With the rapid advancements in Large Language Models (LLMs), LLM-based agents have introduced convenient and user-friendly methods for leveraging tools across various domains. In the field of astronomical observation, the construction of new telescopes has significantly increased astronomers' workload. Deploying LLM-powered agents can effectively alleviate this burden and reduce the costs associated with training personnel. Within the Nearby Galaxy Supernovae Survey (NGSS) project, which encompasses eight telescopes across three observation sites, aiming to find the transients from the galaxies in 50 mpc, we have developed the \textbf{StarWhisper Telescope System} to manage the entire observation process. This system automates tasks such as generating observation lists, conducting observations, analyzing data, and providing feedback to the observer. Observation lists are customized for different sites and strategies to ensure comprehensive coverage of celestial objects. After manual verification, these lists are uploaded to the telescopes via the agents in the system, which initiates observations upon neutral language. The observed images are analyzed in real-time, and the transients are promptly communicated to the observer. The agent modifies them into a real-time follow-up observation proposal and send to the Xinglong observatory group chat, then add them to the next-day observation lists. Additionally, the integration of AI agents within the system provides online accessibility, saving astronomers' time and encouraging greater participation from amateur astronomers in the NGSS project.

翻译：随着大语言模型（LLM）的快速发展，基于LLM的智能体为跨领域工具调用提供了便捷易用的方法。在天文观测领域，新建望远镜的部署显著增加了天文学家的工作负荷。部署基于LLM的智能体可有效缓解这一负担，并降低人员培训成本。在"邻近星系超新星巡天"（NGSS）项目中——该项目涵盖三个观测站点的八台望远镜，旨在探测50百万秒差距内星系中的暂现源——我们开发了**星语望远镜系统**以管理整个观测流程。该系统实现了观测列表生成、观测执行、数据分析及向观测者反馈的全自动化。观测列表根据不同站点和策略进行定制，以确保对天体的全面覆盖。经人工验证后，这些列表通过系统中的智能体上传至望远镜，系统通过自然语言指令启动观测。观测图像被实时分析，检测到的暂现源将即时通知观测者。智能体会将其转化为实时后续观测提案发送至兴隆观测站群组，并加入次日观测列表。此外，系统中AI智能体的集成提供了在线访问功能，既节省了天文学家的时间，也促进了业余天文学家更广泛地参与NGSS项目。