Is RAG-Anything superseded?

RAG-Anything (Retrieval-augmented generation): superseded — cited as a baseline and beaten by newer methods. 1 paper(s) critique it, 2 beat it on benchmarks — #70 of 1179 most-superseded. Sub-problem: cluster led by MMGraphRAG. Newer alternatives in the same sub-problem include VideoStir, Graph-to-Frame RAG, MG$^2$-RAG, AutoThinkRAG, AgenticOCR.

Method Drift›Retrieval-augmented generation

Superseded baseline#70 of 1,179 most-superseded

RAG-Anything

RAG-Anything: All-in-One RAG Framework

Retrieval-augmented generation · 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superseded — cited as a baseline and beaten by newer methods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”
— UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities

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 · All [E-VQA benchmark]
36.3 vs 24.8
mKG-RAG: Multimodal Knowledge Graph-Enhanced RAG for Visual Question Answering
KG-RAG beats RAG-Anything · All [InfoSeek benchmark]
40.5 vs 28.4
mKG-RAG: Multimodal Knowledge Graph-Enhanced RAG for Visual Question Answering
AutothinkRAG beats RAG-Anything · Overall Accuracy [DocBench Overall]
82.13 vs 75.47
AutothinkRAG: Complexity-Aware Control of Retrieval-Augmented Reasoning for Image-Text Interaction
AutothinkRAG beats RAG-Anything · Overall Accuracy [MMLongBench Overall]
51.29 vs 44.36
AutothinkRAG: Complexity-Aware Control of Retrieval-Augmented Reasoning for Image-Text Interaction

Newer alternatives

Recent methods in the same sub-problem, not yet superseded in the knowledge base.