LangGraph Offers 6 Production-Ready Multi-Agent Orchestration Patterns for AI Systems

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A technical writeup from IHUI-AI details six production-grade multi-agent orchestration patterns built using LangGraph, a framework that models agent workflows as stateful directed graphs. Unlike LangChain's linear chain abstraction, LangGraph supports branching, looping, parallel execution, and state persistence natively, making it better suited for complex AI pipelines. The six patterns covered include sequential pipelines, parallel fan-out with result aggregation, conditional routing, iterative feedback loops, hierarchical supervisor-agent structures, and human-in-the-loop workflows. Each pattern is illustrated with Python code drawn from real deployments in the IHUI-AI codebase under apps/ai-service/app/graphs/. The article argues that single LLM agents struggle with long tasks and multi-step reasoning, making structured multi-agent coordination essential for reliable production AI systems.

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