Despite the growing popularity of Neural Posterior Estimation (NPE) methods in astronomy, the adaptation of such technique into routine data analysis has been slow. We identify three critical issues: the steep learning curve of NPE for domain scientists, the inference inexactness, and the under-specification of physical forward models. To address the first two issues, we introduce a new framework and open-source software \textit{nbi}: Neural Bayesian Inference, which implements both amortized and sequential NPE.First, \textit{nbi} provides built-in ``featurizer'' networks with demonstrated efficacy on sequential data, such as light curve and spectra, thus eliminating the need for customization on the user end. Second, we introduce a modified algorithm SNPE-IS, which facilities asymptotically exact inference by using the surrogate posterior only as a proposal distribution for importance sampling.These features allow \textit{nbi} to be applied off-the-shelf to astronomical inference problems involving light curves and spectra, which would otherwise be tackled with MCMC and Nested Sampling. Our package\footnote{Anonymized for review.} is at \url{https://github.com/nbi-review/nbi}. An application paper is concurrently submitted to this workshop and included in the appendix for reviewing purposes.