Langgraph lets developers compose LLM pipelines as directed graphs, where each node performs a distinct operation. Retrieval nodes fetch relevant context from vector stores or databases, often using similarity search, filters, or hybrid scoring. They can be configured with custom embeddings, distance metrics, and batch sizes, and they return a list of documents or snippets that downstream nodes can consume. Generation nodes receive these retrieved pieces and construct prompts for LLMs, supporting system messages, few‑shot examples, and dynamic variable interpolation. Langgraph’s API enables chaining retrieval and generation nodes, handling async execution, state passing, and error propagation. Advanced patterns include re‑ranking retrieved results with a secondary model, conditional branching based on confidence scores, and looping constructs for multi‑turn conversations. Users can define node signatures to enforce type safety, and the framework automatically serializes state for checkpointing and resumability. Integration with popular vector databases (e.g., Pinecone, Weaviate) and LLM providers (OpenAI, Anthropic) is built‑in, while custom adapters allow any backend. Debugging tools visualize the graph, display token usage, and expose intermediate outputs, facilitating rapid iteration on retrieval‑augmented generation pipelines.