{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-stanford-oval--storm","slug":"stanford-oval--storm","name":"storm","type":"webapp","url":"http://storm.genie.stanford.edu","page_url":"https://unfragile.ai/stanford-oval--storm","categories":["research"],"tags":["agentic-rag","deep-research","emnlp2024","knowledge-curation","large-language-models","naacl","nlp","report-generation","retrieval-augmented-generation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"github-stanford-oval--storm__cap_0","uri":"capability://planning.reasoning.perspective.guided.multi.turn.question.generation.for.research","name":"perspective-guided multi-turn question generation for research","description":"Generates research questions through simulated conversations between a Wikipedia writer and topic expert LLM agents, where questions are grounded in perspective discovery from similar existing articles rather than direct prompting. The system surveys related Wikipedia articles to extract diverse viewpoints, then uses these perspectives to guide the question-asking process, ensuring comprehensive topic coverage from multiple angles. This two-agent conversational approach with perspective injection produces more structured and comprehensive research directions than naive question generation.","intents":["Generate research questions that cover multiple perspectives on a topic automatically","Discover what aspects of a topic are typically covered in authoritative sources","Ensure research depth by simulating expert-writer dialogue grounded in real article patterns","Avoid redundant or shallow questions by learning from existing knowledge organization"],"best_for":["researchers building automated knowledge synthesis systems","teams generating long-form content at scale with citation requirements","knowledge curation platforms needing multi-perspective coverage"],"limitations":["Perspective discovery requires access to similar existing articles (Wikipedia or equivalent), limiting effectiveness for novel/niche topics","Multi-turn conversation overhead adds latency compared to single-prompt question generation","Quality depends on availability of reference articles; sparse topic domains may yield limited perspectives","Conversation context window constraints may limit question depth for very broad topics"],"requires":["Python 3.9+","LLM API access (OpenAI, Anthropic, or compatible provider)","Internet access for retrieving reference articles and search results","knowledge-storm package (PyPI version 1.1.1+)"],"input_types":["topic string (text)","optional reference article URLs or content"],"output_types":["structured question list with perspective labels","conversation history with writer/expert turns"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stanford-oval--storm__cap_1","uri":"capability://planning.reasoning.hierarchical.outline.generation.with.citation.anchoring","name":"hierarchical outline generation with citation anchoring","description":"Generates multi-level article outlines (sections, subsections, key points) using collected research references, where each outline node is anchored to specific retrieved sources. The system structures the outline hierarchically to match Wikipedia article conventions, then maps each outline element to supporting citations from the knowledge curation phase. This enables the subsequent writing stage to generate text with proper in-line citations by maintaining explicit outline-to-source mappings throughout the generation pipeline.","intents":["Create structured article plans that map directly to available sources","Ensure every outline point has citation support before writing begins","Generate Wikipedia-style hierarchical structures automatically from research data","Enable downstream article generation to produce properly cited content without additional source lookup"],"best_for":["automated content generation systems requiring citation integrity","knowledge bases building Wikipedia-like article collections","research platforms needing structured knowledge organization"],"limitations":["Outline quality depends entirely on research phase coverage; gaps in collected sources create outline gaps","Hierarchical depth is constrained by LLM context window and citation density","Cannot generate outlines for topics with insufficient retrieved sources","Outline-to-source mapping may become stale if sources are updated or removed"],"requires":["completed knowledge curation phase with collected references","LLM API access for outline generation","structured reference data with URLs and content snippets"],"input_types":["topic string","InformationTable object containing collected research references","optional outline style/template specification"],"output_types":["hierarchical outline structure (nested sections/subsections)","outline nodes with citation references","source-to-outline mapping metadata"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stanford-oval--storm__cap_10","uri":"capability://automation.workflow.batch.article.generation.with.pipeline.orchestration","name":"batch article generation with pipeline orchestration","description":"Orchestrates the complete STORM pipeline (knowledge curation → outline generation → article writing → polishing) for batch processing of multiple topics, implemented through STORMWikiRunner that manages state, error handling, and progress tracking across pipeline stages. The system executes each stage sequentially for each topic, maintaining intermediate results and enabling resumption from failure points. This orchestration layer abstracts pipeline complexity and enables users to generate article collections without managing individual stage invocations.","intents":["Generate multiple articles in