Prophet is a GAM (Generalised Additive Model). It decomposes time series in additive components: trend, seasonality, holidays and noise. Most interesting time-series are not so simply decomposable. Making Prophet Bayesian and producing probabilistic forecast by MCMC sampling from trend/seasonality/holiday posteriors still keeps its GAM structure. Might be for a simple exploratory analysis Prophet is a good go-to tool but all the research action is now in Deep Learning Forecasting Models.

Also, IMHO, Prophet deals with individual TS and teaching it to produce vector forecast for multiple TSes at the same time is tricky (or not even possible).