The ability to track the positions and poses of multiple animals in three-dimensional (3D) space in real time is highly desired by non-human primate (NHP) researchers in behavioral and systems neuroscience. This capability enables the analysis of social behaviors involving multiple NHPs and supports closed-loop experiments. Although several animal 3D pose tracking systems have been developed, most are difficult to deploy in new environments and lack real-time analysis capabilities. To address these limitations, we developed MarmoPose, a deep-learning-based, real-time 3D pose tracking system for multiple common marmosets, an increasingly critical NHP model in neuroscience research. This system can accurately track the 3D poses of multiple marmosets freely moving in their home cage with minimal hardware requirements. By employing a marmoset skeleton model, MarmoPose can further optimize 3D poses and estimate invisible body locations. Additionally, MarmoPose achieves high inference speeds and enables real-time closed-loop experimental control based on events detected from 3D poses.