CryptoProcessing Python API Docs | dltHub

CryptoProcessing is a payments platform that provides REST APIs to create and manage crypto invoices, payments, deposits, withdrawals, exchanges and related account resources. The REST API base URL is https://cryptoprocessing.io/api/v1 and All requests require an API key (Token) in request headers; some endpoints support X-processing-key or signature headers for extra verification..

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 CryptoProcessing data in under 10 minutes.

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What data can I load from CryptoProcessing?

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

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How do I authenticate with the CryptoProcessing API?

Authentication is done with an API key provided in the Authorization header as: Authorization: Token <api_key>. Some API surfaces also accept X-processing-key header and additional Sign header (RSA-SHA256 signature) for request signing on sensitive endpoints.

1. Get your credentials

1. Log in to your CryptoProcessing dashboard. 2) Open the API / Integration or API keys page (Integration guide -> Obtaining API keys). 3) Create or copy the API key shown. 4) For signed requests, generate an RSA key pair, send the public key to CryptoProcessing support (per docs) and keep the private key locally to compute Sign header.

2. Add them to .dlt/secrets.toml

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[sources.crypto_processing_source]
api_key = "your_api_key_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 CryptoProcessing 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 crypto_processing_pipeline.py

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

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Pipeline crypto_processing_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset crypto_processing_data
The duckdb destination used duckdb:/crypto_processing.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 crypto_processing_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 invoices and transactions_info from the CryptoProcessing 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 crypto_processing_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://cryptoprocessing.io/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "invoices", "endpoint": {"path": "checkout/stores/:store_id/invoices"}},
            {"name": "transactions_info", "endpoint": {"path": "transactions/info"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="crypto_processing_pipeline",
        destination="duckdb",
        dataset_name="crypto_processing_data",
    )
    load_info = pipeline.run(crypto_processing_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("crypto_processing_pipeline").dataset()
sessions_df = data.transactions_info.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM crypto_processing_data.transactions_info LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("crypto_processing_pipeline").dataset()
data.transactions_info.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 CryptoProcessing 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, verify you are sending the API key in the Authorization header exactly as: Authorization: Token <api_key>. If using X-processing-key or custom signature headers, ensure header names and values match the dashboard‑provided keys and the Sign header is computed with your private RSA key as documented.

Rate limits and Too Many Requests (429)

The API returns 429 when requests are too frequent. Respect rate limits; use exponential backoff and retry after a short delay. Many list endpoints support page and limit parameters to reduce response size.

Pagination quirks

List endpoints use page (starting at 1) and limit (default 25, max 100). Use page=1 as the first page. The API does not always return paginated metadata fields; rely on page/limit params to traverse pages until an empty array or no more items are returned.

Common HTTP errors

400 Bad Request — invalid parameters. 401 Unauthorized — bad API key. 403 Forbidden — not permitted. 404 Not Found — resource missing. 405 Method Not Allowed — wrong HTTP verb. 429 Too Many Requests — rate limit. 5xx — server errors (retry).

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