batch without manual pipeline orchestration","Resume interrupted batch jobs from the last completed stage","Track progress and handle errors across multi-stage pipelines","Generate article collections for knowledge bases at scale"],"best_for":["knowledge base platforms generating article collections","content creation teams producing multiple articles","research platforms automating large-scale knowledge synthesis"],"limitations":["Sequential pipeline execution limits parallelization; batch processing is slow (minutes per article)","No built-in distributed execution; scaling requires external orchestration (Kubernetes, etc.)","Intermediate state storage requires external persistence; in-memory state is lost on failure","Error handling is basic; failures in one article don't prevent processing of others but may require manual intervention","Resource consumption is high; batch processing of 100+ articles requires significant API quota"],"requires":["LLM API access with sufficient quota","internet search capability","Python 3.9+","knowledge-storm package"],"input_types":["list of topic strings","pipeline configuration (LLM provider, search depth, etc.)"],"output_types":["generated articles (markdown or HTML)","pipeline execution logs","progress metadata"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stanford-oval--storm__cap_11","uri":"capability://memory.knowledge.encoder.based.semantic.similarity.for.perspective.discovery","name":"encoder-based semantic similarity for perspective discovery","description":"Uses sentence encoders (embeddings) to compute semantic similarity between research questions and existing article content, enabling the system to discover relevant perspectives from similar articles without explicit keyword matching. The encoder system converts text to dense vector representations, enabling efficient similarity search across large article collections. This semantic approach discovers perspectives that keyword-based methods would miss, improving the diversity and relevance of research questions.","intents":["Discover relevant perspectives from similar articles using semantic similarity","Find related articles without explicit keyword matching","Improve perspective diversity by identifying semantically similar but lexically different content","Enable efficient similarity search across large article collections"],"best_for":["systems requiring semantic understanding of article content","perspective discovery for diverse topic coverage","large-scale article collections with similarity search needs"],"limitations":["Encoder quality depends on training data; domain-specific encoders may be needed for specialized topics","Embedding computation adds latency (100-500ms per article for large collections)","Similarity thresholds require tuning; too low yields irrelevant perspectives, too high misses valid ones","Semantic similarity may conflate different perspectives that use similar language","Encoder models require significant memory; scaling to millions of articles requires distributed storage"],"requires":["sentence encoder model (e.g., sentence-transformers)","vector storage for embeddings (optional, for large collections)","Python 3.9+"],"input_types":["text snippets or full articles","research questions"],"output_types":["similarity scores","ranked similar articles","perspective labels"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stanford-oval--storm__cap_2","uri":"capability://text.generation.language.internet.grounded.long.form.article.generation.with.inline.citations","name":"internet-grounded long-form article generation with inline citations","description":"Generates full-length Wikipedia-style articles (2000+ words) by consuming hierarchical outlines and mapped citations, producing text with inline citations that reference specific retrieved sources. The system uses the outline structure to guide section-by-section generation, maintaining citation context from the outline-to-source mappings to ensure every claim references a specific source. This multi-stage approach (outline → section generation → citation insertion) produces coherent long-form content with proper attribution without requiring additional source retrieval during writing.","intents":["Generate complete, citation-rich articles from research data automatically","Produce Wikipedia-quality long-form content with proper inline citations","Create articles that maintain factual grounding throughout multi-section content","Enable batch article generation for knowledge bases without manual citation work"],"best_for":["knowledge base platforms generating article collections at scale","research platforms automating long-form content creation","teams building citation-rich documentation systems"],"limitations":["Article coherence depends on outline quality; poor outlines produce disjointed articles","Citation density may be uneven across sections if source distribution is skewed","Long-form generation requires multiple LLM API calls, increasing latency (minutes per article)","Generated text may require editorial review for factual accuracy despite source grounding","Context window constraints may limit article length or force section-by-section generation"],"requires":["completed outline generation with citation mappings","LLM API access with sufficient context window (8k+ tokens recommended)","source content/snippets from knowledge curation phase"],"input_types":["hierarchical outline with citation anchors","InformationTable with source content","topic metadata and style preferences"],"output_types":["full-length article text (markdown or HTML)","inline citations with source references","article metadata (word count, section structure)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stanford-oval--storm__cap_3","uri":"capability://search.retrieval.internet.search.integration.with.multi.source.retrieval","name":"internet search integration with multi-source retrieval","description":"Integrates with internet search APIs (Bing, Google, or custom) to retrieve relevant sources for research questions, implementing a retrieval module that handles query expansion, result ranking, and content extraction. The system executes search queries derived from research questions, collects results with metadata (URLs, snippets, relevance scores), and extracts full-text content from retrieved pages. This retrieval layer feeds the knowledge curation phase with grounded source material, enabling all downstream stages to operate on internet-sourced information.","intents":["Retrieve relevant sources for automatically generated research questions","Collect internet-sourced information for knowledge curation","Extract and structure web content for use in article generation","Enable multi-source aggregation for comprehensive topic coverage"],"best_for":["systems requiring current/real-time information beyond training data","knowledge curation platforms needing diverse source coverage","research tools automating literature discovery"],"limitations":["Search quality depends on query formulation; poorly phrased questions yield irrelevant results","Web content extraction is brittle; page structure changes break parsing","Search API rate limits constrain retrieval scale (typically 100-1000 queries/day)","Retrieved content may contain outdated, biased, or low-quality information","Full-text extraction from paywalled or JavaScript-heavy sites may fail"],"requires":["search API credentials (Bing Search API, Google Custom Search, or equivalent)","internet connectivity","web scraping/content extraction library (BeautifulSoup, Playwright, or similar)"],"input_types":["search queries (strings)","optional query expansion parameters"],"output_types":["ranked search results with URLs and snippets","extracted full-text content from retrieved pages","source metadata (publication date, domain, relevance score)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stanford-oval--storm__cap_4","uri":"capability://memory.knowledge.knowledge.base.construction.with.dynamic.concept.organization","name":"knowledge base construction with dynamic concept organization","description":"Builds and maintains a hierarchical knowledge base (mind map) that organizes collected information into a dynamic concept structure, implemented as the KnowledgeBase class that stores information as nested concepts with relationships. The system continuously reorganizes information as new sources are added, maintaining a shared conceptual space that reduces cognitive load during knowledge curation. This knowledge base serves as the source of truth for outline generation and article writing, enabling both automated and human-collaborative workflows to reference a consistent information structure.","intents":["Organize collected research information into a hierarchical concept structure","Enable dynamic reorganization of information as new sources are discovered","Provide a shared reference structure for both automated and human-collaborative workflows","Reduce cognitive load during long research sessions by maintaining structured information"],"best_for":["collaborative research platforms with human-AI interaction","knowledge curation systems requiring information reorganization","teams building topic-specific knowledge bases"],"limitations":["Knowledge base organization quality depends on LLM-driven concept extraction","Reorganization overhead increases with knowledge base size (O(n) operations per update)","No built-in persistence; requires external storage for long-term knowledge base management","Concept hierarchy may become unbalanced or redundant without active curation","Merging conflicting concept structures from multiple sources requires manual intervention"],"requires":["LLM API access for concept extraction and reorganization","source information from knowledge curation phase","optional external storage backend for persistence"],"input_types":["collected source information (text snippets, URLs)","topic context and domain specification"],"output_types":["hierarchical concept structure (KnowledgeBase object)","concept-to-source mappings","concept relationship metadata"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stanford-oval--storm__cap_5","uri":"capability://planning.reasoning.human.ai.collaborative.discourse.with.moderator.coordination","name":"human-ai collaborative discourse with moderator coordination","description":"Implements a three-agent collaborative discourse protocol (Co-STORM) where human users, LLM expert agents, and a moderator agent participate in structured knowledge curation conversations. The moderator agent generates thought-provoking questions inspired by retrieved information not yet discussed, expert agents answer questions grounded in external sources and raise follow-up questions, and human users can observe passively or actively steer the conversation. The system maintains conversation history and the shared knowledge base, enabling the moderator to track discussed vs. undiscussed information and guide the discourse toward comprehensive coverage.","intents":["Enable human experts to collaborate with AI agents in knowledge curation","Automatically generate follow-up questions that explore undiscussed aspects","Maintain conversation coherence and coverage tracking across long discourse sessions","Allow humans to steer research direction while AI handles information synthesis"],"best_for":["research teams combining human expertise with AI-powered information