The Joint Effort for Data assimilation Integration (JEDI) is an international collaboration aimed at developing an open software ecosystem for model agnostic data assimilation. This paper considers implementation of the model-agnostic family of the local volume solvers in the JEDI framework. The implemented solvers include the Local Ensemble Transform Kalman Filter (LETKF), the Gain form Ensemble Transform Kalman Filter (GETKF), and the optimal interpolation variant of the LETKF filter (LETKF-OI). This paper documents the implementation choices and strategies that allow model agnostic implementation. We also document an expansive set of localization approaches that includes generic distance-based localization, localization based on modulated ensemble products, but also localizations specific to ocean (based on the Rossby radius of deformation), and land (based on the terrain difference between observation and model grid point). Finally, we apply the developed solvers in a limited set of experiments, including single-observation experiments in atmosphere and ocean, and cycling experiments for the ocean, land, and aerosol assimilation. We also provide a proof of concept that illustrates how JEDI Ensemble Kalman Filter solvers can be used in a strongly coupled framework providing increments to the ocean based on the combined observations from the ocean and the atmosphere.