HADES is an open-source software suite developed by Observational Health Data Sciences and Informatics (OHDSI) for observational research. It operates on the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and enables analysis of healthcare data such as electronic health records and administrative claims. HADES supports characterization, causal effect estimation, and patient-level prediction, and can perform federated analyses across multiple data sources while keeping patient data local. It is designed to run on various technical environments and includes continuous integration testing to ensure reliability. HADES follows OHDSI best practices and is used in numerous studies, some of which have influenced regulatory decisions. HADES is built using R packages and leverages C++, Java, and Python for advanced analytics. It includes tools for data quality assessment, patient-level prediction, causal effect estimation, and evidence synthesis. The software is used in over 38 clinical research papers and 29 methods research papers. Notable applications include studies on antihypertensive drugs, COVID-19 risk calculators, and hydroxychloroquine safety. HADES is also used for vaccine safety surveillance and has been endorsed by the European Medicines Agency as best practice. HADES is an R package suite that leverages the OMOP CDM for analyzing healthcare data. It transforms CDM data into diagnostics, statistics, and visuals, shaping clinical decisions. Researchers worldwide have utilized HADES in impactful studies, with open-source code for reproducibility. HADES' liberal Apache v2.0 license fosters flexibility for collaboration, modification, and sharing. Designed for federated networks, HADES prioritizes privacy by localizing data and sharing analytics. HADES is developed and maintained by OHDSI, evolving to enhance efficiency, broaden epidemiological designs, and offer an interactive interface for easier utilization. Access HADES at: https://ohdsi.github.io/Hades/.HADES is an open-source software suite developed by Observational Health Data Sciences and Informatics (OHDSI) for observational research. It operates on the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and enables analysis of healthcare data such as electronic health records and administrative claims. HADES supports characterization, causal effect estimation, and patient-level prediction, and can perform federated analyses across multiple data sources while keeping patient data local. It is designed to run on various technical environments and includes continuous integration testing to ensure reliability. HADES follows OHDSI best practices and is used in numerous studies, some of which have influenced regulatory decisions. HADES is built using R packages and leverages C++, Java, and Python for advanced analytics. It includes tools for data quality assessment, patient-level prediction, causal effect estimation, and evidence synthesis. The software is used in over 38 clinical research papers and 29 methods research papers. Notable applications include studies on antihypertensive drugs, COVID-19 risk calculators, and hydroxychloroquine safety. HADES is also used for vaccine safety surveillance and has been endorsed by the European Medicines Agency as best practice. HADES is an R package suite that leverages the OMOP CDM for analyzing healthcare data. It transforms CDM data into diagnostics, statistics, and visuals, shaping clinical decisions. Researchers worldwide have utilized HADES in impactful studies, with open-source code for reproducibility. HADES' liberal Apache v2.0 license fosters flexibility for collaboration, modification, and sharing. Designed for federated networks, HADES prioritizes privacy by localizing data and sharing analytics. HADES is developed and maintained by OHDSI, evolving to enhance efficiency, broaden epidemiological designs, and offer an interactive interface for easier utilization. Access HADES at: https://ohdsi.github.io/Hades/.