Large Language Models (LLMs) show promise in recommender systems due to their generalisation and in-context learning abilities. However, effective top-k recommendation typically requires a multi-stage workflow, namely user modelling, knowledge retrieval, initial ranking, and re-ranking that a single LLM cannot handle end-to-end. Moreover, existing approaches in the literature often optimise only parts of this workflow, limiting the overall performance. To address this, we propose KMAR, a knowledge-enhanced multi-agent framework that covers all four stages. KMAR includes a coordinator that adapts agent selection based on dataset characteristics and can trigger a debate–reflection routine for output refinement. Each agent specialises in a key stage, from user preference modelling to final re-ranking using a fine-tuned Llama-3.1. Experiments on three datasets show KMAR outperforms eleven state-of-the-art methods by up to 7.44%, with further gains from dynamic agent coordination and debate–reflection. Our source code is available at https://github.com/terrierteam/KMAR .