  Language agents have achieved considerable performance on various complex
question-answering tasks by planning with external tools. 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 introduce AutoAct, an
automatic agent learning framework for QA that does not rely on large-scale
annotated data and synthetic planning trajectories from closed-source models
(e.g., GPT-4). Given limited data with a tool library, AutoAct first
automatically synthesizes planning trajectories without any assistance from
humans or strong closed-source models. Then, AutoAct leverages a
division-of-labor strategy 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 that AutoAct yields better or parallel performance compared
to various strong baselines. Further analysis demonstrates the effectiveness of
the division-of-labor strategy, with the trajectory quality generated by
AutoAct generally outperforming that of others. Code will be available at
https://github.com/zjunlp/AutoAct.
