In this work we achieved this using our newly developed GRAPES (GRay-level Analysis of ParticlES) python toolkit that rapidly transforms large tomography datasets into easily queryable tables for tracking grey-level change. As part of this work, we improved the GREAT algorithm previously demonstrated by our group in Wade et al. . Our new GREAT2 algorithm, used in the GRAPES toolkit and presented here, is able to process large datasets rapidly. Wade et al. reports processing about 1400 particles per day with GREAT. By comparison the updated GREAT2 algorithm is able to process ∼10,000 similar particles in 37 seconds. The GRAPES toolkit calculates a variety of useful particle characteristics and contains utility functions designed to help users process the dataset created. As we show in this article the GRAPES toolkit can work effectively with operando data where the aim is to track grey-level change in the same particle at different time steps. Alongside the manuscript, the GRAPES python toolkit and a user friendly GRAPES graphical user interface (GUI) have been made publicly available.