Supersaas Python API Docs | dltHub

SuperSaaS is an online appointment scheduling platform that provides REST APIs to manage users, appointments, forms, schedules and related information. The REST API base URL is https://www.supersaas.com/api and All requests require an account name and an api_key passed as query parameters or via HTTP Basic Authentication..

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 Supersaas data in under 10 minutes.

---

What data can I load from Supersaas?

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

---

How do I authenticate with the Supersaas API?

Authenticate by including account=YourAccountName and api_key=YourApiKey as URL parameters, or use HTTP Basic Auth with the account name as the username and the api_key as the password.

1. Get your credentials

1. Log in to your SuperSaaS account.
2. Navigate to Account Info / Account Settings.
3. Scroll to the API key section.
4. Click "Generate" (or copy the existing key).
5. Use this API key together with your account name in requests or configure it for HTTP Basic Auth.

2. Add them to .dlt/secrets.toml

Copy
[sources.supersaas_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Supersaas 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:

Copy
python supersaas_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline supersaas_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.

---

Python pipeline example

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

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

@dlt.source
def supersaas_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.supersaas.com/api",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "api/users.json"}},
            {"name": "bookings", "endpoint": {"path": "api/bookings.json"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="supersaas_pipeline",
        destination="duckdb",
        dataset_name="supersaas_data",
    )
    load_info = pipeline.run(supersaas_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.

---

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):

Copy
import dlt

data = dlt.pipeline("supersaas_pipeline").dataset()
sessions_df = data.bookings.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM supersaas_data.bookings LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("supersaas_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.

---

What destinations can I load Supersaas 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.

---

Troubleshooting

Authentication failures

Ensure you include both account and api_key (or use HTTP Basic auth) with every API request. Incorrect credentials return 403 Forbidden; missing resources may return 404 Not Found. For browser‑side flows use the MD5 checksum option as documented to avoid exposing the api_key.

Pagination and limits

List endpoints default to limited page sizes (e.g., users default 100). Use limit and offset query parameters (e.g., ?limit=500&offset=1000) to page through larger result sets. Iterate with increasing offsets until fewer than the requested limit are returned.

Rate limiting and caching

The documentation recommends using If-Modified-Since on repeated calls to the availability (/api/free) endpoint to receive 304 Not Modified when unchanged. For frequent polling consider configuring webhooks to receive real‑time updates.

Common HTTP errors

• 200 OK: successful GET
• 201 Created: successful POST (Location header provides the new resource URL)
• 304 Not Modified: when If-Modified-Since is used and nothing changed
• 403 Forbidden: invalid api_key or unauthorized access
• 404 Not Found: resource or schedule does not exist
• 422 Unprocessable Entity: validation error on create/update (error details in response body)

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

---

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.

Copy
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