RD-Agent

One-Minute Overview#

RD-Agent is a revolutionary AI R&D automation framework designed to automate the most critical and valuable aspects of industrial R&D processes. By coordinating two core components - "R" (Research) for proposing new ideas and "D" (Development) for implementing them, it helps researchers and engineers achieve complete automation from data mining to model iteration. Whether for quantitative trading, data science competitions, or academic research, RD-Agent can significantly enhance R&D efficiency, reduce costs, and improve outcome quality.

Core Value: Automating the entire R&D process through AI, enabling data-driven intelligent evolution to dramatically improve R&D efficiency and outcome quality.

Quick Start#

Installation Difficulty: Medium - Requires Docker environment, Python setup, and proper LLM configuration

# Create Conda environment
conda create -n rdagent python=3.10
conda activate rdagent

# Install RD-Agent
pip install rdagent

# Health check
rdagent health_check --no-check-env

Is this suitable for me?

• ✅ Quantitative Trading Research: Automatically iterate factor-models to achieve approximately 2× higher ARR than traditional factor libraries while using 70% fewer factors
• ✅ Data Science Competitions: Automated model tuning and feature engineering for platforms like Kaggle
• ✅ Academic Research Assistance: Automatically read research papers and implement model structures or build datasets
• ❌ Non-Linux Systems: Currently only supports Linux environments
• ❌ No LLM Access: Requires ChatCompletion, json_mode, and embedding query capabilities

Core Capabilities#

1. Quantitative Trading Automation - Addressing traditional quantitative R&D inefficiency#

• Automates full-stack quantitative strategy R&D through RD-Agent(Q) framework with coordinated factor-model co-optimization User Value: Achieving approximately 2× higher ARR than benchmark factor libraries while using over 70% fewer factors at a cost under $10

2. Data Mining Agent - Addressing data exploration and model iteration challenges#

• Automatically proposes data and model ideas and implements them by gaining knowledge from data User Value: Significantly reduces data exploration and model iteration time, improving data utilization

3. Research Copilot - Addressing knowledge acquisition and application efficiency#

• Automatically reads research papers, financial reports, and implements model structures or builds datasets User Value: Accelerates knowledge discovery and application, shortening research cycles

4. Kaggle Agent - Addressing competition optimization challenges#

• Automated model tuning and feature engineering to improve competition performance User Value: Achieves better results in data science competitions like Kaggle

Technology Stack and Integration#

Development Language: Python Key Dependencies: Docker, LiteLLM (supporting multiple LLM providers), OpenAI API Integration Method: Command-line tool, API interface

Maintenance Status#

• Development Activity: High - Actively maintained by Microsoft with regular new versions and feature updates
• Recent Updates: Recent - Recent developments including NeurIPS 2025 paper acceptance and updated MLE-bench results
• Community Response: Active - Provides Discord and WeChat community support with regular technical documentation and examples

Documentation and Learning Resources#

• Documentation Quality: Comprehensive - Provides detailed installation guides, configuration instructions, and scenario tutorials
• Official Documentation: https://rdagent.readthedocs.io/en/latest/
• Sample Code: Provides complete examples for various scenarios including quantitative trading and data science