Overview

You’ve already sent your first trace. Now go deeper: this guide takes your Python app from that first trace to full instrumentation: integrating Logfire with your logging, adding manual and automatic tracing, and recording metrics.

Once you’ve worked through these steps, you’ll be collecting all the data you need to monitor performance, find and fix bugs, understand behavior, and make data-driven decisions.

Note

If you aren’t familiar with traces and spans, start with the Tracing with Spans page.

What you’ll set up

• Integrate Logfire: Fully integrate Logfire with your logging system and the packages you are using.

• Add Logfire manual tracing: Enhance your tracing data by manually adding custom spans and logs to your code for more targeted data collection.

• Add Logfire auto-tracing: Discover how to use Logfire’s auto-tracing capabilities to automatically instrument your application with minimal code changes.

• Add Logfire metrics: Learn how to create and use metrics to track and measure important aspects of your application’s performance and behavior.

We’ll walk you through each step, introducing relevant features and concepts as we go. While the main focus of this guide is on getting data into Logfire so you can use it in the future, we’ll also provide an introduction to the Logfire Web UI and show you how to interact with the data you’re generating.

Note

For a more comprehensive walkthrough of the Logfire Web UI and its features, you may be interested in our Logfire Web UI Guide.

Let’s get started!