Property Management System Python API Docs | dltHub

Supergood Property Management API is a gateway that normalizes property management system (PMS) data (properties, units/rooms, tenants, GL accounts, occupancy, settings) across multiple PMS platforms so integrations can pull consistent JSON payloads. The REST API base URL is https://api.supergood.ai/integrations/{integration_id} and Username/password (or managed service credentials) with MFA; API returns an auth token used as Bearer for subsequent 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 Property Management System data in under 10 minutes.

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What data can I load from Property Management System?

Here are some of the endpoints you can load from Property Management System:

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How do I authenticate with the Property Management System API?

Authenticate by POSTing JSON credentials to /integrations/{integration_id}/auth (body: domain, email, password). Response contains {"authToken":"..."}. Use Authorization: Bearer {token} header on subsequent calls.

1. Get your credentials

1. Sign up / log in to Supergood dashboard. 2) Create an integration for your PMS to obtain the integration_id. 3) Under integration details provide platform domain and generate or register service account credentials (email/password) or supply customer credentials. 4) POST domain/email/password to /integrations/{integration_id}/auth to receive authToken. 5) Store returned token and use Bearer auth for sync calls.

2. Add them to .dlt/secrets.toml

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[sources.pms_source_source]
api_key = "sg_auth_token_here"
integration_id = "89e4dffe-4624-4ce9-bcf2-4e8686d157be"

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 Property Management System 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 pms_source_pipeline.py

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

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Pipeline pms_source_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset pms_source_data
The duckdb destination used duckdb:/pms_source.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 pms_source_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 units and tenants from the Property Management System 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 pms_source_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.supergood.ai/integrations/{integration_id}",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "units", "endpoint": {"path": "integrations/{integration_id}/sync (GET with occupancy)", "data_selector": "units"}},
            {"name": "tenants", "endpoint": {"path": "integrations/{integration_id}/sync (POST search body)", "data_selector": "tenants"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pms_source_pipeline",
        destination="duckdb",
        dataset_name="pms_source_data",
    )
    load_info = pipeline.run(pms_source_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("pms_source_pipeline").dataset()
sessions_df = data.units.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM pms_source_data.units LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("pms_source_pipeline").dataset()
data.units.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 Property Management System 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 POST /integrations/{integration_id}/auth returns 4xx, verify domain/email/password and that MFA was handled. Supergood returns {"authToken":"..."} on success. If 401, refresh credentials and request a new token.

Rate limiting and retries

Some provider endpoints (Alexa, Booking) return 429. Implement exponential backoff and respect maxResults limits (Alexa maxResults 50). Supergood states rate limits are optimized per-platform.

Pagination quirks

Alexa Smart Properties uses "paginationContext.nextToken" for subsequent pages. Booking and other PMS APIs often use page/total_results keys (supergood sample returns "total_results" and "page"). For Supergood sync endpoints include page parameters in POST body or nextToken as provided.

Common API errors

• 400 Bad Request — malformed or missing required parameters.
• 401 Unauthorized — missing/expired token or invalid credentials.
• 403 Forbidden — token valid but operation not allowed.
• 404 Not Found — resource not found (wrong integration_id or resource id).
• 429 Too Many Requests — rate limit exceeded; retry with backoff.
• 500/503 Server Error — server-side errors; retry later.

Notes and recommendations

• Exact Supergood base: use https://api.supergood.ai/integrations/{integration_id} and call /auth then /sync. Example responses from docs show gl_accounts under "gl_accounts", tenants under "tenants", occupancy units under "units" and search returns include "total_results" and "page".
• Alexa Smart Properties host varies by country (https://api.amazonalexa.com or https://api.eu.amazonalexa.com etc.); list units endpoint returns records under "results" and paginationContext.nextToken.
• Alfresco REST API base pattern: https://{host}/alfresco/api/-default-/public/alfresco/versions/1/... and many collection endpoints return collections with entries or similar collection wrappers; consult endpoint-specific docs when integrating.

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