The Oslo method comprises a set of analysis techniques designed to extract nuclear level density and average γ-decay strength function from a set of excitation-energy tagged γ-ray spectra. Here we present a new software implementation of the entire Oslo method, called OMpy. We provide a summary of the theoretical basis and derive the essential equations used in the Oslo method. In addition to the functionality of the original analysis code, the new implementation includes novel components such as a rigorous method to propagate uncertainties throughout all steps of the Oslo method using a Monte Carlo approach. The resulting level density and γ-ray strength function have to be normalized to auxiliary data. The normalization is performed simultaneously for both quantities, thus preserving all correlations. The software is verified by the analysis of a synthetic spectrum and compared to the results of the previous implementation, the oslo-method-software. Program Title:OMpy (Midtbø et al., 2020) CPC Library link to program files:https://doi.org/10.17632/jbthtbm9bd.1 Code Ocean Capsule:https://doi.org/10.24433/CO.6094094.v1 Licensing provisions: GPLv3 Programming language: Python, Cython Nature of problem: Extraction of the nuclear level density and average γ-ray strength function from a set of excitation-energy tagged γ-ray spectra including the quantification of uncertainties and correlations of the results. Solution method: The level density and γ-ray strength function can be obtained simultaneously using a set of analysis techniques called the Oslo method. To propagate the uncertainty from the counting statistics, we analyze an ensemble of perturbed spectra, which are created based on the experimental input. One obtains a set of level densities and γ-ray strength functions for each realization from a fit process. The fitting metric (χ2) is degenerate, but the degeneracy is removed by a simultaneous normalization of the level density and γ-ray strength function to external data, such that all correlations are preserved. There have been several modifications to facilitate a modular program flow and to enhance accuracy, reproducibility and transparency of the results. The main revisions in OMpy are that it (i) uses an ensemble based uncertainty quantification throughout whole method, (ii) the fitting is based on well-tested external libraries, (iii) corrections for the normalization procedure have been introduced, (iv) the code base is auto-documented with Sphinx and automatically tested.