Abstract: Automated processes are indispensable for continuous high-throughput applications while offering unmatched reproducibility and requiring limited supervision compared to manual operation. Automation has long been integrated in production settings, but in pre-clinical research, it is mainly limited to specific scenarios due to the dynamic environment where protocols and priorities constantly evolve, and scientists lack a background in automation. Flow cytometry is a primary high-throughput research tool with single-cell resolution that can analyze over 40 markers on thousands of samples that will benefit from the automation of sample processing, sample acquisition and data analysis. Novel smaller and more versatile robotic platforms can be integrated into a research environment and be operated by scientists with limited expertise in robotics or programming. We took advantage of these novel tools to establish a semi-automated workflow for flow cytometry where human intervention is still required but is limited to moving samples among different robotic platforms. As a result, we improved reproducibility, reduced processing times, and lowered costs. To automate the sample processing step, multiple liquid handler systems were validated to dispense reagents, miniaturize assays, and exchange buffers without centrifugation. In comparison to manual processing, signal variation dropped from 30% to less than 2% and consumable costs were reduced by 80% while cell recovery increased by 85%. To automatically acquire processed samples, three flow cytometry platforms with robotic enhancements, designed to target different assays, were validated leading to 12-16 hours of uninterrupted operation with a variation in signal intensity lower than 1.5% while maximizing the instrument usage by doubling the daily throughput. Finally, automation of the data analysis was achieved by combining the batch processing of manual data analysis software with Python-based custom scripts for a scalable workflow targeting both low and high parameter panels, while allowing to meet project-specific requirements. This resulted in more than 90% reduction in employee time to analyze the data and compile reports. In summary, we have developed a semi-automated workflow for our flow cytometry pipeline that offers flexibility for evolving protocols and requires a limited capital investment and preliminary knowledge on control systems, robotics, or programming. As a result, we increased our throughput, minimized the user error, and maximized reproducibility while significantly reducing the processing time, reagent costs and hands-on employee time.