El Agente Seguro: An Agent for Chemical Safety

Laboratory safety is becoming increasingly complex as chemical and materials research integrates automation, artificial intelligence, and data-driven experimentation. However, existing safety management approaches rely heavily on manual interpretation or on unconstrained language-model reasoning, which can lead to inconsistent and unreliable outcomes. In this work, we present El Agente Seguro, a graph-based multi-agent framework for safety planning and evaluation in chemical and materials laboratories. The system separates coordination and execution through a central safety director and a dependency-aware safety-solving graph, in which specialized subagents perform structured tasks with schema-constrained data exchange. This design enables controlled and data-driven safety reasoning across multiple layers, including chemical hazards, reaction risks, regulatory constraints, and opera-tional considerations. Benchmark evaluation across seven representative safety tasks demonstrates that El Agente Seguro achieves consistently strong performance relative to frontier general-purpose language models, while also providing more comprehensive and practically useful safety reasoning according to ranking-based evaluations. Beyond benchmark assessment, El Agente Seguro further demonstrates real-world safety capabilities including protocol-level hazard analysis, autonomous procedure modification, dual-use and prohibited chemical enforcement, multimodal SOP generation from laboratory images and experimental procedures, and computationally guided reaction safety prediction through integration with external scientific agents. These results show that structured, dependency-aware multi-agent workflows can improve context-sensitive safety reasoning beyond unconstrained language-model inference alone. Overall, El Agente Seguro establishes a scalable, extensible and practically deployable framework for intelligent laboratory safety into both human-operated and autonomous research environments.