LangGraph Enables 6 Production-Grade Multi-Agent Orchestration Patterns

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A technical writeup from IHUI-AI outlines six production-level multi-agent orchestration patterns built using LangGraph, a framework that models agent workflows as stateful directed graphs. Unlike LangChain's linear chain abstraction, LangGraph natively supports branching, looping, parallel execution, and state persistence, making it better suited for complex AI systems. The six patterns covered include sequential pipelines, parallel fan-out with result aggregation, conditional routing, and additional architectures deployed in the team's apps/ai-service/app/graphs/ directory. All orchestration patterns share a common TypedDict-based state definition that controls message passing, routing logic, and task status across agents. The article serves as an engineering summary for teams looking to move beyond single-agent limitations toward collaborative, debuggable, and observable multi-agent systems.

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