In their respective studies, Flores-Leonar et al. and Coley et al. identified the essential elements of the task management chain in automated chemistry laboratories through a platform-based approach. Bai et al. provided a clear schematic representation of this platform-based approach. The aforementioned schematic representation is reproduced for the sake of simplicity and adapted for a large and entire laboratory automation case, as illustrated in Figure . The primary distinctions between the original Bai et al. scheme and the proposed modification pertain to the incorporation of additional entities essential to the operation of large-scale systems, such as the logistics laboratory management (chemicals ERP, solvent and waste management) module. This module is complemented by a dedicated data processor (Processor) unit, which oversees the systematic processing of scientific equipmentgenerated data and facilitates their potential utilization for structural elucidation and chemometric analysis. A further modification to the original scheme is the incorporation of sample transfer and robotics into the Executor section. As previously described by Thurow and Junginger and also discussed by Wolf et al., when considering a complete laboratory automation system, it is likely that the system will have to be open and decentralized. This requires the combination of different types of robotics and automation, including mobile platforms, SCARA and 6axis arms, and fluidics. The present paper concentrates on the segment of the automation process that commences with the Coordinator and concludes with the Executor. In particular, it examines the scheduling aspect of the process, which is represented by the Scheduler in the Coordinator block. Furthermore, the structure of the process is considered, including the Equipment Application Programming Interface (API) located in the Executor. The scheduling of tasks is of paramount importance in a global automation system, as it converts the workflows into a sequence of physical operations, orchestrates the tasks in the appropriate order and at the optimal timing, taking into account the status of the different equipment and the completion of previous tasks. For this, a novel multilayered Git-based 37 opensource laboratory automation scheduling architecture, designated GLAS for Git-based Lab Automation Scheduler or eventually Get Lab Automation Simplified is presented here. The global lab scheduling is based on a multilayered and repetitive container 38 architecture, which is coded in a Git environment. The system has been designed to be applicable in large, entirely automated laboratories, which present a significant variety of scientific and logistic equipment. Furthermore, it can also be implemented into smaller and localized laboratory automation projects that combine a limited number of scientific equipment and sample manipulators.