  We present ${\tt bajes}$, a parallel and lightweight framework for Bayesian
inference of multimessenger transients. ${\tt bajes}$ is a Python modular
package with minimal dependencies on external libraries adaptable to the
majority of the Bayesian models and to various sampling methods. We describe
the general workflow and the parameter estimation pipeline for
compact-binary-coalescence gravitational-wave transients. The latter is
validated against injections of binary black hole and binary neutron star
waveforms, including confidence interval tests that demonstrates the inference
is well-calibrated. Binary neutron star postmerger injections are also studied
using a network of five detectors made of LIGO, Virgo, KAGRA and Einstein
Telescope. Postmerger signals will be detectable for sources at
${\lesssim}80\,$Mpc, with Einstein Telescope contributing over 90\% of the
total signal-to-noise ratio. As a full scale application, we re-analyze the
GWTC-1 black hole transients using the effective-one-body ${\tt TEOBResumS}$
approximant, and reproduce selected results with other approximants. ${\tt
bajes}$ inferences are consistent with previous results; the direct comparison
of ${\tt bajes}$ and ${\tt bilby}$ analyses of GW150914 shows a maximum
Jensen-Shannon divergence of $5.2{\times}10^{-4}$. GW170817 is re-analyzed
using ${\tt TaylorF2}$ with 5.5PN point-mass and 7.5PN tides, ${\tt
TEOBResumSPA}$, and ${\tt IMRPhenomPv2\_NRTidal}$ with different
cutoff-frequencies of $1024\,$Hz and $2048\,$Hz. We find that the former choice
minimizes systematics on the reduced tidal parameter, while a larger amount of
tidal information is gained with the latter choice. ${\tt bajes}$ can perform
these analyses in about 1~day using 128 CPUs.
