PyRTlib is a new standalone Python package for non-scattering line-by-line microwave radiative transfer simulations. It is a flexible and user-friendly tool for computing down- and upwelling brightness temperatures and related quantities, written in Python, a language widely used in scientific software development. PyRTlib allows for simulating observations from ground-based, airborne, and satellite microwave sensors in clear-sky and cloudy conditions under the non-scattering Rayleigh approximation. The intention of PyRTlib is not to compete with state-of-the-art atmospheric radiative transfer codes but to provide an educational tool for simulating atmospheric microwave radiative transfer from various input profiles, including predefined climatologies, global radiosonde archives, and model reanalysis. The paper presents examples for the built-in modules to access popular open data archives and demonstrates how to simulate brightness temperatures for different platforms using various input profiles. PyRTlib can be easily embedded in other Python codes needing atmospheric microwave radiative transfer. Despite its simplicity, PyRTlib can produce present-day scientific results, as demonstrated by two examples showing an absorption model comparison and validation with ground-based radiometric observations and uncertainty propagation of spectroscopic parameters through radiative transfer calculations. PyRTlib is unique in providing uncertainty estimates for microwave radiative transfer calculations, which are not available in other codes. The paper introduces PyRTlib version 1.0 and advocates its use through examples demonstrating its value in producing passive microwave simulations from notable input datasets. The paper is structured as follows: a brief introduction on the basics of radiative transfer models, the main absorption model available, and how profiles can be interpolated and extrapolated is provided in Sect. 2. The tools for retrieving and managing input data from open-access repositories are discussed in Sect. 3. Usage of the code, as well as some implementation details and a few examples of applications, is presented in Sect. 4. Section 5 summarizes the conclusions and future developments, while Sect. 6 provides instructions for code availability and usage.PyRTlib is a new standalone Python package for non-scattering line-by-line microwave radiative transfer simulations. It is a flexible and user-friendly tool for computing down- and upwelling brightness temperatures and related quantities, written in Python, a language widely used in scientific software development. PyRTlib allows for simulating observations from ground-based, airborne, and satellite microwave sensors in clear-sky and cloudy conditions under the non-scattering Rayleigh approximation. The intention of PyRTlib is not to compete with state-of-the-art atmospheric radiative transfer codes but to provide an educational tool for simulating atmospheric microwave radiative transfer from various input profiles, including predefined climatologies, global radiosonde archives, and model reanalysis. The paper presents examples for the built-in modules to access popular open data archives and demonstrates how to simulate brightness temperatures for different platforms using various input profiles. PyRTlib can be easily embedded in other Python codes needing atmospheric microwave radiative transfer. Despite its simplicity, PyRTlib can produce present-day scientific results, as demonstrated by two examples showing an absorption model comparison and validation with ground-based radiometric observations and uncertainty propagation of spectroscopic parameters through radiative transfer calculations. PyRTlib is unique in providing uncertainty estimates for microwave radiative transfer calculations, which are not available in other codes. The paper introduces PyRTlib version 1.0 and advocates its use through examples demonstrating its value in producing passive microwave simulations from notable input datasets. The paper is structured as follows: a brief introduction on the basics of radiative transfer models, the main absorption model available, and how profiles can be interpolated and extrapolated is provided in Sect. 2. The tools for retrieving and managing input data from open-access repositories are discussed in Sect. 3. Usage of the code, as well as some implementation details and a few examples of applications, is presented in Sect. 4. Section 5 summarizes the conclusions and future developments, while Sect. 6 provides instructions for code availability and usage.