This platform represents an advanced extension of our initial ServerHub, HELAO . It is capable of executing experiments both sequentially and in parallel, and allows for the integration of an active learning decision-maker to orchestrate tasks across instruments. The complete workflow is illustrated in Figure . At the beginning, researchers configure experimental setups and schedule multiple, dynamic batches of experiments through a user-friendly and modular interface (Supp. 1). The user interface (UI) is constructed using HTML, and CSS with dynamic functionalities implemented in Node.js and facilitates easy browsing with a diverse array of electrochemical protocols. This interaction layer is further enhanced by Flask 49 , a Python-based web framework, to streamline user engagement. Once single or multiple experimental batches are designed, the configurations are passed to the Python back-end, requiring no additional user intervention, and are translated to FastAPI requests. The central orchestrator will then efficiently schedule and manage high-throughput experimentation in an asynchronous manner. After the execution of all requested experimental batches, the operator receives a response from the orchestrator with a comprehensive report detailing all experimental steps, their timestamps, and outcomes.