Overview
An overview of the Galileo SDKs.
The Galileo SDKs provide a comprehensive set of tools for logging, evaluating, and experimenting with LLM applications. Regardless of how you go about logging your AI application, you will still need to install the Galileo SDK and initialize your API keys by following the steps below.
Installation
If you want to use the OpenAI wrapper in Python, you need to install with the optional OpenAI dependencies.
Initialization and Authentication
You need a Galileo API key set as an environment variable called GALILEO_API_KEY
. The Galileo SDK will automatically pick this up from the environment variable at run time.
You can also optionally set the following environment variables to define the project, log stream, and console URL that Galileo should use.
Environment variable | Description |
---|---|
GALILEO_PROJECT | The Galileo project to log to. If this is not set, you will need to pass the project name in code. |
GALILEO_LOG_STREAM | The default log stream to log to. If this is not set, you will need to pass the log stream name in code. |
GALILEO_CONSOLE_URL | For custom Galileo deployments only, set this to the URL of your Galileo console to log to. If this is not set, it will default to the hosted Galileo version at app.galileo.ai. |
If you are using the free version of Galileo, there is no need to set the GALILEO_CONSOLE_URL
environment variable.
When developing your application, you should use a .env
file. Create or update a .env
file with the following values as required:
You can then load the environment variables from this file:
For Python, you will need to install python-dotenv
if you haven’t already.
GALILEO_CONSOLE_URL
to your own installation.Logging
The Galileo SDKs allow you to log all prompts, responses, and statistics around your LLM usage. There are three main ways to log your application:
- Use a third-party integration - use wrappers that integrate with common SDKs to automatically log LLM calls or agentic workflows.
- Use a decorator - by decorating a function that calls an LLM with the
@log
decorator orlog
wrapper, the Galileo SDK logs all AI prompts within. - Directly using the
GalileoLogger
class - For more control over your logging, you can use theGalileoLogger
directly. This allows you to manually create sessions, start traces, and log spans. This can be mixed with the other methods, for example accessing the logger directly inside a decorated function call to manually add spans.
Log Experiments
Experiments are logged automatically when they are run, but you can use these same SDK concepts inside the code being run by your experiment for greater control and additional logging. This allows you to not only create distinct experiments, such as in notebooks, but to also add experiments to your production application code.
See our run experiments with code documentation for more details.
Next Steps
Logging with the SDKs
Learn how to log experiments
Learn how to run experiments with multiple data points using datasets and prompt templates
Galileo logger
Log with full control over sessions, traces, and spans using the Galileo logger.
Log decorator
Quickly add logging to your code with the log decorator and wrapper.
Galileo context
Manage logging using the Galileo context manager.
How-to guides
Log Using the OpenAI Wrapper
Learn how to integrate and use OpenAI’s API with Galileo’s wrapper client.
Python
Log Using the @log Decorator
Learn how to use the Galileo @log decorator to log functions to traces
Python
Create Traces and Spans
Learn how to create log traces and spans manually in your AI apps
Python