Opportunistic sensors (OS), such as personal weather stations (PWSs), commercial microwave links (CMLs), and satellite microwave links (SMLs) can provide detailed rainfall information near the Earth's surface, complementary to information from ground-based weather radars and rain gauges from official networks. Due to their opportunistic nature, their data requires dedicated processing and quality control. A variety of open-source tools for these tasks exist. Their interoperability is low, however, and implementation details are too heterogeneous to allow joint community-driven development. Within the COST Action OpenSense (Opportunistic Precipitation Sensing Network) we have set out to improve this situation. Here we first summarize the state of the preexisting open-source tools and then describe the new OpenSense software ecosystem that we built. Specific attention is given to the three new software packages developed within OpenSense: <em>poligrain</em>, which simplifies common tasks for working with point, line and gridded sensor data; <em>pypwsqc</em>, which provides several quality control algorithms for PWS data, that were previously available as separate packages; <em>mergeplg</em>, which contains merging methods for point, line and grid data. These packages, with <em>poligrain</em> as the foundation, form the OpenSense software ecosystem for which we show an example use case. This software ecosystem shall serve as a community platform for reproducible research and operational integration of opportunistic sensor data for rainfall estimation.