Build Powerful Multi-Agent AI Workflows with LangGraph - (Implementation)

Description

In this video, we build a complete Multi-Agent AI System using LangGraph, LangChain, Grok, Gemini, and Llama inside a Jupyter Notebook. 🚀 Instead of using one LLM for every task, we create specialized AI agents where each agent performs a specific responsibility — just like real-world teams. 🔹 Research Agent → Grok 🔹 Writer Agent → Gemini 🔹 Critique Agent → Llama/Ollama 🔹 Improve Agent → Gemini You will learn: ✅ How to create multi-agent workflows in LangGraph ✅ How to initialize multiple LLMs using LangChain ✅ How to define workflow states using TypedDict ✅ How to create nodes, edges, and conditional routing ✅ How critique-feedback loops improve AI responses ✅ How to compile and execute LangGraph workflows This tutorial shows the real power of Agentic AI Systems and how specialization creates better AI workflows than using a single agent for everything. 📌 Technologies Used: * LangGraph * LangChain * Grok API * Google Gemini API * Ollama / Llama * Python * Jupyter Notebook Perfect for learners interested in: AI Agents • Multi-Agent Systems • Agentic AI • LangGraph • LangChain • LLM Engineering • Generative AI #LangGraph #LangChain #MultiAgentSystems #AIAgents #AgenticAI #GenerativeAI #Python #LLM #ArtificialIntelligence