STORM

// Overview

STORM is an open-source knowledge-curation system from Stanford OVAL that turns a topic prompt into a long, citation-grounded, Wikipedia-style article. It splits the job into two stages — research and writing — so the LLM has structured material to draw from instead of being asked to write a long article from memory.

In the research stage STORM first discovers different perspectives on the topic, then runs simulated conversations between a writer agent and an expert agent grounded in web search results, building up an outline and a set of citations. In the writing stage it generates the full article section by section against those citations. A companion mode, Co-STORM, opens the loop so a human can collaborate with the agents while they research.

STORM is built on DSPy, which makes its components modular and tunable. It supports multiple retrieval back-ends — You.com, Bing, DuckDuckGo, Tavily and document-based retrievers — and works as a library you can drop into your own pipeline or via the hosted demo at storm.genie.stanford.edu.

// What it does

• Two-stage pipeline: perspective-guided research, then long-form sectioned writing
• Simulated writer↔expert conversations grounded in retrieved web results
• Co-STORM mode where humans collaborate with the agents during research
• Modular DSPy-based architecture so language models and retrievers can be swapped
• Multiple retrieval back-ends: You.com, Bing, DuckDuckGo, Tavily, document search
• Outputs an outline plus a long article with inline citations

// Getting started

Install the knowledge-storm package, configure a language model and a retriever, then call STORMWikiRunner to research a topic and generate the article.

pip install knowledge-storm

export OPENAI_API_KEY=sk-...
export YDC_API_KEY=...  # You.com retriever

from knowledge_storm import STORMWikiRunner, STORMWikiRunnerArguments, STORMWikiLMConfigs
from knowledge_storm.lm import LitellmModel
from knowledge_storm.rm import YouRM

lm_configs = STORMWikiLMConfigs()
engine_args = STORMWikiRunnerArguments(output_dir="./out")
retriever_module = YouRM(ydc_api_key="...", k=3)
runner = STORMWikiRunner(engine_args, lm_configs, retriever_module)
runner.run(topic="Small language models", do_research=True, do_generate_article=True)

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Pre-writing a long article on an unfamiliar topic before a human editor takes over
• Generating internal topic primers that come with citations to the underlying sources
• Researching a topic interactively as a team using Co-STORM
• Studying or extending agent research pipelines built on DSPy

// How STORM compares

STORM alongside other open-source agent frameworks & builders tools AI/TLDR tracks, ranked by GitHub stars.