The emergence of solid solution high entropy alloys (HEAs) and complex concentrated alloys (CCAs) offers opportunities to design novel alloys with tailored strength and ductility. The growing community of integrated-computational materials engineering can benefit from implementing state-of-the-art solid-solution strengthening models to alloy design practices. Here we introduce pySSpredict, an open-source python-based toolkit that automates high-throughput calculations of solid-solution strengths of CCAs and thermodynamic properties. We present the functions of the pySSpredict code: (1) automating high-throughput calculations of solid-solution strengths for hundreds of thousands CCA compositions, (2) managing the data of thermodynamic calculations from databases or other software, and (3) visualizing and filtering the data to identify candidate alloys. The toolkit implements the latest theoretical edge and screw dislocation models for stress predictions for face-centered cubic (FCC) and body-centered cubic (BCC) alloys. We employed the “mechanism maps” by mapping the predicted stresses of different models to highlight the possible competing strengthening mechanisms for the TiNbZr+Mo alloys at various temperatures and compositions. We will demonstrate and discuss its application to filter and select single-phase and high-strength refractory alloys, which can be directly compared to high-throughput experimental hardness measurements.