Evolving Fuzzy Systems (eFS) have gained significant attention due to their ability to adaptively update their structure in response to data dynamics while maintaining interpretability. However, the lack of publicly available implementations of these models limits their accessibility and widespread adoption. To address this gap, we present evolvingfuzzysystems, a Python library that provides implementations of several well-established eFS models, including ePL-KRLS-DISCO, ePL+, eMG, ePL, exTS, Simpl\_eTS, and eTS. The library facilitates model evaluation and comparison by offering built-in tools for training, visualization, and performance assessment. The models are evaluated using the fetch\_california\_housing dataset, with performance measured in terms of normalized root-mean-square error (NRMSE), non-dimensional error index (NDEI), and mean absolute percentage error (MAPE). Additionally, computational complexity is analyzed by measuring execution times and rule evolution during training and testing phases. The results highlight ePL as a simple yet efficient model that balances accuracy and computational cost, making it particularly suitable for real-world applications. By making these models publicly available, evolvingfuzzysystems aims to foster research and practical applications in adaptive and interpretable machine learning.