COR Brief
Agents & Automation

Rd Agent

RD-Agent is an open-source AI tool developed by Microsoft Research Asia designed to automate research and development workflows, especially for data-driven tasks such as model evolution, hypothesis testing, and quantitative strategy development. It integrates large language models (LLMs) like GPT-4 to automate repetitive tasks including data ingestion, hypothesis generation, model coding, testing, and reporting. The tool operates through an autonomous agent framework with distinct Research and Development components that iteratively improve through feedback and real-world application. RD-Agent supports diverse input types such as research papers, financial reports, and structured data, enabling it to assist in general research, identify data patterns in sectors like finance and healthcare, and automate feature engineering for quantitative systems. As an open-source project, it allows customization and scalability, with setup facilitated via Conda or Docker environments and requiring configuration of LLM API keys.

Updated Feb 12, 2026open-source

RD-Agent automates data-driven R&D workflows by combining autonomous agents with large language models to handle tasks from hypothesis generation to model implementation and reporting.

Pricing
open-source
Category
Agents & Automation
Company
Interactive PresentationOpen Fullscreen ↗
01
Automates the entire R&D process including data ingestion, hypothesis generation, model coding, testing, and outcome reporting.
02
Uses Research and Development agents that refine ideas and implementations based on continuous feedback and real-world practice.
03
Supports integration with large language models such as GPT-4 through API key configuration for enhanced hypothesis and model generation.
04
Available on GitHub for customization and ongoing updates to support more methods and application scenarios.

General Research Assistance

Automates reading research papers and implementing models to accelerate research workflows.

Data Pattern Identification

Analyzes financial or healthcare data to identify patterns and generate testable hypotheses.

Automated Quantitative Strategy Development

Supports automated feature engineering and model evolution for quantitative trading strategies.

1
Create Conda Environment
Set up a new Conda environment to ensure compatibility.
2
Activate Environment
Activate the Conda environment using conda activate.
3
Install RD-Agent
Clone and install RD-Agent from the official GitHub repository.
4
Configure LLM API Key
Set up the GPT model API key to enable LLM integration.
5
Run RD-Agent
Execute RD-Agent to automate R&D workflows; Docker integration is also supported.
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Pricing
Model: open-source

RD-Agent is free to use as an open-source tool; however, usage of external LLM APIs like GPT-4 may incur costs.

Assessment
Strengths
  • Eliminates repetitive manual tasks in R&D, allowing researchers to focus on complex work.
  • Processes diverse inputs such as research papers and financial reports to generate testable models.
  • Improves over time through iterative feedback between Research and Development agents.
  • Easy setup with Conda or Docker environments and adaptable to various LLMs.
  • Open-source nature enables customization and scalability.
Limitations
  • Requires external LLM API keys, which may lead to additional usage costs.
  • Dependent on the quality of the integrated LLM for accuracy in hypothesis and model generation.