FlashRAG is a modular, efficient open-source toolkit designed to assist researchers in reproducing and developing Retrieval-Augmented Generation (RAG) methods. It implements 12 advanced RAG methods and provides 32 benchmark datasets, along with a customizable framework, pre-implemented RAG components, and comprehensive evaluation metrics. The toolkit features a modular component module with five key components: Judger, Retriever, Reranker, Refiner, and Generator. It also includes a pipeline module that supports various RAG process types, including sequential, branching, conditional, and loop pipelines. FlashRAG offers extensive datasets, including 32 benchmark datasets, and provides efficient preprocessing scripts and tools for corpus creation. The toolkit is designed to streamline RAG research by enabling researchers to easily replicate existing methods and develop new algorithms within a unified framework. FlashRAG addresses the limitations of existing RAG toolkits by offering a more flexible and user-friendly solution, with features such as pre-implemented RAG algorithms, efficient preprocessing, and comprehensive evaluation metrics. The toolkit is available at https://github.com/RUC-NLPIR/FlashRAG.FlashRAG is a modular, efficient open-source toolkit designed to assist researchers in reproducing and developing Retrieval-Augmented Generation (RAG) methods. It implements 12 advanced RAG methods and provides 32 benchmark datasets, along with a customizable framework, pre-implemented RAG components, and comprehensive evaluation metrics. The toolkit features a modular component module with five key components: Judger, Retriever, Reranker, Refiner, and Generator. It also includes a pipeline module that supports various RAG process types, including sequential, branching, conditional, and loop pipelines. FlashRAG offers extensive datasets, including 32 benchmark datasets, and provides efficient preprocessing scripts and tools for corpus creation. The toolkit is designed to streamline RAG research by enabling researchers to easily replicate existing methods and develop new algorithms within a unified framework. FlashRAG addresses the limitations of existing RAG toolkits by offering a more flexible and user-friendly solution, with features such as pre-implemented RAG algorithms, efficient preprocessing, and comprehensive evaluation metrics. The toolkit is available at https://github.com/RUC-NLPIR/FlashRAG.