FlashRAG is a Python toolkit designed for replicating and developing research in retrieval-augmented generation (RAG). It includes 32 pre-processed benchmark RAG datasets and 12 state-of-the-art RAG algorithms. FlashRAG offers a comprehensive and customizable framework, encompassing essential components for RAG scenarios such as retriever, reranker, generator, and compressor, enabling flexible assembly of complex pipelines. Moreover, FlashRAG provides efficient preprocessing stages and optimized execution, supporting tools like vLLM and FastChat to accelerate LLM inference and vector index management.