How to Build a LangGraph Support Triage Workflow with Trace Visibility

A lot of LangGraph demos prove that graphs can run. Fewer prove that teams can operate them. That difference matters. Once a workflow starts classifying tickets, choosing queues, deciding whether to escalate, and generating internal summaries, the important question is no longer just "did the graph execute?" It becomes "why did it make that decision?" That is the motivation behind langgraph-ticket-triage , a small Python starter that shows how to build a support triage workflow with LangGraph, FastAPI, and Tokvera trace visibility. Why LangGraph workflows need observability LangGraph is useful because it gives you a clean way to model multi-step workflows. But in production-like systems, graph execution alone is not enough. Teams still need to understand: how a ticket was classified why a queue was selected whether escalation logic was applied what summary was generated for the internal team whether the result came from mock mode or a live model call Without that visibility, graph-base