Answer patient enquiries for a clinic by chat
An n8n and Gemini agent receives patient chat messages, keeps context, uses clinic info from Google Sheets and responds to enquiries.
Original post ↗Enable n8n OTLP tracing to pinpoint which node slows a workflow and why, using a restock report as the example.
A restock report workflow ran slowly and the workflow view alone could not explain the delay. Turning on trace export to an OTLP backend revealed the bottleneck was not the node logic itself but nearly all of the HTTP calls waiting on a database connection.
Configure n8n to send traces for every workflow and node to an OTLP-compatible backend. The source points to a setup and config reference rather than listing the individual settings.
Execute the workflow you want to diagnose. In the example this was a restock report that took about six seconds end to end.
Use the workflow view to see which node consumed the most time. This narrows the search to a specific step before digging deeper.
Open the trace in the OTLP backend to see what the slow node was actually waiting on. In the example, 292 of 300 HTTP calls were blocked on a database connection, which explained the delay.
Address the underlying dependency the trace exposed, then rerun the workflow and compare trace timings to confirm the improvement.
An n8n and Gemini agent receives patient chat messages, keeps context, uses clinic info from Google Sheets and responds to enquiries.
Original post ↗Score every demo request against your ICP, update HubSpot and ping the right rep in Slack within a minute of the form submit.
Every new Zendesk ticket gets a category, priority and sentiment tag plus a draft reply in an internal note before an agent opens it.