MOTIVATION: Synthetic cellular tagging technologies play a crucial role in cell fate and lineage-tracing studies. Their integration with single-cell and spatial transcriptomics assays has heightened the need for scalable software solutions to analyze such data. However, previous methods are either designed for a subset of tagging technologies, or lack the performance needed for large-scale applications. RESULTS: To address these challenges, we developed Quick Clonal Analysis Toolkit (QuiCAT), an end-to-end Python-based package that streamlines the extraction, clustering, and analysis of synthetic tags from sequencing data. QuiCAT outperforms existing pipelines in both speed and accuracy. Its outputs are widely compatible with the Python ecosystem for single-cell and spatial transcriptomics data analysis packages allowing seamless integrations and downstream analyses. QuiCAT provides users with two workflows: a reference-free approach for extracting and mapping synthetic tags, and a reference-based approach for aligning tags against known sequences. We validate QuiCAT across diverse datasets, including population-level data, single-cell and spatially resolved transcriptomics, and benchmarked it against the two most recently published tools. Our computational optimizations enhance performance while improving accuracy. AVAILABILITY: QuiCAT is available as a Python package to be installed. The source code is available at https://github.com/theislab/quicat.