Seasonal climate forecasting in West Africa and the Sahel has historically relied on subjective, consensus-based approaches that lack reproducibility and detailed traceability. To address these limitations, we developed wass2s, an open-source Python tool that automates an objective, multi-method forecasting workflow tailored for the region. Combining traditional statistical techniques with machine learning algorithms, wass2s integrates data acquisition, preprocessing, model training, cross-validation, ensemble consolidation, and verification within a single scalable pipeline. It enhances forecast skill and transparency by enabling reproducible, skill-assessed seasonal climate forecasting. This tool supports operational meteorological services by improving methodological rigor and facilitating capacity building, thereby advancing climate services and decision-making in West Africa and the Sahel.