OnceHub Python API Docs | dltHub

OnceHub is a scheduling and appointment platform providing REST APIs to manage bookings, booking calendars, users, teams, contacts, webhooks and related scheduling resources. The REST API base URL is https://api.oncehub.com/v2 and All requests require an API key sent in the API-Key request header..

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 OnceHub data in under 10 minutes.

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What data can I load from OnceHub?

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

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How do I authenticate with the OnceHub API?

OnceHub uses an API key; include the key in the API-Key header (HTTPS only). Use the /test endpoint to validate the key.

1. Get your credentials

1. Log in to your OnceHub account.
2. Click the gear icon (top-right) and select Account Integrations.
3. Open the APIs & Webhooks tile (APIs Integrations section).
4. Copy the API key displayed at the top or click Regenerate to issue a new key.

2. Add them to .dlt/secrets.toml

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[sources.once_hub_source]
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 OnceHub 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 once_hub_pipeline.py

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

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Pipeline once_hub_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset once_hub_data
The duckdb destination used duckdb:/once_hub.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 once_hub_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 booking_calendars and bookings from the OnceHub 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 once_hub_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.oncehub.com/v2",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "booking_calendars", "endpoint": {"path": "booking-calendars", "data_selector": "data"}},
            {"name": "bookings", "endpoint": {"path": "bookings", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="once_hub_pipeline",
        destination="duckdb",
        dataset_name="once_hub_data",
    )
    load_info = pipeline.run(once_hub_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("once_hub_pipeline").dataset()
sessions_df = data.booking_calendars.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM once_hub_data.booking_calendars LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("once_hub_pipeline").dataset()
data.booking_calendars.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 OnceHub 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, confirm the API-Key header is present, the key is correct, and you are calling the v2 server over HTTPS. Use GET /test to validate the key.

Rate limits

The API may return 429 Too Many Requests when rate limits are exceeded. Implement exponential backoff and retries. Monitor response headers for rate-limit hints.

Pagination and list envelopes

List endpoints return a consistent ListResponseBase envelope. Records are provided under the "data" key. When paginated, follow the pagination fields supplied in the envelope (check response for paging metadata) and request subsequent pages using the documented query parameters.

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