> ## Documentation Index
> Fetch the complete documentation index at: https://evalgate.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Capture your first trace

> Plain-English setup for sending one safe interaction to EvalGate.

A trace is one recorded interaction in your app. It can be an AI answer, an API
request, a background job, or a tool call. EvalGate uses traces as evidence so a
team can turn real behavior into checks and catch regressions before shipping.

## Start local

You do not need an EvalGate API key, model provider key, or telemetry collector
to inspect a repository, create local health checks, or run a local gate.

```bash theme={null}
npx @evalgate/sdk understand
npx @evalgate/sdk health --config evalgate.application.json --format github
npx @evalgate/sdk gate
```

## Send traces when ready

When you want EvalGate to store trace history in a workspace, choose the path
that matches your app:

* Python AI app: use OpenInference or OpenTelemetry instrumentation and export
  OTLP/HTTP traces to `/api/otel/v1/traces`.
* TypeScript or Node app: use an OTLP HTTP trace exporter with the EvalGate
  endpoint and an EvalGate API key.
* Existing collector: add EvalGate as an `otlphttp` exporter.
* No tracing library yet: send one manual collector payload to `/api/collector`.

The setup page can send a deterministic test trace first. That proves the
workspace can accept and store trace evidence without sending your app's real
data.

## What to expect

After a trace is stored, open Traces and search for its trace ID. You should see
the trace title, spans, inputs and outputs that survived your redaction policy,
token and model metadata when present, and a source of `otel` or `api`.