We present RxInfer.jl, which is a Julia (Jeff Bezanson et al., 2012; J. Bezanson et al., 2017) package for real-time variational Bayesian inference based on reactive message passing in a factor graph representation of the model under study (Bagaev & Vries, 2021). RxInfer.jl provides access to a powerful model specification language that translates a textual description of a probabilistic model into a corresponding factor graph representation. In addition, RxInfer.jl supports hybrid variational inference processes, where different Bayesian inference methods can be combined in different parts of the model, resulting in a straightforward mechanism to trade off accuracy for computational speed. The underlying implementation relies on a reactive programming paradigm and supports by design the processing of infinite asynchronous data streams. In the proposed framework, the inference engine reacts to new data and automatically updates relevant posteriors.