Apr 15
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Hello everyone, Welcome back to SummarizedAI! In today’s video, we dive into an important concept in LangGraph — Fan-Out and Fan-In patterns. Imagine you’re writing a blog post: Step 1: Decide the topic → “Explain LangGraph Workflows” (Start step of the graph) Step 2: Plan and delegate tasks to multiple helpers: - One writes the introduction - Another writes examples - Another writes code snippets All helpers work in parallel Merge Node: Combines all outputs into a single final blog post Key Concepts: Fan-Out Pattern: Planner node triggers multiple independent nodes in parallel → reduces execution time Fan-In Pattern: Merge node collects results from parallel nodes → combines into **one output** Why Fan-In/Fan-Out? Efficiently handles multiple subtasks, enables concurrency, and produces a final consolidated result GitHub Code Reference: https://github.com/toimrank/summarizedai/blob/develop/langgraph/fan_out_in.py If you enjoyed this tutorial: Like | Comment your questions Subscribe for more AI & System Design content #LangGraph #PythonTutorial #FanOutFanIn #ParallelWorkflows #AIWorkflows #SystemDesign #SummarizedAI #LLMAutomation #PythonAI #ProgrammingTutorial