Refactoring is crucial for maintaining a project, but it requires developers to understand code structure and system design principles well. Recent research on Large Language Models(LLMs) has shown their great capability for handling complex tasks, making them a possible solution for overcoming these challenges. In this paper, we propose MUARF, an LLM-based solution designed to automate method-level code refactoring, aiming to generate correct, high-quality, and human-like refactored code. MUARF leverages Contextual Retrieval-Augmented Generation to search for similar refactoring samples for few-shot learning, uses Multi-Agent Workflow to simulate the human refactoring process, and integrates advanced software engineering tools (e.g., RefactoringMiner, PurityChecker, StyleChecker) to assist refactoring. Evaluation results show that MUARF achieves a compilation pass rate of 86.5% and a test success rate of 83.8% for the refactored code it generates. Additionally, metrics such as CodeBLEU score and AST Diff accuracy—which compare human-refactored code with the output of MUARF —highlight the generated code is human-like. The ablation results show that RefactoringMiner and Agentware made the greatest contribution to MUARF.