Geological disaster knowledge services play a critical role in emergency command, public safety, and science communication. However, existing large language model (LLM)-based question-answering systems still have limitations in multi-source heterogeneous knowledge fusion, complex task planning, and highly trustworthy reasoning. To address these issues, this study proposes a multi-agent collaborative reasoning framework for geological disaster knowledge services. Driven by explicit task states, the proposed framework decomposes complex queries into subtasks such as retrieval, analysis, generation, and verification through a role-based agent collaboration mechanism and ensures the traceability of the reasoning process and the controllability of results through task write-back and closed-loop consistency verification. At the data level, the framework supports unified semantic access to knowledge from multiple sources, such as popular science and domain-specific knowledge, laws and regulations, and disaster records. At the reasoning level, a hierarchical reasoning process based on collaborative execution and quality control is constructed. Experimental results demonstrate that the proposed framework outperforms the baseline method, which is based on a single model combined with external retrieval, in terms of domain knowledge coverage, answer consistency, and collaborative reasoning capability, and shows more stable performance in high-reliability tasks involving laws, regulations, and disaster statistics. This study provides a feasible systematic solution for the application of multi-agent LLMs in disaster emergency knowledge services.