LLMFlowGO: An LLM-Driven System for Workflow Generation and Optimization

Workflow plays a critical role in enhancing the performance and automation capability of such applications. However, traditional workflow modeling and optimization still rely on manual efforts or semi-automated tools.

How to achieve automated and adaptive workflow generation while simultaneously optimizing resource allocation in edge environments remains a significant challenge. To address these issues, we propose LLMFlowGO, an LLMdriven system that integrates automated workflow generation and resource optimization in edge computing. By leveraging the semantic understanding and reasoning capabilities of large language models, LLMFlowGO automatically transforms task descriptions into structured workflows and supports service selection and configuration aligned with application constraints.