A lightweight Python package for setting up robustness experiments and to compute robustness distributions.

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A robust framework for formal verification of multi-agent systems powered by LLMs, ensuring safe and reliable decision-making. Integrates machine learning with formal methods to validate agent behaviors, safety constraints, and real-world simulations in critical domains like autonomous driving and robotics.