Here, we report an open-source workflow, UmetaFlow, that applies combinatorial computational algorithms for high-throughput liquid chromatography tandem mass spectrometry (LC-MS/MS) data processing and analysis, using OpenMS 3.0 tools for feature detection, map alignment, adduct annotation, re-quantification and feature linking, spectral matching, and structural and formula predictions via SIRIUS and CSI:FingerID . OpenMS algorithms have been implemented for generating all the files necessary for GNPS Feature-Based Molecular Networking (FBMN) and Ion Identity Molecular Networking (IIMN) . All these steps are complemented with Python scripts for data integration. The workflow is implemented in a workflow manager, Snakemake , making it easy to operate in diverse HPC or cloud environments. We evaluated and benchmarked UmetaFlow and demonstrated that it ranks as one of the best tools for feature detection, quantification and marker selection when compared with other untargeted metabolomics software tools, indicating that UmetaFlow can be used as a tool for large-scale metabolomics data processing and analysis.