R&D-Agent is an open-source tool designed to automate data-driven research and development processes, particularly in AI model development and data manipulation. It targets researchers and engineers seeking to streamline iterative R&D cycles by enabling AI to propose, implement, and refine ideas. The primary benefit is increased productivity through automated experimentation and learning.

How It Works

The framework operates on a two-component model: 'R' for proposing new ideas and 'D' for implementing them. It leverages Large Language Models (LLMs) to read and extract information from various sources like research papers and financial reports, then translates this into executable code for model implementation or data feature engineering. An iterative feedback loop allows the agent to learn from results and improve its R&D capabilities over time.

Quick Start & Requirements

• Installation: pip install rdagent
• Prerequisites: Python 3.10 or 3.11, Docker (for most scenarios), LLM API access (e.g., OpenAI, Azure OpenAI) configured via .env file. LiteLLM backend is also supported.
• Setup: Requires configuring LLM API keys and potentially downloading datasets or setting up specific environment variables for different scenarios (e.g., Kaggle API, PhysioNet credentials).
• Documentation: rdagent.readthedocs.io
• Live Demo: rdagent.azurewebsites.net

Highlighted Details

• Supports multiple R&D scenarios: Finance (factor/model evolution, report analysis), Medical (model evolution), General (research paper analysis, Kaggle competitions).
• Integrates with Qlib for quantitative trading research.
• Offers a UI for monitoring application results (rdagent ui).
• Supports LiteLLM for flexible LLM backend integration.

Maintenance & Community

• Actively developed by Microsoft.
• Community support available via Discord: discord.gg/ybQ97B6Jjy
• Publicly available documentation and roadmap.

Licensing & Compatibility

• Licensed under the MIT License.
• Compatible with commercial use and closed-source linking, with a disclaimer regarding financial advice and user liability.

Limitations & Caveats

The tool is presented "as is" and is not intended for financial investment advice. Users are responsible for assessing risks, ensuring responsible AI use, and complying with regulations. LLM implementation capabilities may require iterative refinement.