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Abstract. We present SeidarT, an open-source, community-driven software package for full-waveform finite-difference time-domain (FDTD) modeling of elastic and electromagnetic wave propagation in heterogeneous, anisotropic, and attenuating media. SeidarT natively incorporates full-tensor anisotropy (all 21 stiffness coefficients), frequency-independent attenuation through a generalized Q formulation, and unified treatment of both seismic and electromagnetic wave physics on a simple Cartesian grid. The software prioritizes accessibility and extensibility by combining the computational efficiency of FORTRAN with the user-friendly scripting capabilities of Python. Model construction leverages an intuitive image-based geometry workflow and JSON project files, eliminating the need for complex mesh generation while allowing flexible specification of arbitrary stiffness or permittivity tensors, material distributions, and boundary conditions. We implement the Convolutional Perfectly Matched Layer (CPML) with explicit tuning strategies adapted for anisotropic media, and provide automatic stability checking via the Courant-Friedrichs-Lévy and wavenumber-bandlimit criteria. The software is validated against analytical solutions for elastic wave propagation and empirically constrained through comparison with ground-penetrating radar and seismic field observations in snow and ice. We document the physical property parameterizations for ice and snow as functions of temperature, pressure, and liquid water content, and provide multiple material homogenization schemes (Hill average, Gassmann substitution, Self-Consistent Approximation) to accommodate variable porosity and fluid saturation regimes. SeidarT is designed to lower economic and technical barriers for scientists, engineers, and students integrating sophisticated wave-physics simulations into workflows spanning cryospheric research, environmental monitoring, subsurface characterization, and civil infrastructure assessment. The open-source development model on GitHub and PyPI encourages community contributions and iterative improvements, positioning SeidarT as a versatile platform for advancing both fundamental understanding and applied geophysical imaging.