whereismyai
RD-Agent
RD-Agent is Microsoft's open-source research agent for machine-learning engineering, Kaggle work and quant experiments.
RD-Agent is a Microsoft open-source agent that runs research-and-development loops for machine-learning engineering. The authors report it as the top-performing machine-learning engineering agent on MLE-bench in their published results, and they have a Kaggle scenario, a general data-science agent and R&D-Agent-Quant for trading research (NeurIPS 2025). LiteLLM is the default backend for many model providers. A web UI (rdagent serverui) shows traces in real time. Related papers include FT-Dojo / FT-Agent for autonomous fine-tuning (ICML 2026) and a technical report on the overall framework. A live demo and documentation are linked from the repository.
RD-Agent at a glance
- Best for: Researcher, Software engineer, Finance
- Runs on: open source, Web
- Pricing: Free and open source; model API usage is billed by the provider
- Released: April 2024
- Category: AI research tool
- Website: rdagent.azurewebsites.net
Key features
- ML engineering agent evaluated on MLE-bench
- Kaggle scenario and a general data-science agent
- R&D-Agent-Quant for trading research
- LiteLLM backend for many model providers
- Web UI for traces and live interaction
- Papers, a technical report and a live demo
Use it for
- Run an MLE-bench-style model-engineering loop
- Have the agent try a Kaggle-style tabular task
- Watch an experiment trace in the web UI
Alternatives to RD-Agent
- Paper2Agent — It enables direct interaction with research papers and automatic result reproduction.
- OpenResearch — Run parallel research workflows locally or on remote compute.
- LLM Wiki Research — Compiles parallel agent research into source-backed markdown knowledge bases.
- rLLM — Enables reinforcement learning training for AI agents with minimal code changes.
- AI Scientist v3 — Automates end-to-end scientific research from task descriptions to complete papers.
- DiscoveryRSI — Achieves high actionable-reference rates and supports multi-agent evolutionary research search.
- MiroThinker — It handles multi-step reasoning and complex research tasks with high stability.
- Lune — Accesses trusted knowledge from top conference papers for AI agents.
- Auto Deep Researcher — Automates experiment operations to reduce manual babysitting of training loops.
- Arbor — It grows a hypothesis tree and tests changes on held-out data.
- MiroFlow — Achieves top rankings on multiple agentic benchmarks with modular architecture.
- Jevon Tane — It provides a rigorous, pre-registered framework for AI agents to conduct science together.
All RD-Agent alternatives