Retrieval-augmented generation
RAG-Anything
RAG-Anything: All-in-One RAG Framework
Superseded baseline#70 of 1,179 most-superseded · first seen Oct 14, 2025
Superseded — cited as a baseline and beaten by newer methods
1 papers critique it · 2 beat it on benchmarks
What papers say
Verbatim critique sentences, each from a paper that cites RAG-Anything as a baseline.
converts all the multimodal knowledge into textual form, at the cost of heavy preprocessing and loss of modality-specific information
Beaten on benchmarks
Head-to-head results where a newer method reports beating RAG-Anything. Values are copied from the source paper's tables — verify against the cited paper.
KG-RAG beats RAG-Anything
36.3 vs 24.8
All · [E-VQA benchmark]
mKG-RAG: Multimodal Knowledge Graph-Enhanced RAG for Visual Question AnsweringAutothinkRAG beats RAG-Anything
51.29 vs 44.36
Overall Accuracy · [MMLongBench Overall]
AutothinkRAG: Complexity-Aware Control of Retrieval-Augmented Reasoning for Image-Text Interaction
What to use instead
Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.
- Apr 7, 2026
- Graph-to-Frame RAGGraph-to-Frame RAG: Visual-Space Knowledge Fusion for Training-Free and Auditable Video ReasoningApr 6, 2026
- Apr 4, 2026
- AutoThinkRAGAutothinkRAG: Complexity-Aware Control of Retrieval-Augmented Reasoning for Image-Text InteractionMar 17, 2026
- Feb 27, 2026
- VimRAGVimRAG: Navigating Massive Visual Context in Retrieval-Augmented Generation via Multimodal Memory GraphFeb 13, 2026
- Feb 5, 2026
- Feb 1, 2026
- Oct 8, 2025