["Transactions in GIS, Volume 30, Issue 5, August 2026. ", "\nABSTRACT\nRoad network vulnerability analysis is crucial for improving the reliability and sustainability of urban transportation systems. However, conventional methods struggle to handle multi‐scenario complex reasoning. To address this issue, we propose an automated LLM‐based multi‐agent pipeline for road network vulnerability analysis. The collaboration pipeline includes three modules: a requirement thinker module for interpreting analysis requirements and formalizing scenario‐specific properties; a strategy maker module that leverages a knowledge base to generate scenario‐adaptive strategies; and a vulnerability analyst module that performs reasoning and refinement to complete the analysis. Furthermore, the verification and memory summarization mechanisms are integrated to improve the reasoning stability and computational efficiency. Experiments show that our approach outperforms manual and single‐LLM methods. Ablation studies indicate that removing verification and memory summarization mechanisms reduces precision by 10.01% and 3.33%. Overall, the proposed LLM‐based multi‐agent pipeline offers an adaptive and reliable end‐to‐end solution for road network vulnerability analysis.\n"]