Skip to main content
This integration is only available in the Python SDK.

​
Overview

This guide shows you how to integrate Latitude Telemetry into an existing application that uses the official Groq SDK. After completing these steps:
  • Every Groq call (e.g. chat.completions.create) 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 Groq-powered features from the Latitude dashboard.
You’ll keep calling Groq exactly as you do today — Telemetry simply observes and enriches those calls.

​
Requirements

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

​
Steps

1

Install requirements

Add the Latitude Telemetry package to your project:
pip install latitude-telemetry
2

Initialize Latitude Telemetry

Create a single Telemetry instance when your app starts.You must enable the Groq instrumentor so Telemetry can trace it.
telemetry.py
import os
from latitude_telemetry import Telemetry, Instrumentors, TelemetryOptions

telemetry = Telemetry(
    os.environ["LATITUDE_API_KEY"],
    TelemetryOptions(
        instrumentors=[Instrumentors.Groq],  # This enables automatic tracing for the Groq SDK
    ),
)
The Telemetry instance should only be created once. Any Groq client instantiated after this will be automatically traced.
3

Wrap your Groq-powered feature

Wrap the code that calls Groq using telemetry.capture.You can use the capture method as a decorator (recommended) or as a context manager:
Using decorator (recommended)
from groq import Groq
from telemetry import telemetry

@telemetry.capture(
    project_id=123,  # The ID of your project in Latitude
    path="generate-support-reply",  # Add a path to identify this prompt in Latitude
)
def generate_support_reply(input: str) -> str:
    # Your regular LLM-powered feature code here
    client = Groq()
    completion = client.chat.completions.create(
        model="llama-3.3-70b-versatile",
        messages=[{"role": "user", "content": input}],
    )

    # You can return anything you want — the value is passed through unchanged
    return completion.choices[0].message.content
Using context manager
from groq import Groq
from telemetry import telemetry

def generate_support_reply(input: str) -> str:
    with telemetry.capture(
        project_id=123,  # The ID of your project in Latitude
        path="generate-support-reply",  # Add a path to identify this prompt in Latitude
    ):
        # Your regular LLM-powered feature code here
        client = Groq()
        completion = client.chat.completions.create(
            model="llama-3.3-70b-versatile",
            messages=[{"role": "user", "content": input}],
        )

        # You can return anything you want — the value is passed through unchanged
        return completion.choices[0].message.content
The path:
  • Identifies the prompt in Latitude
  • Can be new or existing
  • Should not contain spaces or special characters (use letters, numbers, - _ / .)

​
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 Groq call appears as a child span under the captured prompt execution, giving you a full, end-to-end view of what happened.

​
That’s it

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