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The Splunk Agent Observability SDK provides 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 Splunk Agent Observability SDK and initialize your API keys by following the steps below.

Python SDK

The Splunk Agent Observability Python SDK on PyPI.

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Installation

If you want to use the OpenAI wrapper in Python, you need to install with the optional OpenAI dependencies.

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Initialization and authentication

If you are using an on-premises, standalone, or custom deployment, you need a Splunk Agent Observability API key set as an environment variable called SPLUNK_AO_API_KEY. The Splunk Agent Observability 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, Agent Stream, and URL that Splunk Agent Observability should use.
If you are using the free version of Splunk Agent Observability, there is no need to set the SPLUNK_AO_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.

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Logging

The Splunk Agent Observability SDKs allow you to log all prompts, responses, and statistics around your LLM usage. There are three main ways to log your application:
  1. Use a third-party integration - use wrappers that integrate with common SDKs to automatically log LLM calls or agentic workflows.
  2. Use a decorator - by decorating a function that calls an LLM with the @log decorator or log wrapper, the Splunk Agent Observability SDK logs all AI prompts within.
  3. Directly using the SplunkAOLogger class - For more control over your logging, you can use the SplunkAOLogger 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.

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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.

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Next steps

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Logging with the SDKs

Learn how to log experiments

Learn how to run experiments with multiple data points using datasets and prompt templates

Splunk Agent Observability logger

Log with full control over sessions, traces, and spans using the Splunk Agent Observability logger.

Log decorator

Quickly add logging to your code with the log decorator and wrapper.

Splunk Agent Observability context

Manage logging using the Splunk Agent Observability context manager.

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How-to guides

Log Using the OpenAI Wrapper

Learn how to integrate and use OpenAI’s API with Splunk Agent Observability’s wrapper client.
Python

Log Using the @log Decorator

Learn how to use the Splunk Agent Observability @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

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SDK reference

Python SDK Reference

The Splunk Agent Observability Python SDK reference.