Track AI model executions with LangFuse observability for better performance insights
## About this template
This template is to demonstrate how to trace the observations per execution ID in Langfuse via ingestion API.
## Good to know
* Endpoint: `https://cloud.langfuse.com/api/public/ingestion`
* Auth is a `Generic Credential Type` with a `Basic Auth`: `username` = `you_public_key`, `password` = `your_secret_key`.
## How it works
* **Trigger**: the workflow is executed by another workflow after an AI run finishes (input parameter `execution_id`).
* **Remove duplicates**
Ensures we only process each `execution_id` once (optional but recommended).
* **Wait to get execution data**
Delay (60-80 secs) so totals and per-step metrics are available.
* **Get execution**
Fetches workflow metadata and token totals.
* **Code: structure execution data**
Normalizes your run into an array of `perModelRuns` with model, tokens, latency, and text previews.
* **Split Out** → **Loop Over Items**
Iterates each run step.
* **Code: prepare JSON for Langfuse**
Builds a batch with:
* trace-create (stable id trace-<executionId>, grouped into session-<workflowId>)
* generation-create (model, input/output, usage, timings from latency)
* **HTTP Request to Langfuse**
Posts the batch. Optional short Wait between sends.
## Requirements
1. Langfuse Cloud project and API keys
2. n8n instance with the HTTP node
## Customizing
1. Add span-create and set `parentObservationId` on the generation to nest under spans.
2. Add scores or feedback later via score-create.
3. Replace `sessionId` strategy (per workflow, per user, etc.). If some steps don’t produce tokens, compute and set usage yourself before sending.
