Microseismic monitoring is a valuable geophysical tool, providing vital insights into subsurface dynamics through passive measurements. Its main objective is to detect, locate, and characterize weak seismic events induced by subsurface processes or anthropogenic activities. Processing microseismic data is challenging and involves multiple steps from acquisition to interpretation. While AI is gaining popularity for microseismic monitoring, it is essential to establish a solid benchmark to validate such new methods against. Many researchers and students, however, lack access to conventional microseismic monitoring techniques or struggle with their implementation due to the complexity and required coding expertise. We present FraCSPy (Framework for Conventional microSeismic Processing), an open-source Python package providing a comprehensive, user-friendly toolbox for the full conventional microseismic processing pipeline. FraCSPy makes established techniques accessible to a broader audience, including students, researchers, and educators, by offering an intuitive interface for modeling, processing, and visualization of microseismic data. This allows users to focus on interpreting results rather than technical challenges. The toolbox includes modules for detection, event localization, and source mechanism determination, seamlessly integrating with existing open-source libraries. For event localization, we implemented five different conventional methods: three extend the Kirchhoff operator implementation of PyLops — adjoint migration, least-squares inversion, and fast iterative shrinkage-thresholding algorithm (FISTA) inversion. The other two — cross-correlation imaging and diffraction stacking — use PyTorch and NumPy. Besides providing efficient implementations of the core algorithms, FraCSPy features detailed web-based documentation and extensive tutorials, making it a useful educational resource. It is already being used in ongoing research and education, aimed at the development of AI-driven microseismic techniques. As a flexible, open-source framework, FraCSPy not only serves as a benchmark for conventional microseismic processing techniques but also encourages collaboration and innovation in the field of microseismic monitoring.