Creating UI tests for mobile applications is a difficult and time-consuming task. As such, there has been a considerable amount of work carried out to automate the generation of mobile tests—largely focused upon the goals of maximizing code coverage or finding crashes. However, comparatively fewer automated techniques have been proposed to generate a highly sought after type of test: usage-based tests. These tests exercise targeted app functionalities for activities such as regression testing. In this paper, we present the AVGUST tool for automating the construction of usage-based tests for mobile apps. AVGUST learns usage patterns from videos of app executions collected by beta testers or crowd-workers, translates these into an appagnostic state-machine encoding, and then uses this encoding to generate new test cases for an unseen target app. We evaluated AVGUST on 374 videos of use cases from 18 popular apps and found that it can successfully exercise the desired usage in 69% of the tests. AVGUST is an open-source tool available at https://github.com/felicitia/UsageTesting-Repo/tree/demo. A video illustrating the capabilities of AVGUST can be found at: https://youtu.be/LPICxVd0YAg.