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Learn by Interaction: Advancing Agentic AI for Web Automation with LangGraph

20 February 2025, Version 1
This content is an early or alternative research output and has not been peer-reviewed by Cambridge University Press at the time of posting.

Abstract

With the rapid development of large language model technology, Web agents, as a key technology for automated Web interaction, have gradually become a research hotspot. In this study, a LangGraph - based Web agent was designed and implemented. Driven by the multimodal large language model GPT-4o, and through the automated Web browsing environment Playwright, multiple Web page operation tools were realized. The research demonstrated successful cases of the agent in Web interaction, and at the same time, revealed its challenges in aspects such as page navigation and hallucination handling. Future research will focus on optimizing the agent to improve its stability and execution efficiency in the Web environment.

Keywords

Large Language Model
Web Agent
LangGraph
GPT-4o
Hallucination

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