In recent years, a Concept Drift topic have gained popularity in scientific papers. Most papers, however, concentrate on describing new techniques for detecting this phenomenon without giving clear use cases or explaining how to use such detectors in a data stream pipeline. In this paper, we propose a complete toolkit for utilizing various types of Concept Drift detectors and visualizing their outcome. Additionally, we propose some metrics that could be suitable for comparing the effectiveness of various detectors.