Build Agentic RAG with LangGraph in Python

Build an agentic RAG pipeline in LangGraph that decomposes complex queries, grades every retrieved chunk for relevance, and re-searches automatically when quality is low — all in Python with about 40 lines of core logic. What you'll learn: - Decompose multi-hop questions into focused sub-queries with an LLM - Grade retrieved documents for relevance before they reach the generator - Build a self-corrective retrieval loop that rewrites and retries weak queries - Wire decompose → retrieve → grade → synthesize as a LangGraph state graph The video starts with why basic RAG breaks on complex questions, then walks through the full implementation: a Pydantic state schema, a ChromaDB vector store, and three LangGraph nodes connected by conditional edges. You'll see how the relevance grader triggers automatic query rewriting, and how the pipeline synthesizes a final answer from multiple retrieved sources. [GitHub Repo] https://github.com/ByteBuilderLabs/AI-Demos/tree/main/agentic-rag [LangGraph Docs] https://langchain-ai.github.io/langgraph/ [LangChain Docs] https://python.langchain.com/ [ChromaDB Docs] https://docs.trychroma.com/ Subscribe to ByteBuilder for weekly AI engineering tutorials that go from concept to working code. [GitHub] https://github.com/ByteBuilderLabs #ai #aiagents #python #langgraph #langchain #retrievalaugmentedgeneration