Practical Agentic RAG patterns implemented with LangGraph

What happens when your RAG system retrieves the wrong documents? Or when the retrieved context is not enough to answer the question? A traditional RAG pipeline usually doesn't think twice and its path is so its a single attempt generated answer.

"Retrieve → Generate → Answer"

What I built

Retrieve → Reason → Verify → Correct → Answer

I created a collection of self contained notebooks demonstrating different Agentic RAG patterns with LangGraph. Each notebook focuses on a practical pattern that you can understand, experiment with and adapt to your own AI projects.

Free version: https://github.com/ChandulaSenevirathna/Agentic_RAG

Advanced version: https://chandula7.gumroad.com/l/Advanced_RAG_LangGraph_Patte...

Hey thankyou for the info, I was wondering if you can add a notebook about supervisor agent as well.

Useful content for my current project thanx for posting

nice content very informative keep it up