GStars
    stanford-oval

    stanford-oval/storm

    An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.

    llm
    nlp
    agentic-rag
    deep-research
    emnlp2024
    knowledge-curation
    large-language-models
    Python
    MIT
    31.8K stars
    3.0K forks
    188 GitHub watchers
    Updated Nov 9, 2026
    View on GitHub

    Build with Backblaze B2

    SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.

    Explore Backblaze Labs

    Loading star history...

    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    No releases in the last 90 days; the most recent was 655 days ago, with 23 contributors on GitHub.

    Use Cases & Benefits

    • Generates comprehensive, citation-backed reports on topics by leveraging large language models and internet-based research.
    • Enables multi-perspective, iterative question asking and simulated expert conversations to improve depth and breadth of knowledge curation.
    • Use for automating pre-writing research and outline generation for Wikipedia-style article creation.
    • Use for collaborative human-AI knowledge curation with dynamic mind maps to organize and steer complex topic discussions.
    • Use for customizing and integrating various language and retrieval models to build tailored deep research and report generation pipelines.

    Should you depend on it?

    License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.

    License

    MIT

    Permissive: commercial use, modification and redistribution are allowed with attribution.

    Community files

    • README: present
    • Contributing guide: present

    Watch-outs

    • Warning: No commits in over a year (last push Sep 2025).

    General guidance from repository metadata, not legal advice. How we score

    From the README

    STORM is a LLM system that writes Wikipedia-like articles from scratch based on Internet search. Co-STORM further enhanced its feature by enabling human to collaborative LLM system to support more aligned and preferred information seeking and knowledge curation.

    While the system cannot produce publication-ready articles that often require a significant number of edits, experienced Wikipedia editors have found it helpful in their pre-writing stage.

    More than 70,000 people have tried our live research preview. Try it out to see how STORM can help your knowledge exploration journey and please provide feedback to help us improve the system 🙏!

    Excerpt from the stanford-oval/storm README on GitHub (MIT license). © its authors.Read the full README on GitHub

    Daily snapshots

    Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots

    Forks

    3.0K+106

    Open issues

    119+11

    Compared with peers in LLM

    Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth

    View category →
    stanford-oval/storm compared with LLM repositories by stars, 7-day star growth, health score and last release
    RepositoryStars+7dHealthLast release
    NVIDIA/auto-ontology11.4K+8.5K75—
    thedotmack/claude-mem115.8K+4.7K75today
    DietrichGebert/ponytail188.4K+4.4K7512 days ago
    affaan-m/ECC300.8K+4.3K7540 days ago
    Graphify-Labs/graphify145.7K+3.8K75today
    stanford-oval/storm31.8K—24655 days ago
    Category median145.7K+4.4K75—
    Compare storm with auto-ontology, claude-mem, ponytail →

    Discover Repositories

    Search across tracked repositories by name or description