Block Low-Rank factorizations are a very useful compromise between accuracy and memory and computational requirements; as such, they can be used effectively in many situation [1]. In the context of Domain Decomposition (DD) methods, Block Low-Rank factorizations can serve as incomplete solvers for local subdomain problems, providing a good balance between accuracy and efficiency. Furthermore, they can be employed as smoothers within Algebraic Multigrid (AMG) cycles, either in block-Jacobi methods or in more general DD approaches, where they help reduce high-frequency error components while maintaining computational tractability, or inside distribuBlock Low-Rank factorizations are a very useful compromise between accuracy and memory and computational requirements; as such, they can be used effectively in many situations [1], including in the context of algebraic multigrid preconditionersted coarse solvers [2]. We discuss here the integration of the Block Low-Rank solvers in the framework of the PSCToolkit [4] software package, as employed in the solution of very large linear systems coming from realistic applications [3]. In particular, we will report on experiments in the framework of two EU funded projects, the Energy Oriented Center of Exellence EoCoE-3, and the dealii.X project, and Exascale Framework for Digital Twins of the Human Body. [1] Patrick R. Amestoy, Alfredo Buttari, Jean-Yves L’Excellent, and Theo Mary. Performance and scalability of the block low-rank multifrontal factorization on multicore architectures. ACM Trans. Math. Softw., 45(1), February 2019. [2] Alfredo Buttari, Markus Huber, Philippe Leleux, Theo Mary, Ulrich Rüde, and Barbara Wohlmuth. Block low-rank single precision coarse grid solvers for extreme scale multigrid methods. Numerical Linear Algebra with Applications, 29(1):e2407, 2022. [3] Pasqua D’Ambra, Fabio Durastante, and Salvatore Filippone. Amg preconditioners for linear solvers towards extreme scale. SIAM Journal on Scientific Computing, 43(5):S679–S703, 2021. [4] Pasqua D’Ambra, Fabio Durastante, and Salvatore Filippone. Parallel sparse computation toolkit. Software Impacts, 15:100463, 2023.