Sequential Workflow in LangGraph Explained | Step-by-Step AI Execution

Description

Welcome back to my YouTube channel SummarizedAI. GitHub Code Reference: https://github.com/toimrank/summarizedai/blob/develop/langgraph/langgraph_sequential_workflow.py In today’s video, we’ll understand Sequential Workflows in LangGraph using a simple real-world analogy and a practical implementation. We begin with an easy example—cooking food—where each step must happen in order: cut vegetables → cook → serve. You can’t skip steps, and you can’t change the order. This is exactly how a sequential workflow behaves. A Sequential Workflow in LangGraph is a process where: 1. Tasks execute one after another 2. Each step starts only after the previous step completes 3. Every step depends on the output of the previous step In this video, you’ll learn: 1. What a sequential workflow is in LangGraph 2. How shared state passes data between steps 3. How each node reads from and writes to the same state 4. Why sequential execution is important for predictable workflows We walk through a real-world use case where: 1. A user provides a question or requirement 2. The question is stored in shared state 3. Code is generated based on the requirement 4. The generated code is built 5. The application is deployed 6. The final state is returned to the end user At the end of the workflow, the user receives: 1. The original question 2. Generated code 3. Build status 4. Deployment status This pattern is ideal for linear, dependent, and production-style AI workflows. If you found this video helpful, don’t forget to like, share, and subscribe to SummarizedAI. #LangGraph #SequentialWorkflow #AIWorkflows #AIAgents #LangChain #StatefulAI #GraphBasedAI #LLM #AIEngineering #Python #ArtificialIntelligence #MachineLearning #WorkflowAutomation #SummarizedAI