While Large Language Models (LLMs) like ChatGPT and GPT-4 have demonstrated exceptional proficiency in natural language processing, their efficacy in addressing complex, multifaceted tasks remains limited. A growing area of research focuses on LLM-based agents equipped with external tools capable of performing diverse tasks. However, existing LLM-based agents only support a limited set of tools which is unable to cover a diverse range of user queries, especially for those involving expertise domains. It remains a challenge for LLM-based agents to extend their tools autonomously when confronted with various user queries. As GitHub has hosted a multitude of repositories which can be seen as a good resource for tools, a promising solution is that LLM-based agents can autonomously integrate the repositories in GitHub according to the user queries to extend their tool set. In this paper, we introduce GitAgent, an agent capable of achieving the autonomous tool extension from GitHub. GitAgent follows a four-phase procedure to incorporate repositories and it can learn human experience by resorting to GitHub Issues/PRs to solve problems encountered during the procedure. Experimental evaluation involving 30 user queries demonstrates GitAgent's effectiveness, achieving a 69.4% success rate on average.

翻译：尽管ChatGPT和GPT-4等大型语言模型在自然语言处理方面展现出卓越能力，但其在处理复杂多层面任务时的有效性仍存在局限。当前研究热点聚焦于配备外部工具、能执行多样化任务的基于大型语言模型的智能体。然而现有基于大型语言模型的智能体仅支持有限的工具集，无法覆盖用户多样化的查询需求，尤其涉及专业领域时。当面对各类用户查询时，如何使基于大型语言模型的智能体自主扩展工具集仍是一大挑战。鉴于GitHub托管了大量可作为优质工具资源的代码仓库，一种前景可期的解决方案是让基于大型语言模型的智能体根据用户查询自主整合GitHub仓库以扩展其工具集。本文提出的GitAgent智能体能够实现来自GitHub的自主工具扩展。GitAgent通过四阶段流程整合代码仓库，并可借助GitHub的Issues/PRs学习人类经验以解决流程中遇到的问题。在30项用户查询的实验中，GitAgent展现出68.4%的平均成功率。