内容摘录
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<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="https://aka.ms/RD-Agent-Tech-Report" target="_blank">📄 Tech Report</a> |
<a href="#-paperwork-list"> 📃 Papers </a>
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📰 News
| 🗞️ News | 📝 Description |
| -- | ------ |
| NeurIPS 2025 Acceptance | We are thrilled to announce that our paper R&D-Agent-Quant has been accepted to NeurIPS 2025 |
| Technical Report Release | Overall framework description and results on MLE-bench |
| R&D-Agent-Quant Release | Apply R&D-Agent to quant trading |
| MLE-Bench Results Released | R&D-Agent currently leads as the top-performing machine learning engineering agent on MLE-bench |
| Support LiteLLM Backend | We now fully support **LiteLLM** as our default backend for integration with multiple LLM providers. |
| General Data Science Agent | Data Science Agent |
| Kaggle Scenario release | We release **Kaggle Agent**, try the new features! |
| Official WeChat group release | We created a WeChat group, welcome to join! (🗪QR Code) |
| Official Discord release | We launch our first chatting channel in Discord (🗪Chat) |
| First release | **R&D-Agent** is released on GitHub |
🏆 The Best Machine Learning Engineering Agent!
MLE-bench is a comprehensive benchmark evaluating the performance of AI agents on machine learning engineering tasks. Utilizing datasets from 75 Kaggle competitions, MLE-bench provides robust assessments of AI systems' capabilities in real-world ML engineering scenarios.
R&D-Agent currently leads as the top-performing machine learning engineering agent on MLE-bench:
| Agent | Low == Lite (%) | Medium (%) | High (%) | All (%) |
|---------|--------|-----------|---------|----------|
| R&D-Agent o3(R)+GPT-4.1(D) | 51.52 ± 6.9 | 19.3 ± 5.5 | 26.67 ± 0 | 30.22 ± 1.5 |
| R&D-Agent o1-preview | 48.18 ± 2.49 | 8.95 ± 2.36 | 18.67 ± 2.98 | 22.4 ± 1.1 |
| AIDE o1-preview | 34.3 ± 2.4 | 8.8 ± 1.1 | 10.0 ± 1.9 | 16.9 ± 1.1 |
**Notes:**
**O3(R)+GPT-4.1(D)**: This version is designed to both reduce average time per loop and leverage a cost-effective combination of backend LLMs by seamlessly integrating Research Agent (o3) with Development Agent (GPT-4.1).
**AIDE o1-preview**: Represents the previously best public result on MLE-bench as reported in the original MLE-bench paper.
Average and standard deviation results for R&D-Agent o1-preview is based on a independent of 5 seeds and for R&D-Agent o3(R)+GPT-4.1(D) is based on 6 seeds.
According to MLE-Bench, the 75 competitions are categorized into three levels of complexity: **Low==Lite** if we estimate that an experienced ML engineer can produce a sensible solution in under 2 hours, excluding the time taken to train any models; **Medium** if it takes between 2 and 10 hours; and **High** if it takes more than 10 hours.
You can inspect the detailed runs of the above results online.
R&D-Agent o1-preview detailed runs
R&D-Agent o3(R)+GPT-4.1(D) detailed runs
For running R&D-Agent on MLE-bench, refer to **MLE-bench Guide: Running ML Engineering via MLE-bench**
🥇 The First Data-Centric Quant Multi-Agent Framework!
R&D-Agent for Quantitative Finance, in short **RD-Agent(Q)**, is the first data-centric, multi-agent framework designed to automate the full-stack research and development of quantitative strategies via coordinated factor-model co-optimization.
!image
Extensive experiments in real stock markets show that, at a cost under $10, RD-Agent(Q) achieves approximately 2× higher ARR than benchmark factor libraries while using over 70% fewer factors. It also surpasses state-of-the-art deep time-series models under smaller resource budgets. Its alternating factor–model optimization further delivers excellent trade-off between predictive accuracy and strategy robustness.
You can learn more details about **RD-Agent(Q)** through the paper and reproduce it through the documentation.
Data Science Agent Preview
Check out our demo video showcasing the current progress of our Data Science Agent under development:
https://github.com/user-attachments/assets/3eccbecb-34a4-4c81-bce4-d3f8862f7305
🌟 Introduction
<div align="center">
<img src="docs/_static/scen.png" alt="Our focused scenario" style="width:80%; ">
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R&D-Agent aims to automate the most critical and valuable aspects of the industrial R&D process, and we begin with focusing on the data-driven scenarios to streamline the development of models and data.
Methodologically, we have identified a framework with two key components: 'R' for proposing new ideas and 'D' for implementing them.
We believe that the automatic evolution of R&D will lead to solutions of significant industrial value.
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R&D is a very general scenario. The advent of R&D-Agent can be your
💰 **Automatic Quant Factory** (🎥Demo Video|▶️YouTube)
🤖 **Data Mining Agent:** Iteratively proposing data & models (🎥Demo Video 1|▶️YouTube) (🎥Demo Video 2|▶️YouTube) and implementing them by gaining knowledge from data.
🦾 **Research Copilot:** Auto read research papers (🎥Demo Video|▶️YouTube) / financial reports (🎥Demo Video|▶️YouTube) and implement model structures or building datasets.
🤖 **Kaggle Agent:** Auto Model Tuning and Feature Engineering([🎥Demo Video Coming Soon...]()) and implementing them to achieve more in competitions.
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