microsoft / RD-Agent

🖥️ Live Demo |
 🎥 Demo Video ▶️YouTube |
 📖 Documentation |
 📄 Tech Report |
 📃 Papers 
# 📰 News
| 🗞️ News | 📝 Description |
| -- | ------ |
| Agent² RL-Bench Preprint | A benchmark for evaluating LLM agents on end-to-end post-training engineering. See the project page and code. |
| 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 |
# 🧪 Agent² RL-Bench
 Project Page ·
 Paper ·
 Code & Quick Start ·
 Microsoft Research 
**Agent² RL-Bench** evaluates whether LLM agents can autonomously engineer end-to-end post-training pipelines, spanning static optimization and stateful online RL.
# 🏆 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 inte