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📰 News

🗞️ News	📝 Description
ICML 2026 Acceptance	We are thrilled to announce that our paper FT-Dojo: Towards Autonomous LLM Fine-Tuning with Language Agents has been accepted to ICML 2026. The FT-Agent implementation is available in the LLM fine-tuning guide.
ACL 2026 Findings Acceptance	We are thrilled to announce that our paper Reasoning as Gradient has been accepted to ACL 2026 Findings. Execution traces are available at Gome GPT-5 Traces
Web UI Release	We release a new frontend that can be built and served by rdagent server_ui for real-time interaction and trace viewing, currently excluding the data_science scenario.
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!

🏆 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.

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

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.

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.
• 🧪 FT-Agent: Autonomous LLM fine-tuning for benchmark-driven domain adaptation. See the LLM fine-tuning guide.
• ...

You can click the links above to view the demo. We're continuously adding more methods and scenarios to the project to enhance your R&D processes and boost productivity.

Additionally, you can take a closer look at the examples in our 🖥️ Live Demo.

⚡ Quick start

RD-Agent currently only supports Linux.

You can try above demos by running the following command:

🐳 Docker installation.

Users must ensure Docker is installed before attempting most scenarios. Please refer to the official 🐳Docker page for installation instructions. Ensure the current user can run Docker commands without using sudo. You can verify this by executing docker run hello-world.

🐍 Create a Conda Environment

• Create a new conda environment with Python (3.10 and 3.11 are well-tested in our CI):

  conda create -n rdagent python=3.10
  

• Activate the environment:

  conda activate rdagent
  

🛠️ Install the R&D-Agent

For Users

• You can directly install the R&D-Agent package from PyPI:

  pip install rdagent
  

For Developers

• If you want to try the latest version or contribute to RD-Agent, you can install it from the s