Despite the promise of Neural Posterior Estimation (NPE) methods in astronomy, the adaptation of NPE into the routine inference workflow has been slow. We identify three critical issues: the need for custom featurizer networks tailored to the observed data, 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} (\textit{Neural Bayesian Inference}), which supports 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 obviating the need for this customization on the user end. Second, we introduce a modified algorithm SNPE-IS, which facilities asymptotically exact inference by using the surrogate posterior under NPE 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. We discuss how \textit{nbi} may serve as an effective alternative to existing methods such as Nested Sampling. Our package is at [url redacted].