In response to these challenges, we have developed RamanSPy -a modular, open-source framework for integrated Raman Spectroscopy analytics in Python. RamanSPy is designed to systematise day-to-day workflows, enhance algorithmic development and validation, and accelerate the adoption of novel AI technologies into the RS field. Firstly, RamanSPy serves as a platform for general-purpose RS analytics supporting the RS data life cycle by providing a suite of ready-to-use modules for data loading, preprocessing, analysis and visualisation. By design, these functionalities are not tied to any specific technology or data type, thereby allowing integrative and transferable Fig. General Raman spectroscopy workflow and core features of RamanSPy . a, RamanSPy supports the Raman spectroscopic data analysis life cycle via a modular, loosely coupled architecture. RS data is parsed to a common data representation format, which is interfaced with preprocessing, analysis and visualisation tools within RamanSPy. The core features of RamanSPy include a comprehensive library of standardised, simple-touse procedures for data loading, preprocessing, analysis and visualisation. These modules are flexible and allow the incorporation of further techniques and in-house methods. For complete information about the modules available in RamanSPy, refer to the documentation at . b, An example workflow use case in RamanSPy: Raman data is loaded, preprocessed and analysed in a few lines of code.