synthesis","knowledge curation platforms requiring human-in-the-loop workflows","educational systems teaching research methodology through AI collaboration"],"limitations":["Moderator question quality depends on retrieved information coverage; sparse topics limit guidance","Three-agent coordination adds significant latency (seconds per turn)","Conversation history grows unbounded; long sessions may exceed LLM context windows","Human steering requires active participation; passive observation provides limited value","No built-in conflict resolution for disagreements between expert agents"],"requires":["LLM API access for three concurrent agent instances","internet search capability for expert grounding","knowledge base system for tracking discussed information","conversation state management (in-memory or persistent)"],"input_types":["topic string","human utterances (optional)","conversation history"],"output_types":["expert agent responses with citations","moderator follow-up questions","updated knowledge base with new information","conversation transcript"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stanford-oval--storm__cap_6","uri":"capability://tool.use.integration.multi.provider.language.model.abstraction.with.unified.api","name":"multi-provider language model abstraction with unified api","description":"Provides a unified language model interface (lm.py module) that abstracts multiple LLM providers (OpenAI, Anthropic, Ollama, local models) behind a common API, enabling seamless provider switching without pipeline code changes. The system handles provider-specific details (API authentication, request formatting, response parsing, token counting) and exposes standardized methods for completion, chat, and function calling. This abstraction layer enables users to swap providers based on cost, latency, or capability requirements without modifying the knowledge curation or article generation logic.","intents":["Use different LLM providers interchangeably without code changes","Optimize for cost by switching between expensive (GPT-4) and cheaper (Ollama) models","Reduce latency by using local models for some tasks and cloud models for others","Maintain compatibility with multiple LLM APIs as they evolve"],"best_for":["teams wanting flexibility in LLM provider selection","cost-conscious deployments mixing cloud and local models","researchers experimenting with different model capabilities"],"limitations":["Abstraction adds ~50-100ms overhead per API call due to wrapper layer","Provider-specific features (vision, function calling) may not be fully abstracted","Token counting varies by provider; cost estimates may be inaccurate","Rate limiting and quota management must be handled per provider","Model capability differences (reasoning, instruction-following) still affect output quality"],"requires":["API credentials for at least one provider (OpenAI, Anthropic, etc.)","Python 3.9+","knowledge-storm package"],"input_types":["provider configuration (API key, model name, endpoint)","prompt text or chat messages"],"output_types":["completion text","token usage metadata","structured function call results"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stanford-oval--storm__cap_7","uri":"capability://safety.moderation.article.polishing.and.fact.checking.with.iterative.refinement","name":"article polishing and fact-checking with iterative refinement","description":"Implements an optional polishing phase that refines generated articles through iterative LLM-based fact-checking and improvement, verifying claims against source material and improving clarity/coherence. The system re-examines article sections against their source citations, identifies unsupported claims or contradictions, and generates refined versions. This post-generation refinement improves article quality without requiring additional source retrieval, leveraging the citation mappings from earlier phases to validate factual accuracy.","intents":["Improve generated article quality through automated fact-checking","Identify and fix unsupported claims before publication","Enhance clarity and coherence of generated text","Reduce editorial review burden by pre-validating factual accuracy"],"best_for":["high-quality content generation systems requiring fact-checking","knowledge bases prioritizing accuracy over speed","editorial platforms automating quality assurance"],"limitations":["Fact-checking quality depends on source material completeness; gaps in sources enable false claims","Iterative refinement adds 20-40% latency to article generation","LLM-based fact-checking may miss subtle inaccuracies or context-dependent claims","Refinement may reduce article length or remove nuanced claims to ensure source alignment","No guarantee of perfect accuracy; human editorial review still recommended"],"requires":["completed article generation with citation mappings","LLM API access for refinement iterations","source content for fact-checking validation"],"input_types":["generated article text","citation mappings and source content","optional refinement parameters (strictness, focus areas)"],"output_types":["refined article text","fact-check report with identified issues","confidence scores for claims"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stanford-oval--storm__cap_8","uri":"capability://automation.workflow.streamlit.based.interactive.research.interface","name":"streamlit-based interactive research interface","description":"Provides a web-based frontend (Streamlit demo) that enables non-technical users to run STORM and Co-STORM pipelines through an interactive UI, handling topic input, progress visualization, and result display. The interface abstracts pipeline complexity, manages LLM