- [Towards Data-Centric Automatic R&D](https://arxiv.org/abs/2404.11276)
```BibTeX
@@ -353,6 +376,21 @@ For more detail, please refer to our **[🖥️ Live Demo page](https://rdagent.
```
![image](https://github.com/user-attachments/assets/75d9769b-0edd-4caf-9d45-57d1e577054b)
## Deep Application in Diverse Scenarios
- [R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization](https://arxiv.org/abs/2505.15155)
```BibTeX
@misc{li2025rdagentquant,
title={R\&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization},
author={Yuante Li and Xu Yang and Xiao Yang and Minrui Xu and Xisen Wang and Weiqing Liu and Jiang Bian},
year={2025},
eprint={2505.15155},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
![image](https://github.com/user-attachments/assets/3198bc10-47ba-4ee0-8a8e-46d5ce44f45d)
# 🤝 Contributing