Open Deep Research vs STORM
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
Open-source LLM system that researches a topic online and drafts a Wikipedia-style article with citations.
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 STORM if you need…
- Developers who want to build a customizable research or report-writing pipeline
- Writers who want a first-draft outline and source list before writing
- Researchers exploring a new topic who want a structured overview with references
- Teams experimenting with different language models and search engines
Side-by-side
| Open Deep Research | STORM | |
|---|---|---|
| Type | AI model / agent | AI model / agent |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 19.9k | 31.8k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Clone and build from source | Install with a package manager |
| Main language | TypeScript | Python |
| Last code update | 7 months ago | 13 months 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.
STORM
STORM is a research system built on large language models. Given a topic, it searches the internet, simulates conversations to gather information, builds an outline, and writes a full-length article with citations. Co-STORM extends it so a human can take part in the discussion and steer the research. The README says the output is not publication-ready and often needs significant editing, but experienced Wikipedia editors have found it useful for pre-writing.
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
STORM
- Developers who want to build a customizable research or report-writing pipeline
- Writers who want a first-draft outline and source list before writing
- Researchers exploring a new topic who want a structured overview with references
- Teams experimenting with different language models and search engines
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
STORM
- Modular design: language models and search engines can be swapped, per the README's litellm and retriever support
- Produces outlines and cited text rather than a single unsourced answer
- Open source under the MIT license with an active research and release history
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
STORM
- Anyone needing publication-ready text, since the README says articles often need significant edits
- People without an API key for a language model or search provider, since the system depends on them
- Users who need a finished no-setup app, since the README centers on a Python package and code setup
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
STORM
- The README says output is not publication-ready and often needs significant editing
- Requires configured language model and search API keys, which may carry costs
- Installation and customization assume Python and some comfort with code
Getting started
Open Deep Research
Install dependencies
npm installStart the research assistant
npm startSTORM
Install the library from PyPI
pip install knowledge-stormClone the repository for source install
git clone https://github.com/stanford-oval/storm.gitFrequently asked questions
What is the difference between Open Deep Research and STORM?
Open Deep Research: A small TypeScript agent that researches a topic in rounds, using web search and an LLM to write a report. STORM: Open-source LLM system that researches a topic online and drafts a Wikipedia-style article with citations. Open Deep Research suits developers who want a readable example of a deep research agent to learn from or extend, while STORM suits developers who want to build a customizable research or report-writing pipeline.
Are Open Deep Research and STORM free?
Open Deep Research: Free and open source. STORM: Free and open source.
Is Open Deep Research or STORM more popular?
STORM has more GitHub stars (31.8k) than Open Deep Research (19.9k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Open Deep Research and STORM?
Other options people compare them with include RD-Agent, DeerFlow, SWE-agent, Open SWE, LangChain.
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.