How to Run STORM for AI-Assisted Research

A Stanford project called STORM can take a topic, research it, and produce a Wikipedia-like article. In the example I generated about artificial intelligence, it organized material into areas such as business, healthcare, education, and security, with references alongside the text. The result is a research draft, not a page published on Wikipedia.

The project is open source, and I am running the interface on my computer. This demonstration still calls the OpenAI API; the web search also uses an external service. Before installing anything, you can try the hosted STORM demo and look at articles it has already generated. At the time of the recording, the site also showed a forthcoming human–AI collaboration mode.

STORM stands for Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking. Its research process considers different perspectives before organizing and writing an article. I previously encountered a bug that required a code change, although the installation in this demonstration works without that fix.

To install the version shown in this July 2024 tutorial, open a terminal in your chosen directory, clone the [STORM repository](https://github.com/stanford-oval/storm), and change into its folder. I use conda to create an environment named `storm` with Python 3.11, activate it, and then run `pip install -r requirements.txt`. Project files and setup steps may have changed since this recording.

Open the project folder in the editor, reactivate the environment if the terminal resets, and create a `secrets.toml` file using the repository’s getting-started template. For this walkthrough I configure OpenAI rather than Azure, then add an OpenAI API key. Keep credentials private and consult the current repository instructions for the supported configuration.

I also create a You.com web search API key and add it to the configuration. At the time, Berman says the service offered a trial but otherwise cost money; pricing and availability can change. The project also supports command-line use, but I prefer the simple web interface for this demonstration.

For the 2024 Streamlit interface, I copy the secrets file into the `.streamlit` folder under the frontend, change into the `demo_light` directory, and run `streamlit run storm.py`. The local server starts and opens the research interface. These are the steps shown in the video, so check the current documentation before reproducing them.

I start a research task on dogs. The logs show the system brainstorming perspectives and making requests to the OpenAI endpoint, followed by browsing activity. The run finishes with a structured article covering subjects such as taxonomy, evolution, physical characteristics, and behavior.

The article has numbered references that can be selected to open the underlying sources and see highlights. That makes it easier to inspect the research, but a citation alone does not establish that a source is accurate or actually supports the nearby claim; readers should verify both before relying on the draft.

If you do not want to use OpenAI’s API, I point to an example using vLLM with Mistral for the model side, although I have not set it up myself. Web search would still need a source of external information unless you built a replacement. I also saw discussion of Ollama, but did not get it working and did not spend much time trying.