Recent observations of the galactic centers of M87 and the Milky Way with the
Event Horizon Telescope have ushered in a new era of black hole based tests of
fundamental physics using very long baseline interferometry (VLBI). Being a
nascent field, there are several different modeling and analysis approaches in
vogue (e.g., geometric and physical models, visibility and closure amplitudes,
agnostic and multimessenger priors). We present \texttt{GALLIFRAY}, an
open-source Python-based framework for estimation/extraction of parameters
using VLBI data. It is developed with modularity, efficiency, and adaptability
as the primary objectives. This article outlines the design and usage of
\texttt{GALLIFRAY}. As an illustration, we fit a geometric and a physical model
to simulated datasets using markov chain monte carlo sampling and find good
convergence of the posterior distribution. We conclude with an outline of
further enhancements currently in development.Comment: 10 pages, 5 figures; accepted for publication in Ap