Log Using the Log Decorator

Overview

This guide shows you how to log spans to Galileo using the @log decorator in Python, or the log wrapper in TypeScript to log function calls using the async OpenAI SDK as LLM spans. In this guide you will:

Set up a project with Galileo

Create a basic app to call OpenAI
Add logging with the log decorator

Before you start

To complete this how-to, you will need:

Install dependencies

To use Galileo, you need to install some package dependencies, and configure environment variables.

Install Required Dependencies

Install the required dependencies for your app. If you are using Python, create a virtual environment using your preferred method, then install dependencies inside that environment:

pip install "galileo[openai]" python-dotenv

npm install galileo dotenv

Create a .env file, and add the following values

# Your Galileo API key
GALILEO_API_KEY="your-galileo-api-key"

# Your Galileo project name
GALILEO_PROJECT="your-galileo-project-name"

# The name of the Log stream you want to use for logging
GALILEO_LOG_STREAM="your-galileo-log-stream"

# Provide the console url below if you are using a
# custom deployment, and not using the free tier, or app.galileo.ai.
# This will look something like “console.galileo.yourcompany.com”.
# GALILEO_CONSOLE_URL="your-galileo-console-url"

# OpenAI properties
OPENAI_API_KEY="your-openai-api-key"

# Optional. The base URL of your OpenAI deployment.
# Leave this commented out if you are using the default OpenAI API.
# OPENAI_BASE_URL="your-openai-base-url-here"

# Optional. Your OpenAI organization.
# OPENAI_ORGANIZATION="your-openai-organization-here"

This assumes you are using a free Galileo account. If you are using a custom deployment, then you will also need to add the URL of your Galileo Console:

.env

GALILEO_CONSOLE_URL=your-Galileo-console-URL

Create the basic app to call OpenAI

Create a file for your application called app.py or app.ts.

Add the following code to call OpenAI to ask a question

import os
import asyncio
import openai
from dotenv import load_dotenv

load_dotenv()

client = openai.AsyncOpenAI(api_key=os.environ.get("OPENAI_API_KEY"))

async def prompt_open_ai(prompt: str) -> str:
    response = await client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}],
    )
    return response.choices[0].message.content.strip()

async def main():
    prompt = "Explain the following topic succinctly: Newton's First Law"
    response = await prompt_open_ai(prompt)
    print(response)

if __name__ == "__main__":
    asyncio.run(main())

import { OpenAI } from "openai";
import dotenv from "dotenv";
dotenv.config();

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY
});

const prompt = "Explain the following topic succinctly: Newton's First Law";

async function promptOpenAI(prompt: string) {
    const response = await openai.chat.completions.create({
        model: "gpt-4.1-mini",
        messages: [{ content: prompt, role: "user" }],
    });
    return response.choices[0].message.content;
}

const response = await promptOpenAI(prompt);

console.log(response);

If you are using TypeScript, you will also need to configure your code to use ESM. Add the following to your package.json file:

package.json

{
  "type": "module",
  ... // Existing contents
}

Run the app to ensure everything is working

python app.py

npx tsx app.ts

You should see a description of Newton’s first law.

(.venv) ➜  python app.py
Newton's First Law, also known as the Law of Inertia, states that an object
at rest will stay at rest and an object in motion will stay in motion with
the same speed and in the same direction, unless acted upon by an
unbalanced force. In simpler terms, it means that an object will keep doing
what it's currently doing until a force makes it do something different.
This law is fundamental to understanding motion and forces as it pertains
to physics.

Add simple logging with the Galileo log decorator or wrapper

Galileo has a @log decorator in Python, and a log wrapper in TypeScript that logs function calls as spans. If these decorated or wrapped calls are called whilst there is an active trace, they are added to that trace. If there is no active trace, a new one is created for this span. In this guide, you will be adding the decorator or wrapper to log the function that calls OpenAI.

Import the log decorator

At the top of your file, add an import for the log decorator:

from galileo import log

import { log, flush } from "galileo";

Decorate or wrap the function

Update the function definition to include the decorator or wrapper:

@log(span_type="llm", name="OpenAI GPT-4o-mini")
async def prompt_open_ai(prompt: str) -> str:

const promptOpenAI = log({ spanType: "llm" }, async (prompt: string) => {
    const response = await openai.chat.completions.create({
        model: "gpt-4.1-mini",
        messages: [{ content: prompt, role: "user" }],
    });
    return response.choices[0].message.content;
});

This will log the function as an LLM span using the span type parameter. You can read more about these in our span types documentation.This also sets the name of the span to “OpenAI GPT-4o-mini”.

Run the app

python app.py

npx tsx app.ts

When the app runs, the span will be logged automatically, with the input as the prompt, the output as the returned response. The duration will also be logged.

View the logged trace

From the Galileo Console, open the Log stream for your project. You will see a trace with a single span containing the logged function call.Select the trace to see a detailed view:

A trace with an input, OpenAI span, and an output

Select the OpenAI span to see the latency.

The trace with the OpenAI span selected and the latency highlighted in the system metrics pane

Your logging is now set up! You are ready to configure metrics for your project.

Overview

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References

Log Using the Log Decorator

Overview

Before you start

Install dependencies

Create the basic app to call OpenAI

Add simple logging with the Galileo log decorator or wrapper

See also

​Overview

​Before you start

​Install dependencies

​Create the basic app to call OpenAI

​Add simple logging with the Galileo log decorator or wrapper

​See also

Overview

Before you start

Install dependencies

Create the basic app to call OpenAI

Add simple logging with the Galileo log decorator or wrapper

See also