Maintaining high-fidelity character personas and tracking trusted narrative facts remain significant challenges for LLM-based role-playing systems, particularly in long-context scenarios. Traditional Retrieval-Augmented Generation (RAG) approaches, which typically rely on static, stateless retrieval, often struggle to capture evolving plot dynamics, leading to character hallucinations and logical inconsistencies over prolonged interactions. To address these limitations, we present FictionRAG, a novel stateful retrieval-augmented framework designed to enhance long-narrative role-playing. FictionRAG introduces a hierarchical memory architecture that decouples narrative information into three distinct lanes: factual events, persona traits, and worldview constraints. Furthermore, it employs a failure-driven metacognitive regulatory loop that dynamically identifies and corrects retrieval deficiencies—such as persona drift or conflicting world rules—before response generation. By treating role-playing as a dynamic state tracking problem rather than simple question answering, FictionRAG ensures that generated responses are strictly grounded in both the narrative timeline and the character’s psychological profile. Extensive experiments on a dataset comprising twenty classic novels demonstrate that FictionRAG significantly outperforms existing baselines in factual accuracy, persona stability, and worldview consistency. Beyond literary role-playing, these results suggest that stateful, evidence-constrained retrieval can serve as a general mechanism for long-form controllable generation tasks that require persistent state tracking and multi-dimensional consistency.