Payrexx Python API Docs | dltHub

Payrexx is a payment processing platform that provides a REST API to manage payments, payment providers, gateways, terminals and related resources. The REST API base URL is https://api.payrexx.com/v1.0 and All requests require authentication via the X-API-KEY header (API Secret)..

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 Payrexx data in under 10 minutes.

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What data can I load from Payrexx?

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

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How do I authenticate with the Payrexx API?

Payrexx requires the API Secret to be sent in the HTTP header X-API-KEY. An alternative HMAC ApiSignature scheme is also documented for signed requests.

1. Get your credentials

1. Log into your Payrexx admin/merchant dashboard for your instance (example.payrexx.com).
2. Navigate to Developer / API or Settings > API.
3. Generate or view your API Secret (API key) for the instance.
4. Use the instance name (subdomain) where required in requests (query param instance=your_instance) and keep the API Secret private.

2. Add them to .dlt/secrets.toml

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[sources.payrexx_source]
api_key = "your_api_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 Payrexx 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 payrexx_pipeline.py

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

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Pipeline payrexx_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset payrexx_data
The duckdb destination used duckdb:/payrexx.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 payrexx_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 payment_providers and gateway from the Payrexx 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 payrexx_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.payrexx.com/v1.0",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "payment_providers", "endpoint": {"path": "PaymentProvider"}},
            {"name": "gateway", "endpoint": {"path": "Gateway/{id}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="payrexx_pipeline",
        destination="duckdb",
        dataset_name="payrexx_data",
    )
    load_info = pipeline.run(payrexx_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("payrexx_pipeline").dataset()
sessions_df = data.payment_providers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM payrexx_data.payment_providers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("payrexx_pipeline").dataset()
data.payment_providers.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 Payrexx 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 X-API-KEY is missing or invalid the API returns standard HTTP error codes. Verify you are sending X-API-KEY with the API Secret for the correct instance. If using ApiSignature, ensure HMAC SHA256 is calculated over the query parameters (excluding instance) and base64 encoded.

Rate limits

Payrexx enforces a global rate limit of 600 requests per 5 minutes (enforced by AWS WAF). Exceeding this may produce 405 Method Not Allowed followed by 403 Forbidden responses.

Pagination and missing response-key notes

The public docs do not include consistent example JSON payloads for every endpoint. When the documentation omits example response bodies, the exact JSON key containing lists of records cannot be safely inferred — do not assume a uniform key (e.g., data). For production integration, retrieve a small sample response from each target endpoint in your instance and inspect the top‑level JSON to determine the correct data selector for dlt.

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