VictorOps Python API Docs | dltHub

VictorOps is an incident management and on‑call orchestration platform (Splunk On‑Call) providing REST APIs to manage incidents, schedules, users, teams and routing keys. The REST API base URL is https://api.victorops.com and API ID and API Key are required for all requests..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading VictorOps data in under 10 minutes.

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What data can I load from VictorOps?

Here are some of the endpoints you can load from VictorOps:

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How do I authenticate with the VictorOps API?

VictorOps requires an API ID and API Key sent via HTTP headers or query parameters; they can be provided as apiId/apiKey options or via the VO_API_ID and VO_API_KEY environment variables. All requests must use HTTPS.

1. Get your credentials

1. Sign in to the VictorOps (Splunk On‑Call) web portal.
2. Go to Integrations → API Keys (or account settings → API credentials).
3. Click Create New API Key and give it a name.
4. Copy the generated API ID and API Key.
5. Store them securely, e.g., as environment variables VO_API_ID and VO_API_KEY or in dlt's secrets.toml.

2. Add them to .dlt/secrets.toml

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[sources.victor_ops_source]
api_id = "your_api_id_here"
api_key = "your_api_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the VictorOps API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python victor_ops_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline victor_ops_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset victor_ops_data
The duckdb destination used duckdb:/victor_ops.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline victor_ops_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads incidents and users from the VictorOps API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def victor_ops_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.victorops.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "incidents", "endpoint": {"path": "api-public/v1/incidents"}},
            {"name": "users", "endpoint": {"path": "api-public/v1/user"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="victor_ops_pipeline",
        destination="duckdb",
        dataset_name="victor_ops_data",
    )
    load_info = pipeline.run(victor_ops_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("victor_ops_pipeline").dataset()
sessions_df = data.incidents.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM victor_ops_data.incidents LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("victor_ops_pipeline").dataset()
data.incidents.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load VictorOps data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If requests return 401 or 403, verify that both the API ID and API Key are correct and that they are being sent in the expected format (headers or query parameters). Ensure you are using HTTPS.

Rate limits and call quotas

VictorOps may limit the total number of API calls per month and enforce per‑endpoint rate limits. When limits are reached, the API returns appropriate error codes; implement exponential backoff or reduce polling frequency.

Pagination and response shape

Some list endpoints paginate or wrap results in an object. The documentation does not always specify the exact JSON root key, so inspect a sample response to determine the correct data selector for dlt.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack