  Recent advancements in large language models (LLMs) have brought significant
changes to various domains, especially through LLM-driven autonomous agents. A
representative scenario is in software development, where LLM agents
demonstrate efficient collaboration, task division, and assurance of software
quality, markedly reducing the need for manual involvement. However, these
agents frequently perform a variety of tasks independently, without benefiting
from past experiences, which leads to repeated mistakes and inefficient
attempts in multi-step task execution. To this end, we introduce Experiential
Co-Learning, a novel LLM-agent learning framework in which instructor and
assistant agents gather shortcut-oriented experiences from their historical
trajectories and use these past experiences for future task execution. The
extensive experiments demonstrate that the framework enables agents to tackle
unseen software-developing tasks more effectively. We anticipate that our
insights will guide LLM agents towards enhanced autonomy and contribute to
their evolutionary growth in cooperative learning. The code and data are
available at https://github.com/OpenBMB/ChatDev.
