Agent AI with Lang Graph: A Modular Framework for Enhancing Machine Translation Using Large Language Models

Abstract

Jialin Wang and Zhihua Duan

This paper explores the transformative role of Agent AI and Lang Graph in advancing the automation and effectiveness of ma- chine translation (MT). Agents are modular components designed to perform specific tasks, such as translating between particular languages, with specializations like Translate En Agent, Translate French Agent, and Translate Jp Agent for English, French, and Japanese translations, respectively. These agents leverage the powerful semantic capabilities of large language models (LLMs), such as GPT-4o, to ensure accurate, contextually relevant translations while maintaining modularity, scalability, and context retention.

Lang Graph, a graph-based framework built on Lang Chain, simplifies the creation and management of these agents and their workflows. It supports dynamic state management, enabling agents to maintain dialogue context and automates complex workflows by linking agents and facilitating their collaboration. With flexibility, open-source community sup- port, and seamless integration with LLMs, Lang Graph empowers agents to deliver high-quality translations.

Together, Agent AI and Lang Graph create a cohesive system where Lang Graph orchestrates agent interactions, ensuring that user inputs are analyzed, routed, and processed efficiently. Experimental results demonstrate the potential of this system to enhance multilingual translation accuracy and scalability. By highlighting modular design and automated workflows, this paper sets the stage for further innovations in intelligent machine translation services.

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