PyOED is a highly extensible scientific package that enables developing and testing model-constrained optimal experimental design (OED) for inverse problems. Specifically, PyOED aims to be a comprehensive Python toolkit for model-constrained OED. The package targets scientists and researchers interested in understanding the details of OED formulations and approaches. It is also meant to enable researchers to experiment with standard and innovative OED technologies with a wide range of test problems (e.g., simulation models). Thus, PyOED is continuously being expanded with a plethora of Bayesian inversion, data assimilation (DA), and OED methods as well as new scientific simulation models, observation error models, priors, and observation operators. These pieces are added such that they can be permuted to enable testing OED methods in various settings of varying complexities. Although the tradeoff for extensibility is scalability, this is precisely PyOED’s mission: to enable rapid development and benchmarking of OED methods with minimal coding effort and to maximize code re-utilization.