The paper introduces EmoRAG, a retrieval-augmented emotion detection system designed for the SemEval-2025 Task 11. It uses an ensemble of models, retrieving similar examples to prompt large language models (LLMs) for emotion predictions. The retriever component fetches the most relevant examples from a database, which are then used as few-shot prompts for the models. EmoRAG achieves strong, scalable performance across languages with no training at all, demonstrating effectiveness in both high and low-resource languages.