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Overview

This guide shows you how to integrate Latitude Telemetry into an existing application that uses the official LangChain SDK. After completing these steps:
  • Every LangChain call (e.g. invoke) can be captured as a log in Latitude.
  • Logs are grouped under a prompt, identified by a path, inside a Latitude project.
  • You can inspect inputs/outputs, measure latency, and debug LangChain-powered features from the Latitude dashboard.
You’ll keep calling LangChain exactly as you do today — Telemetry simply observes and enriches those calls.

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Requirements

Before you start, make sure you have:
  • A Latitude account and API key
  • A Latitude project ID
  • A Node.js-based project that uses the LangChain SDK
That’s it — prompts do not need to be created ahead of time.

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Steps

1

Install requirements

Add the Latitude Telemetry package to your project:
npm add @latitude-data/telemetry
2

Initialize Latitude Telemetry

Create a single LatitudeTelemetry instance when your app startsYou must pass the LangChain SDK so Telemetry can instrument it.
telemetry.ts
import { LatitudeTelemetry } from '@latitude-data/telemetry'
import * as LangchainCallbacks from '@langchain/core/callbacks/manager'

export const telemetry = new LatitudeTelemetry(
  process.env.LATITUDE_API_KEY,
  {
    instrumentations: {
      langchain: {
        callbackManagerModule: LangchainCallbacks, // This enables automatic tracing for the LangChain SDK
      },
    },
  }
)
The Telemetry instance should only be created once. Any LangChain client instantiated after this will be automatically traced.
3

Wrap your LangChain-powered feature

Wrap the code that calls LangChain using telemetry.capture.
import { telemetry } from './telemetry'
import { createAgent } from "langchain";

export async function generateSupportReply(input: string) {
  return telemetry.capture(
    {
      projectId: 123, // The ID of your project in Latitude
      path: 'generate-support-reply', // Add a path to identify this prompt in Latitude
    },
    async () => {

      // Your regular LLM-powered feature code here
      const agent = createAgent({ model: 'claude-sonnet-4-5' });
      const result = await agent.invoke(...);

      // You can return anything you want — the value is passed through unchanged
      return result;
    }
  )
}
The path:
  • Identifies the prompt in Latitude
  • Can be new or existing
  • Should not contain spaces or special characters (use letters, numbers, - _ / .)

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Seeing your logs in Latitude

Once your feature is wrapped, logs will appear automatically.
  1. Open the prompt in your Latitude dashboard (identified by path)
  2. Go to the Traces section
  3. Each execution will show:
    • Input and output messages
    • Model and token usage
    • Latency and errors
    • One trace per feature invocation
Each LangChain call appears as a child span under the captured prompt execution, giving you a full, end-to-end view of what happened.

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That’s it

No changes to your LangChain calls, no special return values, and no extra plumbing — just wrap the feature you want to observe.