We present a modular Python-based framework for the automated control of nitrogen-vacancy (NV) centers using the OPX+ controller. While OPX+ enables real-time pulse generation and adaptive feedback, its configuration remains complex, requiring manual setup. Our framework simplifies this process by automatically generating configuration files and dynamically assembling experimental parameters, pulse sequences, and hardware definitions. It supports real-time adaptation, reduces human error, and accelerates multi-NV experiments, laying the groundwork for integrating advanced optimization techniques and machine-learning-driven calibration routines in future work. Validation through experiments at the University of Leipzig confirms its effectiveness in quantum control tasks such as Rabi oscillations and ODMR, with significantly reduced setup time and maintained precision. The framework is scalable, extensible, and suitable for both research and education. It lays the foundation for future integration of optimization algorithms and hybrid quantum-classical systems.