Mastra agent telemetry

Test the integration

Send and verify events with Mastra agent telemetry

Use Mastra agent telemetry where your app knows the final result. Collect only the fields you need, then verify a test event before building charts.

1
Choose the outcome
Mastra agent observability
2
Define the contract
run_id, workflow, agent_name, model_alias, prompt_version, and release
3
Log the final result
Wrap agent.generate, agent.stream, or the workflow run call. Emit one application outcome after it finishes.
4
Check the stored event
Exercise a known fixture, then inspect agent_run_completed for one correctly typed terminal row.

Before you start

Mastra and telemetry-sh initialized in a trusted server runtime
Stable agent, workflow, prompt-version, and outcome categories
An approved policy for collecting prompts, responses, memory, tool payloads, traces, and scorer output

Delivery setup

Install and initialize server-side

Import telemetry-sh in server-only code and initialize it once with process.env.TELEMETRY_API_KEY. Keep ingestion credentials out of browser bundles, client-visible environment variables, source control, logs, and exception messages.

mastra-install

npm installation

bash

npm install telemetry-sh

1Create one reusable server-side client. Set its timeout and retry limit.
2Log an event when the operation succeeds, fails, retries, or times out.
3Send test events with known results and inspect the stored rows before enabling an alert.

Snippet

Start with one structured event

Add this shape where the workflow completes, fails, or retries. Then build the dashboard from real fields.

mastra

Mastra agent telemetry event

javascript

async function runSupportAgent(prompt, runId) {
  const startedAt = performance.now();
  let status = "success";
  let errorType;

  try {
    return await supportAgent.generate(prompt);
  } catch (error) {
    status = "failed";
    errorType = classifyAgentError(error);
    throw error;
  } finally {
    await telemetry.log("agent_run_completed", {
      run_id: runId,
      workflow: "support_resolution",
      agent_name: "support_agent",
      status,
      error_type: errorType,
      duration_ms: Math.round(performance.now() - startedAt),
      release: process.env.APP_RELEASE,
    });
  }
}

Event schema

run_id, workflow, agent_name, model_alias, prompt_version, and release

status, duration_ms, tool_call_count, retry_count, and estimated_cost_usd

accepted, scorer_category, human_handoff, error_type, and environment

Check your setup

Checkpoint 1

Wrap agent.generate, agent.stream, or the workflow run call. Emit one application outcome after it finishes.

Checkpoint 2

Use Mastra's built-in logs, traces, and evals to investigate individual runs. Send Telemetry the final application outcome.

Checkpoint 3

Use a fixed set of model and provider names. Exclude messages, memory contents, retrieved documents, and tool arguments by default.

Verification

Prove the event arrived

Run this after exercising known success and failure cases. Replace the fallback table name if your final event contract differs from the snippet.

mastra-verification

Mastra agent telemetry verification query

sql

SELECT *
FROM agent_run_completed
ORDER BY timestamp_utc DESC
LIMIT 20;

Confirm one terminal row per logical outcome, with the expected status, identifiers, units, and UTC time.

Inspect the inferred schema and verify that retries do not change field types or generate a new logical event ID.

Search the stored fields for credentials, raw payloads, prompts, private content, and unbounded error messages.

Exercise a provider timeout, ingestion rejection, and process shutdown before treating the dashboard as complete.

Implementation references

Review the event contract, data-safety guidance, and upstream primary documentation before enabling a new production path.

Tracking LLM costs RAG evaluation SQL Canonical wide events Mastra agents overview Mastra observability overview

Where to log

Keep the outcome event small and recoverable

This pattern provides

Record the outcome as an event you can query with SQL.
Stable fields for dashboards, alerts, and cross-event correlation.
Test events for checking success, failure, retries, and timeouts.

This pattern does not provide

An OTLP exporter, automatic collection pipeline, or replacement for detailed traces and diagnostic logs.
Exactly-once delivery merely because the payload contains an event ID.
Permission to collect raw provider payloads, user content, credentials, or regulated data.

Delivery and idempotency Batching and shutdown Troubleshoot ingestion

Example event schemas

Check what each event records, when to send it, and which field types it needs. Review the example payload and privacy checklist before using it in production.

AI operations10 fields