Agentic RAG with LangGraph - Part 11/13

Description

Build and test a complete retrieval-augmented generation (RAG) workflow using LangGraph's stateful message-passing architecture. This video demonstrates agentic decision-making, tool invocation, and response synthesis in a multi-step pipeline. #LangGraph #RAG #Python #AI #AgenticAI #RetrievalAugmentedGeneration #CodingTutorial Zen Koan Explanation: The code demonstrates a tool call-response cycle where the assistant first invokes `retrieve_blog_posts` (the "dreaming" action seeking answers) and later receives the actual answer via the `tool` role message with a matching `tool_call_id` (the "delivering" of answers). This mirrors the koan's theme of questioning (tool call) preceding fulfillment (tool response), where the initial search for knowledge is fulfilled by the system's eventual delivery of wisdom. Source: https://docs.langchain.com/oss/python/langgraph/agentic-rag