Primepay Python API Docs | dltHub

PrimePay is a payments platform and gateway providing REST APIs for card payments, hosted checkout forms, refunds, payouts, reconciliation and reporting. The REST API base URL is https://api-gateway.smartcore.pro and all requests require HTTP Basic auth with merchant credentials 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 Primepay data in under 10 minutes.

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What data can I load from Primepay?

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

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How do I authenticate with the Primepay API?

Each merchant receives a merchantKey and secret; include an Authorization header with Basic base64(merchantKey:secret) and Content-Type: application/json on requests.

1. Get your credentials

1. Contact PrimePay onboarding/partner manager to obtain your merchantKey and secret (credentials are provisioned per merchant). 2) Use those credentials as the HTTP Basic username:password pair (merchantKey:secret). 3) Place them into your secrets.toml under the source section (merchant_key and secret). 4) Test with a sandbox/test base URL provided during onboarding (e.g., api.testcardrest.primenetpay.com or sandbox accounts on api-gateway.smartcore.pro).

2. Add them to .dlt/secrets.toml

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[sources.primepay_source]
merchant_key = "your_merchant_key_here"
secret = "your_secret_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 Primepay 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 primepay_pipeline.py

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

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Pipeline primepay_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset primepay_data
The duckdb destination used duckdb:/primepay.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 primepay_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 payments and check from the Primepay 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 primepay_source(merchant_key, secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api-gateway.smartcore.pro",
            "auth": {
                "type": "http_basic",
                "secret": merchant_key, secret,
            },
        },
        "resources": [
            {"name": "payments", "endpoint": {"path": "initPayment"}},
            {"name": "check", "endpoint": {"path": "check"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="primepay_pipeline",
        destination="duckdb",
        dataset_name="primepay_data",
    )
    load_info = pipeline.run(primepay_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("primepay_pipeline").dataset()
sessions_df = data.payments.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM primepay_data.payments LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("primepay_pipeline").dataset()
data.payments.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 Primepay 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 the Authorization header is present and formed as: Authorization: Basic base64(merchantKey:secret). Use the merchantKey as username and secret as password. Confirm you are using the correct environment (sandbox vs production) and hostname.

Missing callback / fetching final status

PrimePay sends a single callback to your registered callback_url after transaction processing. If your endpoint missed the callback, poll the /check endpoint (POST /check with {"order_id": "..."}) to retrieve final transaction state.

Export task and large‑report quirks

Creating a transaction export returns a task id. Poll GET /aggregation-service/public/transaction/excel/task/{taskId} for status; when ready download with GET .../result which returns an xlsx binary. Handle retries and backoff – do not expect immediate availability.

Common error responses

• 401 UNAUTHORIZED — missing/invalid Authorization header or wrong credentials.
• 400 BAD_REQUEST — invalid request payload (missing required fields such as order_id, amount, card fields).
• 500 INTERNAL_SERVER_ERROR — server‑side failure; retry with backoff and contact PrimePay support if persistent.
• Response objects include fields: status (integer, e.g., 1/2 for success, -1 for failure), errorMessage, cascadeErrors (array of gate errors) and response_code/response_message in callbacks.

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