COBRApy is a Python-based software package for constraint-based reconstruction and analysis (COBRA) of metabolic networks. It provides support for basic COBRA methods and is designed in an object-oriented manner to represent complex biological processes. COBRApy does not require MATLAB and includes an interface to the COBRA Toolbox for MATLAB to facilitate use of legacy codes. It supports parallel processing for computationally intensive tasks. The COBRA Toolbox for MATLAB is a leading software package for genome-scale analysis of metabolism, but it lacks integration with multiomics data and complex biological networks. COBRApy addresses these limitations by offering an object-oriented framework that allows integration with databases and high-throughput data. It includes modules for input/output, flux analysis, topology, testing, solvers, and MATLAB integration. COBRApy's core classes include Model, Metabolite, Reaction, and Gene, which represent organisms, biochemical reactions, and biomolecules. The package supports various functionalities such as flux balance analysis, flux variability analysis, and gene deletion studies. It also provides access to commonly used COBRA methods and allows integration with the COBRA Toolbox for MATLAB through the mlabwrap module. COBRApy is designed to accommodate the increasing complexity of biological processes represented with COBRA methods. It supports a variety of metabolic network models and can read SBML-formatted models from various sources. The software is available at http://opencobra.sourceforge.net and is licensed under the GNU GPL version 3 or later. COBRApy is part of the openCOBRA Project, which promotes constraints-based research through freely available software. It serves as an enabling framework for the community to develop and contribute application-specific modules. The software is compatible with Python and Jython and requires specific libraries for SBML and linear programming solvers. It is supported on multiple operating systems and is suitable for both research and educational purposes.COBRApy is a Python-based software package for constraint-based reconstruction and analysis (COBRA) of metabolic networks. It provides support for basic COBRA methods and is designed in an object-oriented manner to represent complex biological processes. COBRApy does not require MATLAB and includes an interface to the COBRA Toolbox for MATLAB to facilitate use of legacy codes. It supports parallel processing for computationally intensive tasks. The COBRA Toolbox for MATLAB is a leading software package for genome-scale analysis of metabolism, but it lacks integration with multiomics data and complex biological networks. COBRApy addresses these limitations by offering an object-oriented framework that allows integration with databases and high-throughput data. It includes modules for input/output, flux analysis, topology, testing, solvers, and MATLAB integration. COBRApy's core classes include Model, Metabolite, Reaction, and Gene, which represent organisms, biochemical reactions, and biomolecules. The package supports various functionalities such as flux balance analysis, flux variability analysis, and gene deletion studies. It also provides access to commonly used COBRA methods and allows integration with the COBRA Toolbox for MATLAB through the mlabwrap module. COBRApy is designed to accommodate the increasing complexity of biological processes represented with COBRA methods. It supports a variety of metabolic network models and can read SBML-formatted models from various sources. The software is available at http://opencobra.sourceforge.net and is licensed under the GNU GPL version 3 or later. COBRApy is part of the openCOBRA Project, which promotes constraints-based research through freely available software. It serves as an enabling framework for the community to develop and contribute application-specific modules. The software is compatible with Python and Jython and requires specific libraries for SBML and linear programming solvers. It is supported on multiple operating systems and is suitable for both research and educational purposes.