Rebilly Python API Docs | dltHub

Rebilly is a payment orchestration and subscription management platform exposing a RESTful API for managing organizations, customers, payments, invoices, API keys, KYC documents, billing portals and related resources. The REST API base URL is https://api.rebilly.com/organizations/{organizationId} and all requests require a secret API key provided in the REB-APIKEY header together with the organizationId in the path.

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 Rebilly data in under 10 minutes.

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What data can I load from Rebilly?

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

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How do I authenticate with the Rebilly API?

Use your organization ID in the request path and include your secret key in the REB-APIKEY HTTP header for server‑side requests. Publishable keys are for client‑side tokenization only.

1. Get your credentials

1. Sign in to the Rebilly dashboard (app.rebilly.com). 2) Navigate to Settings → API Keys or Manage API keys. 3) Create a secret API key (type 'secret') and copy the secretKey value. 4) Go to Settings → Organizations to find your Organization ID. 5) Use the Organization ID in the request path and the secret key in the REB-APIKEY header.

2. Add them to .dlt/secrets.toml

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[sources.rebilly_source]
api_key = "your_secret_api_key_here"
organization_id = "your_organization_id_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 Rebilly 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 rebilly_pipeline.py

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

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Pipeline rebilly_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset rebilly_data
The duckdb destination used duckdb:/rebilly.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 rebilly_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 api_keys and billing_portals from the Rebilly 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 rebilly_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.rebilly.com/organizations/{organizationId}",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "api_keys", "endpoint": {"path": "api-keys"}},
            {"name": "billing_portals", "endpoint": {"path": "billing-portals"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="rebilly_pipeline",
        destination="duckdb",
        dataset_name="rebilly_data",
    )
    load_info = pipeline.run(rebilly_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("rebilly_pipeline").dataset()
sessions_df = data.billing_portals.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM rebilly_data.billing_portals LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("rebilly_pipeline").dataset()
data.billing_portals.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 Rebilly 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 a 401 Unauthorized, verify that you are using a secret API key (type secret) in the REB-APIKEY header and that the correct organizationId is present in the request path. Publishable keys cannot be used for server‑side endpoints.

Rate limiting

Rebilly applies organization‑level rate limits. In sandbox the default is 3000 requests per 10 minutes for non‑GET endpoints. Inspect response headers X-RateLimit-Limit, X-RateLimit-Remaining and X-RateLimit-Retry-After (or the pagination headers) to understand remaining quota. A 429 Too Many Requests response includes X-RateLimit-Retry-After with a UTC timestamp indicating when you may retry.

Pagination

List endpoints return pagination information via response headers: Pagination-Total, Pagination-Limit, Pagination-Offset. The body is usually a top‑level JSON array. Use the limit and offset query parameters (or the provided header values) to iterate through pages.

Error format

Rebilly follows RFC 9457 Problem Details for HTTP APIs. Error responses contain standard HTTP status codes (e.g., 400, 401, 403, 404, 422, 429, 500) and a JSON problem‑details object with fields such as type, title, status, detail, and optional extensions. Handle additional members gracefully.

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