Kosmulator: A Python framework for cosmological inference with MCMC

We present Kosmulator, a modular and vectorised Python framework designed to accelerate the statistical testing of cosmological models. As the theoretical landscape expands beyond standard $Λ$CDM, implementing new expansion histories into traditional Einstein--Boltzmann solvers becomes a significant computational bottleneck. Kosmulator addresses this by leveraging array-native execution and efficient ensemble slice sampling (via Zeus) to perform rapid Bayesian inference. We validate the framework against the industry-standard Cobaya code using a combination of Type Ia Supernovae, Cosmic Chronometers, and Baryon Acoustic Oscillation (BAO) data. Our results demonstrate that Kosmulator reproduces Cobaya's posterior constraints to within $\leq0.3σ$ statistical agreement on $H_{0}$ and $Ω_{m}$ and $