Appointedd Python API Docs | dltHub

Appointedd is a booking and scheduling platform that provides a RESTful JSON API to query and manage accounts, bookings, customers, services, staff, locations and related resources. The REST API base URL is https://json-api.appointedd.com/v2 and OAuth2 client credentials (create access token) and then use Bearer token for 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 Appointedd data in under 10 minutes.

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What data can I load from Appointedd?

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

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How do I authenticate with the Appointedd API?

Create an OAuth client in the Appointedd dashboard to obtain a client_id and client_secret. Exchange them for an access token via POST https://json-api.appointedd.com/v2/oauth/tokens (grant_type=client_credentials). Include the returned access token in requests as Authorization: Bearer <access_token>.

1. Get your credentials

1. In the Appointedd dashboard go to Management -> Settings -> API (or the OAuth clients page). 2) Click Create New API (or Create New API Client). 3) Name the client, set permissions (All or Specific accounts) and create. 4) Copy and securely store the client_id and client_secret (the secret is shown only once). 5) Exchange client_id and client_secret for an access token via POST to /v2/oauth/tokens.

2. Add them to .dlt/secrets.toml

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[sources.appointedd_source]
client_id = "YOUR_CLIENT_ID"
client_secret = "YOUR_CLIENT_SECRET"

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 Appointedd 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 appointedd_pipeline.py

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

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Pipeline appointedd_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset appointedd_data
The duckdb destination used duckdb:/appointedd.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 appointedd_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 bookings and customers from the Appointedd 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 appointedd_source(client_credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://json-api.appointedd.com/v2",
            "auth": {
                "type": "bearer",
                "access_token": client_credentials,
            },
        },
        "resources": [
            {"name": "bookings", "endpoint": {"path": "v2/bookings", "data_selector": "data"}},
            {"name": "customers", "endpoint": {"path": "v2/customers", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="appointedd_pipeline",
        destination="duckdb",
        dataset_name="appointedd_data",
    )
    load_info = pipeline.run(appointedd_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("appointedd_pipeline").dataset()
sessions_df = data.bookings.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM appointedd_data.bookings LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("appointedd_pipeline").dataset()
data.bookings.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 Appointedd 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: ensure you created an OAuth client, exchanged client_id and client_secret for an access token at /v2/oauth/tokens, and include Authorization: Bearer <access_token>. Tokens expire (default 1 hour) so refresh by requesting a new token when you receive 401.

Rate limiting

Appointedd may enforce rate limits; if you receive 429 Too Many Requests, implement exponential backoff and retry after the Retry-After header, and reduce request concurrency.

Pagination

Most collection GET endpoints follow JSON:API and return paginated results in 'data' with pagination links in 'links' and meta in 'meta'. Use page[size] and page[number] or provided cursor params as documented per‑endpoint.

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