CHORUS: A Generalist Multi-Agent System for Complex Spatial Biology Analysis

Large language model agents increasingly automate biological data analysis through workflow planning and tool use, yet existing systems are often either flexible but weakly constrained or reproducible but experience-blind. This limitation is particularly consequential in spatial biology, where datasets differ substantially in measurement platform, spatial resolution, available modalities, image registration, and preprocessing state, such that the same analytical workflow may be valid for one dataset but inappropriate for another. We introduce START, a State-Aware Tool Reasoning and Adaptive Trajectory Agent for memory-augmented spatial omics analysis. START represents prior executions as structured state–workflow–outcome experiences and retrieves relevant positive and negative evidence to rank candidate workflows. Scientific state constrains which trajectories are admissible, while experience memory guides preference among valid alternatives. Execution outcomes are evaluated and written back to memory, allowing future trajectories to adapt without updating the underlying language model. Experiments across multimodal and cross-platform spatial-omics settings demonstrate reliable and adaptive biological analysis.