Machine-learning-based multiscale modeling connects different length and timescales by training a coarse-grained, but cheap, model on data coming from a fine-scale, but expensive, model. Corresponding training protocols can be very complex as they usually involve different simulation codes and contain many different stages (e.g., pre-training, active learning, finetuning, distillation, …) that are difficult to implement efficiently, and cumbersome to interpret, analyze, and reproduce. For the problem of training machine-learning interatomic potentials (MLIPs) on quantum-mechanical simulations, we propose AutoPot, a software for automating the construction and archiving of MLIPs. AutoPot is based on BlackDynamite, a software operating parametric tasks, e.g., running simulations, or single-point ab initio calculations, in a highly-parallelized fashion, and Motoko, an event-based workflow manager for orchestrating interactions between the tasks. The initial version of AutoPot supports selection of training configurations from large training candidate sets, and on-the-fly selection from molecular dynamics simulations, using Moment Tensor Potentials as implemented in MLIP-2, and single-point calculations of the selected training configurations using VASP. Another strength of AutoPot is its flexibility: BlackDynamite tasks and orchestrators are Python functions to which own existing code can be easily added and manipulated without writing complex parsers. Therefore, it will be straightforward to add other MLIP and ab initio codes, and manipulate the Motoko orchestrators to implement other training protocols. Moreover, functionalities of AutoPot are not limited to training MLIPs but could be applied to other problems, such as learning continuum models from atomistic data.