Monitoring local seismic events is among the responsibilities of the German Federal Seismic Survey. This entity comprises a seismological subdepartment responsible for overseeing the operations of the German Regional Seismic Network and a data center tasked with collecting, archiving, and distributing continuous seismological and infrasound waveform data. As the amount of recorded seismic data dramatically increases every year, the imperative for an appropriate automatic real-time monitoring system becomes apparent. Leveraging advances in deep-learning methods in seismology, we develop a Python wrapper for the automatic estimation of hypocenter, magnitude and first-motion polarity in real time. To assess the performance of our algorithm, we compare the resulting event locations with catalogs of manually located events, with promising outcomes.