configuration, and presents results in readable formats (formatted articles, conversation transcripts, knowledge base visualizations). This frontend enables researchers and content creators to use STORM without writing code, lowering the barrier to entry for knowledge curation workflows.","intents":["Enable non-technical users to generate research articles without coding","Visualize research progress and knowledge base organization during curation","Configure LLM providers and parameters through a user-friendly interface","Export generated articles and research data in multiple formats"],"best_for":["non-technical researchers and content creators","teams deploying STORM as an internal research tool","educational institutions teaching research methodology"],"limitations":["Streamlit performance degrades with large knowledge bases (>10k concepts)","Real-time progress visualization requires polling; latency may be noticeable","Limited customization compared to programmatic API usage","No built-in authentication; requires external security layer for production deployment","Browser-based interface limits offline usage and requires internet connectivity"],"requires":["Streamlit 1.0+","Python 3.9+","knowledge-storm package","LLM API credentials configured"],"input_types":["topic string (text input)","optional configuration parameters (LLM provider, search depth)"],"output_types":["rendered article HTML","conversation transcript display","downloadable article files (markdown, PDF)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stanford-oval--storm__cap_9","uri":"capability://data.processing.analysis.structured.data.extraction.and.information.table.construction","name":"structured data extraction and information table construction","description":"Constructs structured InformationTable objects that organize collected research data (sources, snippets, metadata) into queryable tables with schema-aware operations, enabling downstream stages to access information programmatically. The system extracts and structures information from retrieved sources, maintaining relationships between sources, concepts, and claims. This structured representation enables outline generation and article writing to query information efficiently without re-parsing raw source text.","intents":["Organize collected research information into queryable data structures","Enable efficient information lookup during outline and article generation","Maintain source-to-claim mappings for citation purposes","Support programmatic analysis of collected information"],"best_for":["systems requiring structured access to research data","knowledge bases with complex information relationships","research platforms enabling programmatic data analysis"],"limitations":["Information table construction requires schema definition; schema mismatches cause data loss","Querying large tables (>100k rows) may be slow without indexing","No built-in deduplication; duplicate information from multiple sources requires manual cleanup","Schema evolution is difficult; adding new fields requires table reconstruction"],"requires":["collected source information from retrieval phase","schema definition for information structure","Python 3.9+"],"input_types":["raw source text and metadata","schema specification"],"output_types":["InformationTable objects","queryable data structures","source-to-information mappings"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","LLM API access (OpenAI, Anthropic, or compatible provider)","Internet access for retrieving reference articles and search results","knowledge-storm package (PyPI version 1.1.1+)","completed knowledge curation phase with collected references","LLM API access for outline generation","structured reference data with URLs and content snippets","LLM API access with sufficient quota","internet search capability","knowledge-storm package"],"failure_modes":["Perspective discovery requires access to similar existing articles (Wikipedia or equivalent), limiting effectiveness for novel/niche topics","Multi-turn conversation overhead adds latency compared to single-prompt question generation","Quality depends on availability of reference articles; sparse topic domains may yield limited perspectives","Conversation context window constraints may limit question depth for very broad topics","Outline quality depends entirely on research phase coverage; gaps in collected sources create outline gaps","Hierarchical depth is constrained by LLM context window and citation density","Cannot generate outlines for topics with insufficient retrieved sources","Outline-to-source mapping may become stale if sources are updated or removed","Sequential pipeline execution limits parallelization; batch processing is slow (minutes per article)","No built-in distributed execution; scaling requires external orchestration (Kubernetes, etc.)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.40101684480331967,"quality":0.39,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.35,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-05-06T15:12:23.810Z","last_scraped_at":"2026-05-03T13:58:29.527Z","last_commit":"2025-09-30T18:07:21Z"},"community":{"stars":28156,"forks":2565,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=stanford-oval--storm","compare_url":"https://unfragile.ai/compare?artifact=stanford-oval--storm"}},"signature":"3CyUDG7DiQqTtl9iWOOqYT+utkJ1k3qOpxjBrVGcm8MK1WjWmHQGjEE3jxXh2tSstVb6IkhgW2tG8mU9ZyIfCg==","signedAt":"2026-06-22T06:46:09.172Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/stanford-oval--storm","artifact":"https://unfragile.ai/stanford-oval--storm","verify":"https://unfragile.ai/api/v1/verify?slug=stanford-oval--storm","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}