RadioSED uses nested sampling to perform a Bayesian analysis of radio SEDs constructed from radio flux density measurements obtained as part of large area surveys (or in some limited cases, as part of targeted followup campaigns). It is a pure Python implementation, and is essentially a wrapper around Bilby (ascl:1901.011), the Bayesian inference library. RadioSED uses dynesty (ascl:1809.013) to perform the sampling steps, though other samplers could also be used. Users can make use of a pre-defined set of models and surveys from which to draw flux density measurements, or they can define their own models and provide their own input flux density measurements. All flux density measurements are referenced against the RACS-LOW survey, and source names and IDs from the survey catalogue are used as identifiers.