  Single-cell RNA sequencing (scRNA-seq) data analysis is crucial for
biological research, as it enables the precise characterization of cellular
heterogeneity. However, manual manipulation of various tools to achieve desired
outcomes can be labor-intensive for researchers. To address this, we introduce
CellAgent (http://cell.agent4science.cn/), an LLM-driven multi-agent framework,
specifically designed for the automatic processing and execution of scRNA-seq
data analysis tasks, providing high-quality results with no human intervention.
Firstly, to adapt general LLMs to the biological field, CellAgent constructs
LLM-driven biological expert roles - planner, executor, and evaluator - each
with specific responsibilities. Then, CellAgent introduces a hierarchical
decision-making mechanism to coordinate these biological experts, effectively
driving the planning and step-by-step execution of complex data analysis tasks.
Furthermore, we propose a self-iterative optimization mechanism, enabling
CellAgent to autonomously evaluate and optimize solutions, thereby guaranteeing
output quality. We evaluate CellAgent on a comprehensive benchmark dataset
encompassing dozens of tissues and hundreds of distinct cell types. Evaluation
results consistently show that CellAgent effectively identifies the most
suitable tools and hyperparameters for single-cell analysis tasks, achieving
optimal performance. This automated framework dramatically reduces the workload
for science data analyses, bringing us into the "Agent for Science" era.
