--- title: "RD-Agent" type: "tool" slug: "microsoft-rd-agent" canonical_url: "https://www.graphcanon.com/tools/microsoft-rd-agent" github_url: "https://github.com/microsoft/RD-Agent" homepage_url: "https://rdagent.azurewebsites.net/" stars: 13812 forks: 1753 primary_language: "Python" license: "MIT" categories: ["ai-agents", "llm-frameworks"] tags: ["development", "research", "data-science", "llm", "ai", "data-mining", "automation", "agent"] updated_at: "2026-07-07T19:46:06.540746+00:00" --- # RD-Agent > Automating high-value generic R&D processes with AI R&D-Agent is a tool designed to automate data and model-driven research and development in the AI era, leveraging AI agents for enhanced productivity. The project supports Python and offers various modes of operation including LLM fine-tuning, data science tasks, and integration with multiple LLM providers via LiteLLM backend. ## Facts - Repository: https://github.com/microsoft/RD-Agent - Homepage: https://rdagent.azurewebsites.net/ - Stars: 13,812 · Forks: 1,753 · Open issues: 192 · Watchers: 87 - Primary language: Python - License: MIT - Last pushed: 2026-06-15T18:03:02+00:00 ## Categories - [AI Agents](/categories/ai-agents.md) - [LLM Frameworks](/categories/llm-frameworks.md) ## Tags development, research, data-science, llm, ai, data-mining, automation, agent ## Related tools - [ECC](/tools/affaan-m-ecc.md) - The agent harness performance optimization system (★ 226,991) - [hermes-agent](/tools/nousresearch-hermes-agent.md) - The self-improving AI agent built by Nous Research (★ 210,911) - [AutoGPT](/tools/significant-gravitas-autogpt.md) - AutoGPT: Build, Deploy, and Run AI Agents (★ 185,420) - [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. (★ 175,664) - [prompts.chat](/tools/f-prompts-chat.md) - The world's largest open-source prompt library for AI (★ 165,025) - [transformers](/tools/huggingface-transformers.md) - 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models (★ 162,350) - [langflow](/tools/langflow-ai-langflow.md) - Langflow is a powerful platform for building and deploying AI-powered agents and workflows. (★ 151,311) - [dify](/tools/langgenius-dify.md) - Production-ready platform for agentic workflow development (★ 148,074) ## README (excerpt) ```text

RA-Agent logo 🖥️ 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](https://arxiv.org/abs/2603.01712) has been accepted to ICML 2026. The FT-Agent implementation is available in the [LLM fine-tuning guide](rdagent/app/finetune/llm/README.md). | | ACL 2026 Findings Acceptance | We are thrilled to announce that our paper [Reasoning as Gradient](https://arxiv.org/abs/2603.01692) has been accepted to ACL 2026 Findings. Execution traces are available at [Gome GPT-5 Traces](https://huggingface.co/datasets/amstrongzyf/Gome-GPT5-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](https://arxiv.org/abs/2505.15155) has been accepted to NeurIPS 2025 | | [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 our default backend for integration with multiple LLM providers. | | General Data Science Agent | [Data Science Agent](https://rdagent.readthedocs.io/en/latest/scens/data_science.html) | | Kaggle Scenario release | We release **[Kaggle Agent](https://rdagent.readthedocs.io/en/latest/scens/data_science.html)**, try the new features! | | Official WeChat group release | We created a WeChat group, welcome to join! (🗪[QR Code](https://github.com/microsoft/RD-Agent/issues/880)) | | 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](https://github.com/openai/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 ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/microsoft-rd-agent`](/api/graphcanon/tools/microsoft-rd-agent) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_