Abstarct: Inference in state-space models usually relies on recursive forms for filtering and smoothing of the state vectors regarding the temporal structure of the observations, an assumption that is, from our view point, unnecessary if the data set is fixed, that is, completely available before analysis. In this paper we propose a computational frxamework to perform approximate full Bayesian inference in linear and generalized dynamic linear models baxsed on the Integrated Nested Laplace Approximation (INLA) approach. The proposed frxamework directly approximates the posterior marginals of interest disregarding the assumption of recursive updating/estimation of the states and hyperparameters in the case of fixed data sets and, therefore, enable us to do fully Bayesian analysis of complex state-space models more easily and in a short computational time. The proposed frxamework overcomes some limitations of current tools in the dynamic modeling literature and is vastly illustrated with a series of simulated as well as worked real-life examples, including realistically complex models with correlated error structures and models with more than one state vector, being mutually dependent on each other.