This paper introduces WalkRAG, a spatial Retrieval-Augmented Generation (RAG) framework that leverages Large Language Models (LLMs) to recommend personalized and walkable urban itineraries. It addresses known LLM limitations in spatial reasoning and factual accuracy by integrating spatial and contextual urban knowledge for enhanced route generation and point-of-interest information retrieval.