Knowledge Graphs are an extraordinary source of data due to their vastness, the topics heterogeneity and the presence of sources curated by companies, research groups, volunteers, and dedicated communities. Identifying high-quality Knowledge Graphs requires supporting developers and end-users in comparing and assessing data quality of publicly available Knowledge Graphs. However, no fully working and maintained Knowledge Graph quality assessment tool was found during the review of related research. This article fully describes KGHeartBeat, a community shared open-source knowledge graph quality assessment tool designed to periodically perform quality analysis on a wide range of freely available knowledge graphs registered on the LOD Cloud and DataHub. Users can either visually explore the quality assessment report and compare knowledge graphs via a freely available web-based interface or download data analysis results for further analysis. Moreover, KGHeartBeat is also released as APIs so developers can easily integrate them into any quality management tool. As a proof of concept, we discuss different use cases to show KGHeartBeat in practice, demonstrating how it can be used to compare multiple Knowledge Graphs, assess quality dimensions over time, and report performance analysis in terms of execution time. Resource type       Community Shared Software Framework License                 MIT Web-app                 http://www.isislab.it:12280/kgheartbeat Permanent URL     https://zenodo.org/records/10990547 Pypi package           https://pypi.org/project/kgheartbeat