LangChain agent outcome telemetry

Test the integration

Send and verify events with LangChain agent outcome telemetry

Use LangChain agent outcome 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
LangChain agent completion monitoring
2
Define the contract
run_id, agent_name, feature, model, status, and prompt_version
3
Log the final result
Use LangSmith or OpenTelemetry for detailed traces. Send selected task results to Telemetry to query alongside product and operational data.
4
Check the stored event
Exercise a known fixture, then inspect agent_run_completed for one correctly typed terminal row.

Before you start

A server-side TELEMETRY_API_KEY
Stable agent, feature, tool, and prompt-version names
A redaction policy that keeps messages, tool arguments, and outputs out of general events

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.

langchain-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.

langchain

LangChain agent outcome telemetry event

javascript

import { randomUUID } from "node:crypto";
import telemetry from "telemetry-sh";

const runId = randomUUID();
const startedAt = performance.now();

try {
  const result = await agent.invoke({
    messages: [{ role: "user", content: userInput }],
  });

  await telemetry.log("agent_run_completed", {
    run_id: runId,
    agent_name: "support_agent",
    feature: "support_resolution",
    prompt_version: "support-v6",
    status: "success",
    duration_ms: Math.round(performance.now() - startedAt),
    tool_calls: countToolCalls(result),
    human_handoff: result.handoffRequired,
    completed_task: result.resolutionCreated,
  });

  return result;
} catch (error) {
  await telemetry.log("agent_run_completed", {
    run_id: runId,
    agent_name: "support_agent",
    feature: "support_resolution",
    prompt_version: "support-v6",
    status: "failed",
    duration_ms: Math.round(performance.now() - startedAt),
    error_type: classifyAgentError(error),
  });
  throw error;
}

Event schema

run_id, agent_name, feature, model, status, and prompt_version

duration_ms, tool_calls, retries, human_handoff, and error_type

accepted, saved, completed_task, or another reviewed product outcome

Check your setup

Checkpoint 1

Use LangSmith or OpenTelemetry for detailed traces. Send selected task results to Telemetry to query alongside product and operational data.

Checkpoint 2

Record a run identifier for correlation, not message content or tool arguments.

Checkpoint 3

Define task success from a final product or reviewer outcome rather than the presence of an assistant message.

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.

langchain-verification

LangChain agent outcome 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.

Agent task-success SQL Tool-loop SQL Agent observability template LangChain JavaScript agents LangSmith observability

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 agent operations10 fields