The optimization, intensification, and scale-up of photochemical processes constitute a particularchallenge in a manufacturing environment geared primarily toward thermal chemistry. In this work, wepresent a versatile flow-based robotic platform to address these challenges through the integration ofreadily available hardware and custom software. Our open-source platform combines a liquid handler,syringe pumps, a tunable continuous-flow photoreactor, inexpensive Internet of Things devices, and anin-line benchtop nuclear magnetic resonance spectrometer to enable automated, data-rich optimizationwith a closed-loop Bayesian optimization strategy. A user-friendly graphical interface allows chemistswithout programming or machine learning expertise to easily monitor, analyze, and improvephotocatalytic reactions with respect to both continuous and discrete variables. The system'seffectiveness was demonstrated by increasing overall reaction yields and improving space-time yieldscompared with those of previously reported processes.