To further demonstrate the integration process, Listing 2 offers an insight into how a serialized PyTorch model can be loaded and utilized within the CrystFEL suite using the LibTorch API. These few lines of code are enough to integrate TORO into CrystFEL without the need for recompiling the application. While this endeavour exemplifies TORO's adaptability potential, it's worth noting that TORO's integration within CrystFEL is still in its early stages and would benefit from continuous feedback and refinements from the CrystFEL community. An initial implementation of TOROs CrystFEL plugin is available at . Figure illustrates an example of end-to-end processing pipeline using TORO. It highlights how TORO leverages high-performance computing for real-time indexing with its Python implementation and for offline processing using either the Python or CrystFEL versions. The depicted schematic is modular and scalable, ensuring ease of adaptation and integration into high-performance computing infrastructures or edge computing setups across diverse beamlines.