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Work in progress: Telemetry documentation is still being updated. Integration steps and APIs may be incomplete or out of date. Verify against your SDK versions and check back for revisions.

Overview

This guide shows you how to integrate Latitude Telemetry into an application that uses LangChain.
You’ll keep calling LangChain exactly as you do today. Telemetry simply observes and enriches those calls.

Requirements

  • A Latitude account and API key
  • A Latitude project slug
  • A project that uses LangChain

Steps

1

Install

npm install @latitude-data/telemetry
2

Initialize and use

import { initLatitude, capture } from "@latitude-data/telemetry"
import { ChatOpenAI } from "@langchain/openai"
import { HumanMessage } from "@langchain/core/messages"

const latitude = initLatitude({
  apiKey: process.env.LATITUDE_API_KEY!,
  projectSlug: process.env.LATITUDE_PROJECT_SLUG!,
  instrumentations: ["langchain"],
})

await latitude.ready

const llm = new ChatOpenAI({ modelName: "gpt-4o" })

await capture("langchain-query", async () => {
  const response = await llm.invoke([new HumanMessage("Hello")])
  return response.content
})

await latitude.shutdown()

Seeing Your Traces

Once connected, traces appear automatically in Latitude:
  1. Open your project in the Latitude dashboard
  2. Each execution shows input/output messages, model, token usage, latency, and errors
  3. LangChain chain steps appear as child spans

That’s It

No changes to your LangChain calls: just initialize Latitude and your LLM calls are traced.