Montonio Python API Docs | dltHub

Montonio is a payments platform that provides APIs (Stargate) to accept and manage online payments, payment methods, payment links and related operations. The REST API base URL is https://stargate.montonio.com/api and all requests require a Bearer JWT (signed with store Secret Key) in the Authorization header.

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 Montonio data in under 10 minutes.

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What data can I load from Montonio?

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

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How do I authenticate with the Montonio API?

Authentication uses short-lived JWT Bearer tokens signed with your store Secret Key. Include the token in the Authorization header as: Authorization: Bearer .

1. Get your credentials

1. Sign into the Montonio Partner System (dashboard). 2) Locate your Store/Access Key and Secret Key (Access Key used as JWT payload, Secret Key to sign JWT). 3) Create a JWT with payload {"accessKey":"YOUR_ACCESS_KEY","exp": } signed with HS256 using your Secret Key. 4) Use the JWT as Bearer token in Authorization header for API requests.

2. Add them to .dlt/secrets.toml

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[sources.montonio_payments_source]
access_key = "YOUR_ACCESS_KEY"
secret_key = "YOUR_SECRET_KEY"

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 Montonio 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 montonio_payments_pipeline.py

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

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Pipeline montonio_payments_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset montonio_payments_data
The duckdb destination used duckdb:/montonio_payments.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 montonio_payments_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 stores_payment_methods and payments from the Montonio 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 montonio_payments_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://stargate.montonio.com/api",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "stores_payment_methods", "endpoint": {"path": "stores/payment-methods", "data_selector": "paymentMethods"}},
            {"name": "payments", "endpoint": {"path": "payments", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="montonio_payments_pipeline",
        destination="duckdb",
        dataset_name="montonio_payments_data",
    )
    load_info = pipeline.run(montonio_payments_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("montonio_payments_pipeline").dataset()
sessions_df = data.stores_payment_methods.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM montonio_payments_data.stores_payment_methods LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("montonio_payments_pipeline").dataset()
data.stores_payment_methods.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 Montonio 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 or response body containing STORE_NOT_FOUND or Unauthorized, verify that your JWT token payload contains correct accessKey and hasn't expired, and that it was signed with the correct Secret Key (HS256). Ensure Authorization header is exactly: Authorization: Bearer .

Rate limits and error responses

The API returns standard HTTP error codes (4xx/5xx). For 4xx errors inspect the JSON message field for error codes. Example returned error body for auth issues: {"statusCode":401,"message":"STORE_NOT_FOUND","error":"Unauthorized"}.

Sandbox vs Production base URLs

Use https://stargate.montonio.com/api for production; sandbox requests in examples use https://sandbox-stargate.montonio.com/api. Ensure you point to the correct host for environment.

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