Mass spectrometry data can be numerous and highly complex, creating a need for tools that can analyze large metabolomics datasets. UmetaFlow is a workflow built for automated, highthroughput untargeted mass spectrometry-based metabolomics data processing and analysis using OpenMS algorithms. It allows for fast, scalable, and reproducible analysis through a workflow manager, Snakemake, but also for prototyping or smaller-scale interactive data processing through the Jupyter notebooks. UmetaFlow connects OpenMS, SIRIUS and CSI:FingerID, as well as GNPS FBMN, for processing, annotation, and data interpretation. Here, we show that we have successfully processed 1,245 raw files, validated UmetaFlow with in-house data, and benchmarked it using the publicly available datasets MTBLS733 and MTBLS736. UmetaFlow proved to be an efficient tool when compared with widely used untargeted metabolomics software, both in feature detection, quantification, and marker selection. We anticipate that it will become a broadly used tool for research groups that produce large metabolomics datasets or want to analyze large amounts of publicly available data.