Multi-agent LLM framework automates methane monitoring with 92% accuracy

A team of researchers led by Yang Yan developed a locally deployable, tool-grounded LLM multi-agent framework to streamline methane field monitoring. The system uses LLM agents as workflow coordinators that connect field measurements, meteorological data, deterministic sensor-processing routines, Gaussian plume inversion, and report generation—without directly estimating concentrations itself. Deployed across wastewater treatment plants, landfills, and oil and gas sites, the framework achieved 92.0% accuracy in workflow routing and parameter extraction, 85.0% success in emission-rate estimation and plume prediction, and 95.0% success in generating editable reports.

Compared with manual and general-purpose LLM workflows, the framework reduced processing time from hours-level to minutes-level, minimized manual coordination and prompt engineering, and preserved traceable plume-based outputs. Crucially, most processing runs locally, which limits exposure of sensitive facility and field data to cloud services. These results suggest tool-grounded LLM coordination can meaningfully lower the time, labor, usability, and data-security barriers in methane monitoring, offering a practical path for faster environmental compliance and industrial emission management.