STORM
Stanford AI system that automatically researches any topic via web search and generates Wikipedia-quality long-form articles with citations, free to use online.
STORM (Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking) is an AI research system developed at Stanford's Oval Lab that generates comprehensive, Wikipedia-style articles on any topic by simulating a research and writing process. It first conducts multi-perspective research by generating diverse questions from different viewpoints, searches the web for answers, then synthesizes a structured article with inline citations. The research paper describing STORM was published at NAACL 2024 and demonstrated significantly more comprehensive coverage than single-prompt article generation approaches. A hosted demo is freely available at storm.genie.stanford.edu, and the full system is open-source on GitHub with 14,000+ stars, supporting custom LLM backends for self-hosted deployments.
Key Features
- Multi-perspective research by simulating expert interviews across different viewpoints on a topic
- Hierarchical outline generation covering all major aspects before writing begins
- Long-form article generation with inline citations grounded in verified web sources
- Co-STORM interactive mode for user-guided collaborative research exploration
- Open-source Python library for integrating STORM-style research into custom AI workflows
- Support for multiple LLM backends including OpenAI, Anthropic, and Ollama for self-hosted use
Use Cases
- Journalists and analysts generating comprehensive background briefs on unfamiliar topics rapidly
- Students building detailed research outlines before writing academic papers or reports
- Knowledge base teams creating first-draft Wikipedia-style articles on company or domain topics
- Developers building automated research and report generation pipelines using the open-source library
Pros
- Peer-reviewed at NAACL 2024 - generates more comprehensive and well-cited articles than single-prompt approaches
- Free hosted demo at Stanford requires no signup for quick research on any topic
- Open-source with multi-perspective interview simulation for more thorough topic coverage
Cons
- Generation takes several minutes per article as it runs dozens of search queries and synthesis steps
- Output quality depends on web coverage - niche or emerging topics yield thinner articles
- Not suitable for real-time Q&A or conversational research - designed for comprehensive long-form output only
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