Mar 7
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Welcome back to my YouTube channel SummarizedAI. GitHub Code Reference: https://github.com/toimrank/summarizedai/blob/develop/langgraph/loops.py In today’s video, we’ll understand loops and cycles in LangGraph and why they are a critical concept when building intelligent, real-world AI workflows. Loops (also called cycles) allow a LangGraph workflow to repeat certain steps until a condition is met, instead of executing nodes just once from start to end. We begin by looking at a simple graph with no loops, where execution flows linearly. Then we introduce loops, which are created when an edge in the graph points back to a previous node—allowing the workflow to retry, refine, and improve results. In this video, you’ll learn: 1. What loops and cycles mean in LangGraph 2. How loops are created using edges 3. Why loops are essential for retries and refinement 4. How shared state enables looping logic 5. How decision nodes control whether the graph continues or exits We also walk through a real-world use case where: 1. A user asks a question 2. The question is stored in shared state 3. An answer is generated 4. The answer is evaluated and scored 5. The workflow loops back to improve the answer if needed 6. The graph exits only when the answer is good enough or retry limits are reached This pattern ensures the user always receives the best possible answer, while LangGraph keeps track of everything using shared state. If you found this video helpful, don’t forget to like, share, and subscribe to SummarizedAI. #LangGraph #LoopsInLangGraph #CyclesInGraphs #AIAgents #StatefulAI #AIWorkflows #LangChain #GraphBasedAI #LLM #RetryLogic #DecisionMaking #AIEngineering #ArtificialIntelligence #MachineLearning #SummarizedAI