Computational flow and transport simulations have become critical in many research applications, such as biomedical, aerospace, and environmental engineering. Several high and low-level tools have emerged to streamline simulation development. Commercially available high-level tools (e.g., Ansys Fluent) are powerful and easy to use but are often rigid and do not allow for multiphysics coupling or customized problem definitions. Conversely, low-level tools (e.g., FEniCS) offer greater flexibility at the cost of increased complexity [1, 4, 5]. Developing coupled multiphysics models in low-level packages requires custom weak forms, resulting in long and complex codes. Even minor changes to the problem definition, such as altering boundary conditions, can be difficult or non-intuitive to implement. We present a new, open-source, mid-level wrapper for FEniCSx, called FLATiron Toolkit (FLAT-FE: Flow And Transport using Finite Elements), designed to overcome these limitations [3]. FLATiron’s hierarchical, modular structure expedites the workflow when building coupled physics simulations and offers built-in support for transport-reaction, Stokes, and Navier-Stokes problems. In addition to these physics problems, we have expanded the capabilities of our toolkit to include non-Newtonian dynamics and massless tracer tracking. We demonstrate the efficacy of our approach with included demonstration scripts that model the coupling of these physics in flow-reaction simulations. In addition to the demos available on our GitHub page, we provide extensive documentation to aid users in developing both staggered and monolithic coupled multiphysics models with customized boundary conditions and in generating domain-specific computational endpoints [2]. Our modular structure, combined with UFL’s symbolic mathematics, positions FLATiron as an accessible tool for training environments and for users unfamiliar with computational platforms. Currently, FLATiron is being used as a learning aid in CFD classes at the University of Colorado. Our framework will additionally support the broader modeling community by offering a generalized computational platform for a wide range of disciplines.