In fishes, swimming performance is considered an important metric to measure fitness, dispersal and migratory abilities. The swimming performance of individual larval fishes is often integrated into models to make inferences on how environmental parameters affect population-level dynamics (e.g. connectivity). However, little information exists regarding how experimental protocols affect the swimming performance of marine fish larvae. In addition, the technical setups used to measure larval fish swimming performance often lack automation and accurate control of water quality parameters and flow velocity. In this study, we automated the control of multi-lane swimming chambers for small fishes by developing an open-source algorithm. This automation allowed us to execute repeatable flow scenarios and reduce operator interference and inaccuracies in flow velocity typically associated with manual control. Furthermore, we made structural modifications to a prior design to reduce the areas of lower flow velocity. We then validated the flow dynamics of the new chambers using computational fluid dynamics and particle-tracking software. The algorithm provided an accurate alignment between the set and measured flow velocities and we used it to test whether faster critical swimming speed (U-crit) protocols (i.e. shorter time intervals and higher velocity increments) would increase U-crit of early life stages of two tropical fish species [4-10-mm standard length (SL)]. The U-crit of barramundi (Lates calcarifer) and cinnamon anemonefish (Amphiprion melanopus) increased linearly with fish length, but in cinnamon anemonefish, U-crit started to decrease upon metamorphosis. Swimming protocols using longer time intervals (more than 2.5 times increase) negatively affected U-crit in cinnamon anemonefish but not in barramundi. These species-specific differences in swimming performance highlight the importance of testing suitable U-crit protocols prior to experimentation. The automated control of flow velocity will create more accurate and repeatable data on swimming performance of larval fishes. Integrating refined measurements into individual-based models will support future research on the effects of environmental change.