Constraint answer set programming or CASP, for short, is a hybrid approach in\nautomated reasoning putting together the advances of distinct research areas\nsuch as answer set programming, constraint processing, and satisfiability\nmodulo theories. Constraint answer set programming demonstrates promising\nresults, including the development of a multitude of solvers: acsolver,\nclingcon, ezcsp, idp, inca, dingo, mingo, aspmt, clingo[l,dl], and ezsmt. It\nopens new horizons for declarative programming applications such as solving\ncomplex train scheduling problems. Systems designed to find solutions to\nconstraint answer set programs can be grouped according to their construction\ninto, what we call, integrational or translational approaches. The focus of\nthis paper is an overview of the key ingredients of the design of constraint\nanswer set solvers drawing distinctions and parallels between integrational and\ntranslational approaches. The paper also provides a glimpse at the kind of\nprograms its users develop by utilizing a CASP encoding of Travelling Salesman\nproblem for illustration. In addition, we place the CASP technology on the map\namong its automated reasoning peers as well as discuss future possibilities for\nthe development of CASP.\n