Open Deep Research vs RD-Agent
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A small TypeScript agent that researches a topic in rounds, using web search and an LLM to write a report.
Open source · MIT
An AI agent framework that automates parts of data science and R&D work, such as ML competitions and quant res
The short answer
Choose Open Deep Research if you need…
- Developers who want a readable example of a deep research agent to learn from or extend
- Students exploring how LLMs can be chained with search and scraping
- People who already have OpenAI and Firecrawl API keys and want a starting point for automated topic reports
Choose RD-Agent if you need…
- Researchers who want to test automated ML engineering agents on benchmark-style tasks
- Data scientists exploring agent-driven model and feature development
- Quant researchers interested in automated factor and strategy research
- Teams comparing LLM backends for autonomous experimentation
Side-by-side
| Open Deep Research | RD-Agent | |
|---|---|---|
| Type | AI model / agent | AI model / agent |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 19.9k | 15.2k |
| Setup difficulty | Intermediate | Advanced |
| How you get it | Clone and build from source | Install with a package manager |
| Main language | TypeScript | Python |
| Last code update | 7 months ago | 16 days ago |
What each one is
Open Deep Research
This project is a research assistant you run from the command line. You give it a question, and it generates search queries, reads the results, pulls out key learnings, and then follows new directions based on what it found. When it finishes, it writes a markdown report with findings and sources. The README says the goal is a simple implementation that stays under 500 lines of code so others can learn from it and build on it.
RD-Agent
R&D-Agent is a Microsoft open-source framework that uses large language model agents to automate research and development tasks, with a focus on data and models. The README describes it as letting AI drive data-driven AI work. The README also shows it applied to machine learning engineering benchmarks (MLE-bench) and to quantitative trading strategy research (RD-Agent(Q)).
Best for
Open Deep Research
- Developers who want a readable example of a deep research agent to learn from or extend
- Students exploring how LLMs can be chained with search and scraping
- People who already have OpenAI and Firecrawl API keys and want a starting point for automated topic reports
RD-Agent
- Researchers who want to test automated ML engineering agents on benchmark-style tasks
- Data scientists exploring agent-driven model and feature development
- Quant researchers interested in automated factor and strategy research
- Teams comparing LLM backends for autonomous experimentation
Strengths
Open Deep Research
- Short, readable codebase that the README says is kept under 500 lines
- Configurable breadth and depth let you trade thoroughness for run time and cost
- Supports several model providers via OpenAI-compatible endpoints, per the README
RD-Agent
- Backed by Microsoft Research with linked technical report and peer-reviewed papers cited in the README
- Documented benchmark results on MLE-bench, with run details published online
- Supports multiple LLM providers through LiteLLM, per the README
Not ideal for
Open Deep Research
- Users who want a polished end-user app: the README describes a command-line workflow with environment files and no graphical interface
- People without API keys or budget for them, since the README requires a Firecrawl key and an OpenAI key (or a local LLM endpoint)
- Anyone needing guaranteed-accurate research: the README does not describe any verification of sources or claims
RD-Agent
- People wanting a simple, ready-made app with no setup, since the README describes Python packages, documentation, and configuration rather than a finished end-user product
- Users who need results without paying for LLM API usage, since the agents rely on language model backends
- Non-Linux users, since the PyPI badge lists the platform as Linux
Trade-offs
Open Deep Research
- Requires paid or rate-limited API access (Firecrawl and an LLM); the README notes free tiers may hit rate limits
- The README does not describe how output quality or source accuracy is evaluated
- Setup involves several steps and environment variables, which can be fiddly for non-developers
RD-Agent
- Results and costs depend on the LLM backends used, and the README reports benchmark numbers with standard deviations rather than guarantees
- Installation and configuration details were not fully visible in the truncated README, so setup may take effort
- Some scenarios are limited; the web UI currently excludes the data_science scenario
Getting started
Open Deep Research
Install dependencies
npm installStart the research assistant
npm startRD-Agent
Install from PyPI (package named in badge)
pip install rdagentFrequently asked questions
What is the difference between Open Deep Research and RD-Agent?
Open Deep Research: A small TypeScript agent that researches a topic in rounds, using web search and an LLM to write a report. RD-Agent: An AI agent framework that automates parts of data science and R&D work, such as ML competitions and quant res. Open Deep Research suits developers who want a readable example of a deep research agent to learn from or extend, while RD-Agent suits researchers who want to test automated ml engineering agents on benchmark-style tasks.
Are Open Deep Research and RD-Agent free?
Open Deep Research: Free and open source. RD-Agent: Free and open source.
Which is easier to set up, Open Deep Research or RD-Agent?
Open Deep Research is easier to get started with (rated intermediate); RD-Agent is rated advanced and usually needs more setup.
Is Open Deep Research or RD-Agent more popular?
Open Deep Research has more GitHub stars (19.9k) than RD-Agent (15.2k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Open Deep Research and RD-Agent?
Other options people compare them with include STORM, DeerFlow, SWE-agent, Open SWE, UFO.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.