HKUDS/RAG-Anything
"RAG-Anything: All-in-One RAG Framework"
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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.
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
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| xerj-org/xerj | 12.8K | +8.7K | 75 | today |
| DietrichGebert/ponytail | 171.6K | +8.1K | 75 | 5 days ago |
| tashfeenahmed/freellmapi | 39.0K | +3.7K | 75 | today |
| JuliusBrussee/caveman | 114.1K | +2.4K | 75 | 8 days ago |
| thesysdev/openui | 12.9K | +2.0K | 75 | 6 days ago |
| HKUDS/RAG-Anything | 24.0K | +0 | 75 | today |
| Category median | 39.0K | +3.7K | 75 | — |
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