Run an Experiment

Experiments allow you to evaluate prompts, models, and your application code, using well-defined inputs, against evaluators of your choice.

Run an experiment with UI

In the Splunk Agent Observability UI, use the main menu to select Overview. In the Experiments section, select Create Experiment to add an experiment to a project.

You can use a sample dataset and sample prompt to create your first experiment. If you don’t already have an integration (e.g. with OpenAI), a “Configure integration” link appears for you to add a valid integration for a prompt.

After successfully creating an experiment, you can view the results from the Experiments page of a Splunk Agent Observability project.

Run an experiment with code

Prerequisite: Configure an LLM integration

To run an experiment using a prompt and a dataset, you need to set up an LLM integration. An integration is also required to evaluate LLM outputs with evaluators.

Navigate to the LLM Integrations page

In the Splunk Agent Observability UI, navigate to the LLM Integrations page by selecting your user profile in the upper-right corner and then selecting Integrations.

Add an integration

Locate the LLM provider you are using (or specify a custom integration), then select the +Add Integration button.

Add settings

Specify settings for your integration (such as an API key), then select Save changes.

Example experiment with code

Below is a step-by-step guide. Jump to the application code.

Install dependencies

Install the Splunk Agent Observability SDK, and the dotenv package using the following command in your terminal:

pip install splunk-ao python-dotenv

Set up your environment variables

Create an .env file in your project folder, and set:

Your Splunk Agent Observability API key, which you can get from the App keys page in the UI
Your Splunk Agent Observability project name

SPLUNK_AO_PROJECT="your-splunk-ao-project-name"
# If you are using an on-premises, standalone, or custom deployment
# provide the API key and URL below
# SPLUNK_AO_API_KEY="your-splunk-ao-api-key"
# SPLUNK_AO_CONSOLE_URL="your-splunk-ao-url"

Create your application code

Create a file called app.py (Python) and add the following code:

import os

from splunk_ao import SplunkAOEvaluators, Message, MessageRole
from splunk_ao.config import SplunkAOConfig
from splunk_ao.datasets import create_dataset, get_dataset
from splunk_ao.experiments import run_experiment
from splunk_ao.prompts import create_prompt, get_prompt
from splunk_ao.resources.models.prompt_run_settings import PromptRunSettings

# Load the environment variables
from dotenv import load_dotenv
load_dotenv()

# Create a prompt template, or load it if it already exists
prompt = get_prompt(name="My Prompt")
if not prompt:
    prompt = create_prompt(
        name="My Prompt",
        template=[
            Message(
                role=MessageRole.system,
                content="""
Splunk Agent Observability is the fastest way to ship reliable apps.
Splunk Agent Observability brings automation and insight to AI evaluations so you can
ship with confidence.
""",
            ),
            Message(role=MessageRole.user, content="{{input}}"),
        ],
    )

# Create a dataset, or load it if it already exists
dataset = get_dataset(name="My Dataset")
if not dataset:
    dataset = create_dataset(
        "My Dataset",
        content=[
            {"input": "What is Splunk Agent Observability?"},
            {"input": "What is Copernicus?"}
        ],
    )

# Run the experiment
experiment = run_experiment(
    experiment_name="My Experiment",
    prompt_template=prompt,
    dataset=dataset,
    metrics=[SplunkAOEvaluators.context_adherence],
    project=os.environ.get("SPLUNK_AO_PROJECT"),
    prompt_settings=PromptRunSettings(model_alias="gpt-5-mini"),
)

# Show Splunk Agent Observability information
config = SplunkAOConfig.get()
prompt_url = f"{config.console_url}prompts/{prompt.id}"
dataset_url = f"{config.console_url}datasets/{dataset.dataset.id}"

print()
print("🚀 SPLUNK-AO LOG INFORMATION:")
print(f"🔗 Prompt     : {prompt_url}")
print(f"🔗 Dataset    : {dataset_url}")
print(f"🔗 Experiment : {experiment['link']}")

This code defaults to using gpt-5-mini. If you want to use a different model, update the model_alias in the prompt settings passed to the call to run experiment.

This code creates a prompt containing a system prompt and user prompt, and the user prompt has a mustache template to inject rows from the dataset. It also creates a dataset.It then uses these to run an experiment, measuring context adherence.If the prompt or dataset already exist, they are loaded instead of being recreated.

Run your application

Run your application using the following command in your terminal:

python app.py

View the results in your terminal

Experiment My Experiment has started and is currently processing.
Results will be available at <your-splunk-ao-url>/project/.../experiments/...

🚀 Splunk Agent Observability LOG INFORMATION:
🔗 Prompt     : <your-splunk-ao-url>/prompts/...
🔗 Dataset    : <your-splunk-ao-url>/datasets/...
🔗 Experiment : <your-splunk-ao-url>/project/.../experiments/...

See the experiment in Splunk Agent Observability

Open the experiment in the Splunk Agent Observability UI using the URL output to your terminal. You will see the logged experiment with 2 rows, one for each entry in the dataset.

The experiment in Splunk Agent Observability with 2 traces

Select a trace to see more details, including an explanation of the evaluator score.

The first trace in the experiment in Splunk Agent Observability

Troubleshooting

I need a Splunk Agent Observability API key: Navigate to your Splunk Agent Observability homepage and sign up. Then head to the API keys page to get a new API key.
What’s my project name ?: The project name was set when you created a new project. If you haven’t created a new project, head to Splunk Agent Observability and select the New Project button.

Next steps

Create a dataset

Learn how to create and manage datasets in Splunk Agent Observability.

Run experiments in playgrounds

Learn about running experiments in the Splunk Agent Observability UI using playgrounds and datasets.

Run experiments with code

Learn how to run experiments in Splunk Agent Observability.

Compare experiments

Learn how to compare experiments in Splunk Agent Observability.

Overview

Get Started

Observability

Evaluators

Signals

AI Assistant

Luna Studio

Experiments

Agent Control

Annotations

Integrations

Security

References

Run an experiment with UI

Run an experiment with code

Prerequisite: Configure an LLM integration

Example experiment with code

Troubleshooting

Next steps

Create a dataset

Run experiments in playgrounds

Run experiments with code

Compare experiments

​Run an experiment with UI

​Run an experiment with code

​Prerequisite: Configure an LLM integration

​Example experiment with code

​Troubleshooting

​Next steps

Create a dataset

Run experiments in playgrounds

Run experiments with code

Compare experiments

Run an experiment with UI

Run an experiment with code

Prerequisite: Configure an LLM integration

Example experiment with code

Troubleshooting

Next steps