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π LangGraph Series 9/24 β 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. Hereβs the simple process π π§© Step 1 β Define State Create a structure that stores workflow data such as: β’ User input β’ Intermediate results β’ Final output βοΈ Step 2 β Create Nodes Build functions that perform tasks in the workflow. Examples: β’ LLM calls β’ Tool execution β’ Data processing π Step 3 β Define Transitions Connect nodes with edges to control how data flows between them. π§± Step 4 β Compile Graph Convert the workflow into an executable graph. βΆοΈ Step 5 β Execute Run the graph with input and let LangGraph process each step. π‘ 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: β’ LangGraph architecture β’ AI agents β’ Tool integration β’ Production AI systems #AI #LangGraph #AgenticAI #ArtificialIntelligence #MachineLearning #LangChain #AIEngineering #Developers #TechLearning