Langraph is a framework that represents language model prompts as graph structures, enabling modular composition of retrieval, reasoning, and generation steps. Nodes encapsulate tasks such as querying external knowledge bases, while edges define data flow between them. Retrieval-Augmented Generation (RAG) integrates a dense retriever with a language model, fetching relevant documents to ground the model's output, reducing hallucinations and improving factual accuracy. Langraph automates RAG pipelines by allowing developers to declaratively specify retrieval nodes, post‑processing transforms, and final generation nodes, handling caching, batching, and error propagation. The system supports hybrid retrieval (BM25 + dense vectors), dynamic document scoring, and context window management to fit retrieved passages into the model's token limit. By visualizing the graph, engineers can debug data paths, experiment with alternative retrieval strategies, and reuse components across projects. Langraph also provides adapters for popular LLM APIs, enabling seamless switching between models without rewriting the pipeline. Overall, Langraph abstracts the complexity of building RAG applications, offering a scalable, maintainable, and extensible architecture for knowledge‑augmented language generation.