Clouds represent the main issue affecting optical satellite images. Cloud-free scenes available at specific date is crucial in a wide range of monitoring applications. Differently, Synthetic Aperture Radar (SAR) sensors provide orthogonal information with respect to optical satellite, that enable the retrieval of information lost in optical images due to cloud cover. In the context of an increasing availability of both optical and SAR images, thank to the Sentinel constellation, a number of deep learning method have emerged in recent papers. These methods aim to reconstruct optical data polluted by cloud phenomena, exploiting SAR and optical images. We present an open-source software based on the Orfeo ToolBox and TensorFlow, that provide a framework to apply methods processing Sentinel-1 and Sentinel-2 images. Our software comes with a few pre-trained models that can be used out-of-the-box to remove clouds in Sentinel-2 images from Sentinel-1 and Sentinel-2 time series.