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.

Expert Commentary: The Potential of GitAgent in Autonomous Tool Extension

Large Language Models (LLMs) like ChatGPT and GPT-4 have already showcased their impressive natural language processing capabilities. However, these models still face limitations when it comes to addressing complex and multifaceted tasks. To overcome this challenge, researchers have been exploring the integration of LLM-based agents with external tools that can perform a diverse range of tasks.

One of the key issues with existing LLM-based agents is that they support only a limited set of tools, which hampers their ability to cover a wide range of user queries. This is particularly problematic when it comes to queries that require expertise in specific domains. Therefore, there is a pressing need for LLM-based agents to autonomously extend their toolset in response to user queries.

A potential solution to this problem lies in leveraging GitHub repositories as a resource for tools. GitHub hosts a multitude of repositories that contain valuable tools across various domains. By autonomously integrating these repositories, LLM-based agents can expand their capabilities and better address diverse user queries.

In this context, the paper introduces GitAgent, an agent specifically designed to achieve autonomous tool extension from GitHub. GitAgent follows a four-phase procedure to incorporate repositories and leverages GitHub Issues/PRs to learn from human experiences and tackle challenges encountered during the integration procedure.

The experimental evaluation of GitAgent’s performance involved 30 user queries, and the results demonstrate its effectiveness. With an average success rate of 69.4%, GitAgent showcases its ability to autonomously extend its toolset and provide valuable insights to users.

The concept of GitAgent represents the interdisciplinary nature of current research efforts. By combining natural language processing capabilities, machine learning techniques, and knowledge extraction from GitHub repositories, GitAgent bridges the gap between language models and practical tools in various domains.

This research highlights the potential of LLM-based agents like GitAgent to revolutionize the way humans interact with technology. With further advancements and refinements, such agents can become invaluable companions, empowering users by providing comprehensive and specialized assistance across a wide range of domains.

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