CucurbitAgent: An AI agent for exploring cucurbit multi-omics resources
Functional genomics in crops increasingly depends on integrating information from an extensive literature and heterogeneous multi-omics datasets. Large language models (LLMs) offer a powerful approach to synthesizing these resources and revealing hidden connections across datasets; however, they frequently hallucinate, producing plausible but unsupported claims, when responding to gene-specific queries. Here, we present CucurbitAgent, an AI research agent for functional genomics in the Cucurbitaceae, a major family of horticultural crops. CucurbitAgent integrates multi-omics resources from nine cultivated cucurbit species and more than 18,000 publications, encompassing approximately 256,000 genes, tissue-resolved expression profiles, over 16 million natural genetic variants, and roughly 242,000 predicted protein structures with more than 14 million predicted binding-site residue annotations. Responses link retrieved evidence to source records, including gene entries, expression measurements, structural predictions, and literature passages. On a curated benchmark of 50 tasks assessed by domain experts under blinded conditions, CucurbitAgent achieved an overall pairwise win rate of 96.0% against nine general-purpose LLMs, based on weighted scores for factual accuracy, completeness, relevance, and traceability. The platform is freely available at https://cucurbitagent.com and supports queries in 37 languages collectively spoken by an estimated 70% or more of the world's population. CucurbitAgent not only unifies evidence retrieval and interpretation but also reveals latent associations across datasets, helping researchers formulate testable hypotheses and substantially accelerating candidate-gene research. Its framework is readily transferable to other crops and, more broadly, provides a blueprint for building specialized agents in any domain.