However, implicit solvation models might not be able to capture all relevant solvent-solute interactions, and break down, e.g., for participation of a solvent molecule in a reaction. Then, cluster approaches explicitly accounting for the solvent molecules are required. As these additional molecules strongly increase the computational effort, especially in the solute-solvent cluster generation step, KS-DFT is often not applicable, leaving efficient xTB methods a reasonable choice for this task. While they allow for the calculation of larger systems, the accuracy of KS-DFT is sometimes necessary for describing certain types of chemistry, where the parametrizations are insufficient, the model in general is not flexible enough, or the approximations are too crude. In such cases, combined approaches like embedding or QM:QM (KS-DFT:TB) approaches can be used. Investigating the dynamics of the system of interest directly is another application for xTB methods where they offer a computationally feasible alternative to KS-DFT-based ab initio molecular dynamics, with the possibility to reach systems with millions of atoms. Moreover, the combination of xTB methods with enhanced sampling techniques, such as metadynamics with collective variables (e.g. the root-mean-square displacement), enables a quite general procedure for the exploration of potential energy surfaces (PES) as realized in the conformer-rotamer ensemble sampling tool (CREST) program package. Finally, the application to materials and extended systems, like crystal structure prediction or simulation of liquids, is an emerging opportunity for the application of xTB methods. In this work, we focus on the current state of the main software packages for xTB methods: the xtb program suite and the tblite li-brary. We begin with a brief theoretical excursion behind the xTB framework, explaining the basics of the Tight Binding theory and its GFNspecific parametrization. Following that, we describe the technical side of the xTB-based packages, including their code ecosystem, licensing, and their integration and interaction with the broader scientific computational environment. In the application section, we summarize the most common uses of xTB methods, namely conformer sampling, structure optimizations, AIMD simulations, and data generation. To complete the picture, we review several new features available in the xtb package: aISS docking tool, QM/MM module, mcGFNFF force field for molecular crystal calculations, and a post-SCF implicit solvation model CPCM-X. Finally, through citation dynamics, we demonstrate the significant influence of the xTB methods, and GFN2-xTB in particular, in the field of computational chemistry.