  We present here RadioSED, a Bayesian inference framework tailored to
modelling and classifying broadband radio spectral energy distributions (SEDs)
using only data from publicly-released, large-area surveys. We outline the
functionality of RadioSED, with its focus on broadband radio emissions which
can trace kiloparsec-scale absorption within both the radio jets and the
circumgalactic medium of Active Galactic Nuclei (AGN). In particular, we
discuss the capability of RadioSED to advance our understanding of AGN physics
and composition within youngest and most compact sources, for which high
resolution imaging is often unavailable. These young radio AGN typically
manifest as peaked spectrum (PS) sources which, before RadioSED, were difficult
to identify owing to the large, broadband frequency coverage typically
required, and yet they provide an invaluable environment for understanding AGN
evolution and feedback. We discuss the implementation details of RadioSED, and
we validate our approach against both synthetic and observational data. Since
the surveys used are drawn from multiple epochs of observation, we also
consider the output from RadioSED in the context of AGN variability. Finally,
we show that RadioSED recovers the expected SED shapes for a selection of
well-characterised radio sources from the literature, and we discuss avenues
for further study of these and other sources using radio SED fitting as a
starting point. The scalability and modularity of this framework make it an
exciting tool for multiwavelength astronomers as next-generation telescopes
begin several all-sky surveys. Accordingly, we make the code for RadioSED,
which is written in Python, available on Github.
