Virtual machine (VM) scheduling is one of the critical tasks in cloud computing. Many works have attempted to incorporate machine learning, especially reinforcement learning, to empower VM scheduling procedures. Although improved results are shown in several demo simulators, the performances in real-world scenarios are still underexploited. In this paper, we design a practical VM scheduling platform, i.e., VMAgent, to assist researchers in developing their methods on the VM scheduling problem. VMAgent consists of three components: simulator, scheduler, and visualizer. The simulator abstracts three general realistic scheduling scenarios (fading, recovering, and expansion) based on Huawei Cloud’s scheduling data, which is the core of our platform. Flexible configurations are further provided to make the simulator compatible with practical cloud computing architecture (i.e., Multi Non-Uniform Memory Access) and scenarios. Researchers then need to instantiate the scheduler to interact with the simulator, which is also pre-built in various types (e.g., heuristic, machine learning, and operations research) of scheduling algorithms to speed up the algorithm design. The visualizer, as an auxiliary component of the simulator and scheduler, facilitates researchers to conduct an in-depth analysis of the scheduling procedure and comprehensively compare different scheduling algorithms. We believe that VMAgent would shed light on the AI for the VM scheduling community, and the demo video is presented in https://bit.ly/vmagent-demo-video.