Laboratory automation has been identified as a key strategy for increasing the rate at which new discoveries are made in chemistry and materials science. Automation serves two central purposes: 1) to increase the experimental throughput via continuous and/or parallel execution of otherwise repetitive, manual tasks, and 2) to foster more standardized and reproducible results. While the history of automation in chemistry traces back to the mid-20 th century, recent years have seen a "renaissance" of automation in both academic and industrial laboratories. Advances in robotics and engineering have enabled the automation of increasingly challenging laboratory operations such as thin-film fabrication, sample handling under inert gas, or dosing of powders, gels and slurries. Integrating such automated modules into larger workflows has demonstrated the potential to tackle increasingly complex scientific challenges in an automated fashion. This surge in automated experimentation has produced a growing market of instruments, particularly platform solutions consisting of multiple experimental modules. Arguably, the most prominent such systems have come from companies such as Chemspeed Technologies and Unchained Labs, and have shown the enormous potential to enable highly complex discovery workflows across various fields in chemistry and materials science. Examples include the discovery of battery electrolytes, new catalysts, organic laser materials, polymer formulations, or stereoselective synthesis. The current phase in the evolution of automated laboratories involves the transition from static, predefined automation workflows to modular and flexible labs where decisions about the next experimental steps are adaptively made in real time (Fig. ). Particularly with recent strides in data-driven design and machine learning, this has the potential to optimize the use of automated resources, and thereby accelerate scientific discoveries. Especially against the background of modularity and adaptive decision making, the availability of open software interfaces (application programming interfaces, APIs) for automated platforms are essential for the seamless incorporation into flexible, customizable workflows. At the same time, such dynamic APIs are often not provided by instrument manufacturers, whose software tends to follow a workflow-and instrument-centric philosophy. In fact, available APIs are often constrained to the configuration and post-run evaluation of static workflows. This presents a major barrier to integrating further instruments into the workflow, or to making adaptive data-driven decisions in real time.