PyRTlib is a new standalone Python package designed for non-scattering line-by-line microwave radiative transfer simulations. It is a flexible and user-friendly tool that can compute down- and upwelling brightness temperatures and related quantities, such as atmospheric absorption, optical depth, opacity, and mean radiating temperature. PyRTlib supports simulations from ground-based, airborne, and satellite microwave sensors in both clear-sky and cloudy conditions, using the non-scattering Rayleigh approximation. The package aims to serve as an educational tool, providing an accessible way to simulate atmospheric microwave radiative transfer from various input profiles, including predefined climatologies, global radiosonde archives, and model reanalysis. Key features include easy access to popular open data archives, the ability to modify parameters such as observing angle, surface emissivity, and gas absorption models, and the capability to embed in other Python codes for atmospheric microwave radiative transfer. PyRTlib also offers uncertainty propagation for spectroscopic parameters, a feature not provided by other microwave radiative transfer codes. The paper provides examples demonstrating the use of PyRTlib for simulating brightness temperatures for different platforms and input profiles, and highlights its potential for producing scientific results.PyRTlib is a new standalone Python package designed for non-scattering line-by-line microwave radiative transfer simulations. It is a flexible and user-friendly tool that can compute down- and upwelling brightness temperatures and related quantities, such as atmospheric absorption, optical depth, opacity, and mean radiating temperature. PyRTlib supports simulations from ground-based, airborne, and satellite microwave sensors in both clear-sky and cloudy conditions, using the non-scattering Rayleigh approximation. The package aims to serve as an educational tool, providing an accessible way to simulate atmospheric microwave radiative transfer from various input profiles, including predefined climatologies, global radiosonde archives, and model reanalysis. Key features include easy access to popular open data archives, the ability to modify parameters such as observing angle, surface emissivity, and gas absorption models, and the capability to embed in other Python codes for atmospheric microwave radiative transfer. PyRTlib also offers uncertainty propagation for spectroscopic parameters, a feature not provided by other microwave radiative transfer codes. The paper provides examples demonstrating the use of PyRTlib for simulating brightness temperatures for different platforms and input profiles, and highlights its potential for producing scientific results.