Addressing multi-hop reasoning of complex query effectively is a challenging task in information retrieval field. It demands the ability to retrieve and integrate dispersed knowledge across multiple documents dynamically while maintaining coherence in multi-step reasoning process. This study addresses these challenges with three primary contributions. It explores the integrating of large language models with graph-augmented retrieval methods for complex multihop reasoning. Moreover, the Memory-Driven Chain-of-Reasoning strategy is introduced, leveraging the memory of historical queries and results to optimize multi-step reasoning dynamically. Additionally, the Constraint-Aware Filtering in Chunked Window strategy is developed to improve retrieval precision by partitioning and filtering large retrieval windows based on query constraints. The experiments on public benchmarks indicate that our method substantially outperforms competitive approaches, achieving up to 13.8% and 14.0% improvements in EM and F1 on HotpotQA, 9.4% and 12.8% on MuSiQue, and 6.4% and 3.2% on 2WikiMultiHopQA, respectively.