Acuity-scheduling Python API Docs | dltHub

Acuity Scheduling is an online appointment scheduling platform that provides a REST API to manage appointment types, availability, clients, appointments and calendars. The REST API base URL is https://acuityscheduling.com/api/v1 and All requests use HTTP Basic auth with your Acuity User ID as username and API Key as password..

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 Acuity-scheduling data in under 10 minutes.

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What data can I load from Acuity-scheduling?

Here are some of the endpoints you can load from Acuity-scheduling:

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How do I authenticate with the Acuity-scheduling API?

Authentication is HTTP Basic. Provide the Acuity User ID and API Key in the Authorization header (Basic base64(userId:apiKey)). Example header: Authorization: Basic BASE64_ENCODED("USER_ID:API_KEY").

1. Get your credentials

1. Sign into your Acuity account. 2) Go to Settings → Integrations → API (or visit the API key page at https://secure.acuityscheduling.com/app.php?key=api&action=settings). 3) Copy your User ID and API Key. 4) Use them as HTTP Basic credentials in API calls.

2. Add them to .dlt/secrets.toml

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[sources.acuity_scheduling_source]
api_key = "YOUR_API_KEY"

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 Acuity-scheduling 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 acuity_scheduling_pipeline.py

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

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Pipeline acuity_scheduling_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset acuity_scheduling_data
The duckdb destination used duckdb:/acuity_scheduling.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 acuity_scheduling_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 appointments and availability_times from the Acuity-scheduling 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 acuity_scheduling_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://acuityscheduling.com/api/v1",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "appointments", "endpoint": {"path": "appointments"}},
            {"name": "availability_times", "endpoint": {"path": "availability/times", "data_selector": "times"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="acuity_scheduling_pipeline",
        destination="duckdb",
        dataset_name="acuity_scheduling_data",
    )
    load_info = pipeline.run(acuity_scheduling_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("acuity_scheduling_pipeline").dataset()
sessions_df = data.appointments.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM acuity_scheduling_data.appointments LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("acuity_scheduling_pipeline").dataset()
data.appointments.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 Acuity-scheduling 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 you receive 401 Unauthorized, verify you are using HTTP Basic auth with the correct User ID (username) and API Key (password). Ensure the Authorization header is Basic base64(USER_ID:API_KEY).

Rate limits

The public docs do not specify strict rate limits; if you observe 429 responses, implement exponential backoff and retry. Contact Acuity support for account-specific limits.

Pagination and data selectors

Many list endpoints return objects with a top‑level key containing arrays (for example /availability/dates returns a "dates" array; /availability/times returns "times"; /clients returns "clients"). Some endpoints (appointments, appointment-types) may return top‑level arrays — inspect the response and use the exact key from the docs/examples.

Common API errors: 401 Unauthorized (bad credentials), 429 Too Many Requests (rate limiting), 400 Bad Request (invalid parameters), 404 Not Found (invalid resource id).

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