HKUDS

    HKUDS/RAG-Anything

    #565 this week

    "RAG-Anything: All-in-One RAG Framework"

    llm
    multi-modal-rag
    retrieval-augmented-generation
    Python
    MIT
    24.0K stars
    2.8K forks
    24.0K GitHub watchers
    Updated Oct 22, 2026
    View on GitHub

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    Where it stands

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

    Ranks #10 among LLM repositories tracked this week, and #4 over the last 30 days; #565 of 2,531 repositories GStars tracks this week (top 22%).

    4 releases in the last 90 days, the latest today, with 68 contributors on GitHub.

    Peaked at #10 in the weekly ranking the week of Oct 12, its best in the past year.

    Use Cases & Benefits

    • Provides an all-in-one multimodal retrieval-augmented generation (RAG) framework for processing and querying documents with text, images, tables, and equations.
    • Delivers seamless integration of diverse content modalities in a single pipeline, enabling comprehensive multimodal knowledge extraction and retrieval without multiple specialized tools.
    • Use for academic research to analyze and query complex scientific papers containing interleaved text, figures, tables, and mathematical formulas.
    • Use for enterprise knowledge management to unify and search across heterogeneous document formats like PDFs, Office files, and images with rich multimodal content.
    • Use for financial report analysis to extract insights from structured tables, charts, and textual commentary within a unified multimodal retrieval system.

    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

    Watch-outs

    No watch-outs from GStars' checks.

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

    From the README

    Next-Generation Multimodal Intelligence

    Modern documents increasingly contain diverse multimodal content—text, images, tables, equations, charts, and multimedia—that traditional text-focused RAG systems cannot effectively process. RAG-Anything addresses this challenge as a comprehensive All-in-One Multimodal Document Processing RAG system built on LightRAG.

    As a unified solution, RAG-Anything eliminates the need for multiple specialized tools. It provides seamless processing and querying across all content modalities within a single integrated framework. Unlike conventional RAG approaches that struggle with non-textual elements, our all-in-one system delivers comprehensive multimodal retrieval capabilities.

    Users can query documents containing interleaved text, visual diagrams, structured tables, and mathematical formulations through one cohesive interface. This consolidated approach makes RAG-Anything particularly valuable for academic research, technical documentation, financial reports, and enterprise knowledge management where rich, mixed-content documents demand a unified processing framework.

    Excerpt from the HKUDS/RAG-Anything 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

    2.8K+189

    Open issues

    100-7

    Compared with peers in LLM

    Fastest-growing repositories GStars tracks in this cohort for the week of Oct 18, by 7-day star growth

    HKUDS/RAG-Anything compared with LLM repositories by stars, 7-day star growth, health score and last release
    RepositoryStars+7dHealthLast release
    xerj-org/xerj12.8K+8.7K75today
    DietrichGebert/ponytail171.6K+8.1K755 days ago
    tashfeenahmed/freellmapi39.0K+3.7K75today
    JuliusBrussee/caveman114.1K+2.4K758 days ago
    thesysdev/openui12.9K+2.0K756 days ago
    HKUDS/RAG-Anything24.0K+075today
    Category median39.0K+3.7K75—

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