In this article, we present an active learning software workflow with data-efficient sampling and hyperparameter tuning for multi-composition Gaussian approximation potentials (GAP) in mixtures of molten salts. This workflow is a culmination of a number of best practices we have gained through our past publications . We begin by providing a brief overview of the multi-composition active learning workflow for GAP, termed as AL4GAP, with an emphasis on the Python classes of AL4GAP. The details of the specific methodologies can be found in our prior publications. The AL4GAP workflow currently supports arbitrary molten mixtures spanning 11 cations (Li, Na, K, Rb, Cs, Mg, Ca, Sr, Ba and two heavy species, Nd and Th) and 4 anions (F, Cl, Br and I). We provide an overview of the software framework built around this active learning workflow for sampling arbitrary multi-component molten salt mixture melts. Each of the individual Python classes is subsequently detailed, with additional details provided in the GitHub repository () . In the results section, we showcase the utility of the AL4GAP for the high-throughput generation of five independent GAP models for multi-composition binary-mixture melts each of increasing complexity with respect to multiply charged ionic system, and electronic structure namely: LiCl-KCl, NaCl-CaCl 2 , KCl-NdCl 3 , CaCl 2 -NdCl 3 , and KCl-ThCl 4 . The results section provides two different simulation scenarios for the developed potentials. The first scenario employs the molten NaCl-CaCl 2 as a model system to understand the liquid structure as functions of composition and temperature. For the second scenario, we discuss the chemical physics insights drawn from melt simulations for all five chemical systems with equal mixture composition at 1200K.