Many real-world tasks can be modeled as constraint optimization problems. To ensure scalability and mapping to distributed scenarios, distributed constraint optimization problems (DCOPs) have been proposed, where each variable is locally controlled by its own agent. Most practical applications prefer approximate local iterative algorithms to reach a locally optimal and sufficiently good solution fast. The Iterative Approximate Best-Response Algorithms can be decomposed in three types of components and mixing different components allows to create hybrid algorithms. We implement a mix-and-match framework for these algorithms, using the graph processing framework SIGNAL/COLLECT, where each agent is modeled as a vertex and communication pathways are represented as edges. Choosing this abstraction allows us to exploit the generic graph-oriented distribution/optimization heuristics and makes our proposed framework configurable as well as extensible. It allows us to easily recombine the components, create and exhaustively evaluate possible hybrid algorithms.