Getting Started with Agentic RAG Using LangGraph

Agentic RAG is an upgrade over the usual “retrieve-then-generate” flow: it outfits your AI with a little bit of smarts so it can decide when, what and how to fetch info before it crafts an answer. This tutorial walks you through a step-by-step implementation using LangGraph—complete with code samples, architecture diagrams and reasoning modules that let your agent dynamically plan its retrieval and generation strategy.

Ready to dive in? All code lives on GitHub (github.com/krishnaik06/RAG-Tutorials), and the guide breaks down how to configure your vector store, define your agents, orchestrate multi-hop retrievals and plug it all together into a seamless RAG pipeline.

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