Medical question answering (QA) is a knowledge-intensive task demanding evidence-based reasoning across diverse sources. Despite the demonstrated efficacy of augmenting large language models (LLMs) with domain-specific knowledge graphs (KGs) for QA tasks, the prevailing single-KG paradigm neglects the benefits of multi-KG complementarity. This paper introduces KGFlow, a novel multi-KG reasoning paradigm that mitigates the incomplete coverage and fragmented evidence of single-KG approaches through three LLM agents. Given a query, Knowledge Explorer extracts relevant knowledge triples in parallel from heterogeneous sources. Graph Aligner then semantically harmonizes these triples into global reasoning paths, which are filtered by Contextual Pruner to preserve the most valuable paths for inference. Extensive experiments show that KGFlow achieves promising performance improvements compared to state-of-the-art techniques on five medical QA datasets. We further observe by cases that the plug-and-play nature of our framework enables its application to diverse KG combinations and multilingual scenarios, resulting in broader knowledge coverage than single-KG approaches.