Large language model agents for biological intelligence across genomics, proteomics, spatial biology, and biomedicine

Abstract

Large language models (LLMs) are evolving from passive predictors into agentic systems capable of planning, tool-use, and multimodal reasoning. This shift is especially consequential for biology, where complex, noisy, and multi-scale data require adaptive and integrative computational strategies. In this review, we provide the first systematic synthesis of LLM-based agents across genomics, molecular biology, imaging, biomedical analysis, and automated bioinformatics workflows. We analyze >60 emerging systems and organize them within a unifying framework that characterizes agentic traits, such as autonomous decision-making, external tool invocation, memory, and self-correction. Across domains, agentic LLMs show early promise in enabling multi-step analysis, linking heterogeneous evidence, and supporting exploratory scientific tasks. At the same time, our comparative assessment highlights consistent challenges, including unstable reasoning, limited biological grounding, retrieval misalignment, and barriers to reproducibility and biosafety. We conclude by outlining opportunities for trustworthy and collaborative biological agents, including multimodal integration, closed-loop experimental design, and robust evaluation practices. This survey aims to clarify the emerging landscape and chart a path toward reliable agentic systems for biological discovery.

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