Dynamic isotope labeling data provides crucial information about the operation of metabolic pathways and are commonly generated via liquid chromatography-mass spectrometry (LC-MS). Metabolome-wide analysis is challenging as it requires grouping of metabolite features over different samples. We developed DynaMet for fully automated investigations of isotope labeling experiments from LC-high-resolution MS raw data. DynaMet enables untargeted extraction of metabolite labeling profiles and provides integrated tools for expressive data visualization. To validate DynaMet we first used time course labeling data of the model strain Bacillus methanolicus from (13)C methanol resulting in complex spectra in multicarbon compounds. Analysis of two biological replicates revealed high robustness and reproducibility of the pipeline. In total, DynaMet extracted 386 features showing dynamic labeling within 10 min. Of these features, 357 could be fitted by implemented kinetic models. Feature identification against KEGG database resulted in 215 matches covering multiple pathways of core metabolism and major biosynthetic routes. Moreover, we performed time course labeling experiment with Escherichia coli on uniformly labeled (13)C glucose resulting in a comparable number of detected features with labeling profiles of high quality. The distinct labeling patterns of common central metabolites generated from both model bacteria can readily be explained by one versus multicarbon compound metabolism. DynaMet is freely available as an extension package for Python based eMZed2, an open source framework built for rapid development of LC-MS data analysis workflows.