We present muRelBench, a framework for synthetic benchmarks for weakly-relational abstract domains and their operations. This extensible microbenchmarking framework enables researchers to experimentally evaluate proposed algorithms for numerical abstract domains, such as closure, least-upper bound, and forget, enabling them to quickly prototype and validate performance improvements before considering more intensive experimentation. Additionally, the framework provides mechanisms for checking correctness properties for each of the benchmarks to ensure correctness within the synthetic benchmarks.