The characterisation of the HIV-1 reservoir, which consists of replication-competent integrated proviruses that persist on antiretroviral therapy (ART), is made difficult by the rarity of intact proviruses relative to those that are defective. While the only conclusive test for the replication-competence of HIV-1 proviruses is carried out in cell culture, genetic characterization of genomes by near full-length (NFL) PCR and sequencing can be used to determine whether particular proviruses have insertions, deletions, or substitutions that render them defective. Proviruses that are not excluded by having such defects can be classified as genetically intact and, possibly, replication competent. Identifying and quantifying proviruses that are potentially replication-competent is important for the development of strategies towards a functional cure. However, to date, there are no programs that can be incorporated into deep-sequencing pipelines for the automated characterization and annotation of HIV genomes. Existing programs that perform this work require manual intervention, cannot be widely installed, and do not have easily adjustable settings. Here, we present HIVIntact, a python-based software tool that characterises genomic defects in NFL HIV-1 sequences, allowing putative intact genomes to be identified in-silico. Unlike other applications that assess the genetic intactness of HIV genomes, this tool can be incorporated into existing sequence-analysis pipelines and applied to large next-generation sequencing datasets.