RD-Agent
microsoft/RD-Agent
Automating high-value generic R&D processes through AI.
Overview
R&D-Agent is a Python-based tool automating data and model-centric research and development tasks, enhancing productivity in the AI era. It supports LLM fine-tuning, quant trading, and integrates with various LLM providers via LiteLLM backend.
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Install
pip install RD-AgentREADME
🖥️ Live Demo | 🎥 Demo Video ▶️YouTube | 📖 Documentation | 📄 Tech Report | 📃 Papers
📰 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! |
| 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 (🗪) |
| 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 reporte