Conversational Agentic RAG: LangGraph, Memory, and Multi-Turn Logic

Description

I built a fully conversational, agentic RAG system using LangGraph — not the usual “retrieve and reply” setup, but a complete workflow that can rephrase queries, filter off-topic questions, refine bad searches, manage memory, and recover when retrieval fails. In this video, I walk through how the system works end-to-end: • Multi-turn context handling • Smart topic classification • Document retrieval and grading • Retry logic with query refinement • Clean fallback responses • Full LangGraph workflow with memory Like, comment, and subscribe if you enjoy these deep-dive builds and want more hands-on Agentic AI engineering content. Get the Agentic AI Master Bundle Kit: https://aianytime5.gumroad.com/l/uqmyk Get 6 in 1 AI SaaS Projects: https://aianytime5.gumroad.com/l/fbeifc GitHub: https://github.com/AIAnytime/Agentic-RAG-using-LangGraph Build real-world AI with tutorials, tools, and research from India’s fastest-growing AI community. 👤 Creator’s LinkedIn (Sonu Kumar) Portfolio Site: https://sonukumar.site/ 🌐 AI Anytime's Website: https://aianytime.net/ 🗓️ Office Hours (AI Consulting): https://officehours.aianytime.net/ 👥 LinkedIn (Community Page): https://www.linkedin.com/company/ai-anytime/ 💬 Join Our Discord: https://discord.com/invite/4aGc9PSMgE 👤 Creator’s LinkedIn (Sonu Kumar): https://www.linkedin.com/in/sonukr0/ 🎁 Support the Channel 💸 UPI ID: sonu1000raw@ybl ₿ Bitcoin Wallet: bc1qsneqznxpzyxzzv006jthz4c8v8h5cs57myw342 ✅ Join this Channel for Perks Get access to members-only content and community perks: https://www.youtube.com/channel/UC-zVytOQB62OwMhKRi0TDvg/join #langgraph #RAG #agenticrag