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We present ${\tt bajes}$, a parallel and lightweight framework for Bayesianinference of multimessenger transients. ${\tt bajes}$ is a Python modularpackage with minimal dependencies on external libraries adaptable to themajority of the Bayesian models and to various sampling methods. We describethe general workflow and the parameter estimation pipeline forcompact-binary-coalescence gravitational-wave transients. The latter isvalidated against injections of binary black hole and binary neutron starwaveforms, including confidence interval tests that demonstrates the inferenceis well-calibrated. Binary neutron star postmerger injections are also studiedusing a network of five detectors made of LIGO, Virgo, KAGRA and EinsteinTelescope. Postmerger signals will be detectable for sources at${\lesssim}80\,$Mpc, with Einstein Telescope contributing over 90\% of thetotal signal-to-noise ratio. As a full scale application, we re-analyze theGWTC-1 black hole transients using the effective-one-body ${\tt TEOBResumS}$approximant, and reproduce selected results with other approximants. ${\ttbajes}$ inferences are consistent with previous results; the direct comparisonof ${\tt bajes}$ and ${\tt bilby}$ analyses of GW150914 shows a maximumJensen-Shannon divergence of $5.2{\times}10^{-4}$. GW170817 is re-analyzedusing ${\tt TaylorF2}$ with 5.5PN point-mass and 7.5PN tides, ${\ttTEOBResumSPA}$, and ${\tt IMRPhenomPv2\_NRTidal}$ with differentcutoff-frequencies of $1024\,$Hz and $2048\,$Hz. We find that the former choiceminimizes systematics on the reduced tidal parameter, while a larger amount oftidal information is gained with the latter choice. ${\tt bajes}$ can performthese analyses in about 1~day using 128 CPUs.

${\tt bajes}$: Bayesian inference of multimessenger astrophysical data, methods and application to gravitational-waves