Zenoti Python API Docs | dltHub

Zenoti is a cloud‑based salon, spa and medspa management platform exposing a REST API to access organizations, centers, employees, customers, appointments, services and related data. The REST API base URL is https://api.zenoti.com and All requests require either an API key (Authorization: "apikey {api_key}") or a Bearer access token (Authorization: "bearer {access_token}")..

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 Zenoti data in under 10 minutes.

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What data can I load from Zenoti?

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

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How do I authenticate with the Zenoti API?

Zenoti supports API‑key authentication (Authorization: "apikey {api_key}") and token‑based authentication where a POST generates an access token used as "bearer {access_token}" in subsequent requests.

1. Get your credentials

1. Sign in to your Zenoti account as an Admin.
2. Go to Admin > Setup > Apps (or Apps in the Admin console).
3. Create a backend app and set the required permissions/roles.
4. After creation, Zenoti displays an Application ID, Secret, and an API Key – copy the API Key.
5. Use this API Key directly in the Authorization header or generate a Bearer token via the Generate Access Token API.

2. Add them to .dlt/secrets.toml

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[sources.zenoti_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 Zenoti 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 zenoti_pipeline.py

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

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Pipeline zenoti_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset zenoti_data
The duckdb destination used duckdb:/zenoti.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 zenoti_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 centers and employees from the Zenoti 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 zenoti_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.zenoti.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "centers", "endpoint": {"path": "reference/list-all-centers", "data_selector": "centers"}},
            {"name": "employees", "endpoint": {"path": "reference/list-all-employees", "data_selector": "employees"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="zenoti_pipeline",
        destination="duckdb",
        dataset_name="zenoti_data",
    )
    load_info = pipeline.run(zenoti_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("zenoti_pipeline").dataset()
sessions_df = data.centers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM zenoti_data.centers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("zenoti_pipeline").dataset()
data.centers.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 Zenoti 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 401/403 responses verify that your Authorization header matches the chosen auth mode exactly: for API keys use "Authorization: apikey {api_key}"; for tokens use "Authorization: bearer {access_token}". Ensure the API key is active and the app has required permissions.

Token expiration and rotation

Access tokens expire; if using token‑based auth detect 401s and refresh by re‑generating an access token. API keys do not expire unless rotated or revoked.

Pagination and large result sets

Many list endpoints return paginated responses; use the documented pagination parameters (page, limit or next link) from the endpoint docs. Fetch additional pages until no more records.

Rate limits and error codes

Zenoti documents standard HTTP response codes and error models. Respect 429 Too Many Requests responses by backing off and retrying after the Retry‑After header. Refer to the Response and Error Codes documentation for exact error models.

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