Recommender systems play an important role in our online experience and are involved in a wide range of decisions. Multiple stakeholders demand explanations for the corresponding algorithmic predictions. These demands have triggered significant interest from researchers in academia and industry in methods able to explain why recommendations are provided to users. Despite this interest, no comprehensive toolkit for explainable recommender systems is available to the community yet. In this talk, we introduce XRecSys, a software toolkit that includes several state-of-the-art recommender algorithms, and explainability methods thereof.