Language agents have achieved considerable performance on various complex question-answering tasks. Despite the incessant exploration in this field, existing language agent systems still struggle with costly, non-reproducible data reliance and face the challenge of compelling a single model for multiple functions. To this end, we introduceAutoAct, an automatic agent learning framework that does not rely on large-scale annotated data and synthetic trajectories from closed-source models (e.g., GPT-4) . Given limited data with a tool library,AutoActfirst automatically synthesizes planning trajectories without any assistance from humans or strong closed-source models. Then,AutoActleverages adivision-of-laborstrategy to automatically differentiate based on the target task information and synthesized trajectories, producing a sub-agent group to complete the task. We conduct comprehensive experiments with different LLMs, which demonstrates thatAutoActyields better or parallel performance compared to various strong baselines. Further analysis demonstrates the effectiveness of thedivision-of-laborstrategy, with the trajectory quality generated byAutoActsignificantly outperforming that of others111Code will be available athttps://github.com/zjunlp/AutoAct..