Unmanned Aerial Vehicles (UAVs) have become paramount for Search and Rescue (SAR) missions, due to their ability to access hazardous and challenging environments, thereby reducing response times significantly by providing cost-effective aerial perspectives for situational awareness. Nevertheless, current UAV systems are designed and optimised for specific tasks, often focusing on benchmarking use cases. Therefore, they bear limited possibility to adapt to diverse decision-making capabilities required for SAR missions. Furthermore, commercially available integrated UAV systems are nonopen source, preventing further extension with state-of-the-art decision-making algorithms. This paper introduces AUSPEX, a holistic modular and open-source framework, tailored specifically for enhancing decision-making capabilities of UAV-systems. AUSPEX integrates diverse capabilities to store and update knowledge, to sense the environment, to compute plans with state-of-the-art decisionmaking algorithms, and to execute the plans. Additionally, AUSPEX considers the heterogeneity of available UAV platforms, and offers the possibility of including off-the-shelf UAVs, as well as generic UAVs with an open architecture into the AUSPEX ecosystem. The framework relies only on open-source components, to ensure transparency, as well as system scalability and extensibility. We demonstrate the possibility of connecting AUSPEX to the Unreal Engine based simulation framework REAP for software-in-the-loop validation, as well as a platform-independent graphical user interface written in Flutter (AUGUR). We demonstrate how AUSPEX can be used for generic scenarios in SAR missions, while highlighting its potential for future extensibility.