🚀 LangGraph Series 9/24 – First LangGraph Application

Description

🚀 LangGraph Series 9/24 – First LangGraph Application How do you build your first AI workflow using LangGraph? LangGraph makes it easy to design structured, multi-step AI systems using graph-based workflows. Here’s the simple process 👇 🧩 Step 1 — Define State Create a structure that stores workflow data such as: • User input • Intermediate results • Final output ⚙️ Step 2 — Create Nodes Build functions that perform tasks in the workflow. Examples: • LLM calls • Tool execution • Data processing 🔗 Step 3 — Define Transitions Connect nodes with edges to control how data flows between them. 🧱 Step 4 — Compile Graph Convert the workflow into an executable graph. ▶️ Step 5 — Execute Run the graph with input and let LangGraph process each step. 💡 The result? A graph-based AI application capable of complex reasoning, tool usage, and multi-step workflows. This post is part of my LangGraph Learning Series (9/24) where I simplify Agentic AI development using visual infographics. Follow The ThinkLab by Saurabh for upcoming posts on: • LangGraph architecture • AI agents • Tool integration • Production AI systems #AI #LangGraph #AgenticAI #ArtificialIntelligence #MachineLearning #LangChain #AIEngineering #Developers #TechLearning