Retrieval-augmented generation (RAG) is a method used in large language model (LLM) application development to address the limitations of LLMs by connecting them to external data sources, thereby improving the quality of generated responses. The concept of self-reflective RAG involves the use of LLMs to self-correct poor quality retrievals or generations by employing feedback loops, such as re-generating queries or re-retrieving documents. LangGraph, a new tool for implementing LLM state machines, facilitates this by allowing flexible design of RAG flows with decision points and loops, supporting complex workflows like those found in corrective RAG (CRAG) and self-reflective RAG (Self-RAG). CRAG introduces methods such as retrieval evaluation and web-based document supplementation, while Self-RAG uses self-reflection tokens to guide the RAG process. Both methods aim to enhance the relevance and quality of the information generated by LLMs, with LangGraph enabling easier implementation of these advanced RAG architectures.