MOTIVATION: Spectral flow cytometry provides greater insights into cellular heterogeneity by simultaneous measurement of up to 50 markers. However, analysing such high-dimensional (HD) data is complex through traditional manual gating strategy. To address this gap, we developed CAFEs (Cell Analyzer for Flow Experiments) as an open-source Python-based web application with a graphical user interface. Built with Streamlit, CAFE incorporates libraries such as Scanpy for single-cell analysis, Pandas and PyArrow for efficient data handling, and Matplotlib, Seaborn, Plotly for creating customizable figures. Its robust toolset includes density-based downsampling, dimensionality reduction, batch correction, Leiden-based clustering, cluster merging, and annotation. RESULTS: Using CAFE, we demonstrated analysis of a human PBMC dataset of 350 000 cells identifying 16 distinct cell clusters. CAFE can generate publication-ready figures in real time via interactive slider controls and dropdown menus, eliminating the need for coding expertise and making HD data analysis accessible to all. AVAILABILITY AND IMPLEMENTATION: CAFE is licensed under MIT and is freely available at https://github.com/mhbsiam/cafe.