In this study, we propose a novel hierarchical graph autoregressive (HGAR) model, which jointly leverages atom-level and motiflevel information for molecular retrosynthesis. As illustrated in Figure , our model applies a semi-template strategy to predict reactants. Given a product, we first construct a hierarchical graph by connecting its atom-level molecular graph to its motif-level junction tree . Taking the hierarchical graph as input, our HGAR model applies a variational encoder to derive a graph-level embedding and leverages coupled Graphormers to derive atom-level and motif-level embeddings, respectively. Based on the embeddings, our HGAR model i) identifies reaction centers and decomposes the product into synthons, ii) predicts which motif should be added to each synthon and what kind of bond types should be applied, and iii) check whether each synthon has been completed. Our HGAR model predicts reactants by repeating the above steps autoregressively.