"Most AI frameworks treat interactions as a straight line (Input → Process → Output). LangGraph changes this paradigm by treating interactions as a Graph (specifically, a State Machine). Imagine a whiteboard where you draw circles (Nodes) and arrows (Edges). Nodes are places where work happens (e.g., "Think," "Call Weather API," "Summarize"). Edges are the paths taken based on results. State is a shared clipboard that travels along these arrows, accumulating information."
"Semantic Routing replaces hardcoded logic with a specialized LLM prompt. Instead of scanning for keywords, the Router Node accepts the user's messy, human input and asks an LLM: "Given this input, which of our available tools is the best fit?" User: "I'm worried about my investments dropping." Keyword Router: Might miss this because the word "stock" isn't present. Semantic Router: Understands the meaning implies finance, and routes to the Financial Agent."
LangGraph reframes AI interaction flow from linear pipelines to a state-machine graph where Nodes perform work, Edges direct flow, and a shared State clipboard accumulates context. Persistent state enables graceful error handling and retries when agents fail instead of crashing the whole application. A Semantic Router uses an LLM prompt to select the most appropriate agent from messy user input, replacing brittle keyword-based if/else logic. Agents are implemented as HTTP microservices running remotely, allowing distributed specialized services (e.g., a Calendar Agent in Europe or an Image Gen Agent elsewhere). Combined, these elements create a dynamic, context-aware orchestration layer.
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