Build a Fully Local AI Chatbot with LangGraph, Llama 3.1 & Streamlit (No API Required)

Stop paying for expensive API keys 🚫
In this video, you’ll learn how to build a fully local, stateful AI chatbot using LangGraph, Llama 3.1, Ollama, and Streamlit — running 100% offline on your own machine.

This is not a basic prompt-chaining demo. You’ll build a real-world chatbot architecture with memory, state management, and a clean chat UI, just like production AI apps.

We move beyond simple LangChain chains and dive deep into LangGraph’s StateGraph architecture, allowing your chatbot to manage conversation history, scale logically, and behave like a true AI agent.

🚀 What You’ll Build

A real-time chatbot UI using Streamlit

A Python backend powered by LangGraph

Persistent chat memory using MemorySaver

A local LLM setup using Llama 3.1 via Ollama

A fully offline AI chatbot (no OpenAI, no paid APIs)

🧠 What You’ll Learn

How LangGraph works (and why it’s better than basic chains)

Designing chatbot workflows with StateGraph

Implementing conversation memory with MemorySaver

Running Llama 3.1 locally using Ollama

Connecting a Python backend to a Streamlit chat UI

Building scalable, state-aware AI applications

Best practices for structuring LLM-powered Python apps

🛠️ Tech Stack Used

LangGraph – Stateful, graph-based AI workflows

LangChain – Messaging and LLM abstractions

Ollama – Local LLM runtime

Llama 3.1 – Open-source large language model

Streamlit – Chat UI & frontend

Python – Backend logic

🎯 Who This Video Is For

Python developers learning AI & LLMs

Developers building local AI applications

AI engineers exploring LangGraph & agents

Anyone avoiding OpenAI API costs & limits

Beginners who want real architecture, not toy demos

By the end of this video, you’ll know how to build production-ready local AI chatbots using modern open-source tools.

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