<p>The rapid growth of electric vehicle adoption presents new challenges for distribution system operators and charging station owners. Distribution system operators must correctly manage charging demand, optimize infrastructure usage, and support grid stability, while charging station owners seek to analyze user behavior and predict future demand to improve operational efficiency. Meeting these goals requires accurate analysis and forecasting of charging behaviors. A major bottleneck, however, lies in the heterogeneity of available electric vehicle charging datasets: each dataset comes with its own structure, quality issues, and missing information, requiring time-consuming and error-prone preprocessing before any analysis or forecasting can be performed. To overcome this limitation, we introduce EV-Insights, an open-source framework designed to provide standardized services for data ingestion, preprocessing, analysis, and forecasting by supporting the integration of real-time data, synthetic data, and public datasets. Once data is integrated, users can easily generate insights on charging behavior and extend the framework with new analyses or forecasting models through modular interfaces. We evaluated EV-Insights using seven real-world public datasets comprising over 3 million charging sessions, demonstrating its potential to uncover valuable insights and support informed decision-making. Ev-Insights is available as open source at <a href="https://github.com/EV-Insights">https://github.com/EV-Insights</a></p>