Atomic PayLink Python API Docs | dltHub

Atomic PayLink is a REST API platform for creating access tokens, managing user linked accounts, retrieving account/orders/transaction data, and performing payment-method update flows for PayLink Manage and PayLink Switch. The REST API base URL is Production: https://api.atomicfi.com (PayLink: https://api.atomicfi.com/pay-link and PCI flows: https://pci.atomicfi.com or https://sandbox-pci.atomicfi.com for sandbox). Common sandbox base URLs shown in docs: https://sandbox-api.atomicfi.com and https://sandbox-pci.atomicfi.com. and All requests require API Key and Secret headers (server-side); some endpoints accept Access Tokens (x-public-token) for user-scoped access..

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 Atomic PayLink data in under 10 minutes.

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What data can I load from Atomic PayLink?

Here are some of the endpoints you can load from Atomic PayLink:

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How do I authenticate with the Atomic PayLink API?

Atomic authenticates server-to-server requests using x-api-key and x-api-secret headers. For user-scoped flows, an x-public-token (Access Token) may be supplied in the x-public-token header.

1. Get your credentials

1. Log in to the Atomic Console (https://console.atomicfi.com).
2. Go to Settings > Credentials (or /settings/credentials).
3. Create or copy your API Key and API Secret.
4. Keep keys secret and use only from server-side environments.

2. Add them to .dlt/secrets.toml

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[sources.atomic_paylink_source]
api_key = "your_api_key_here"
api_secret = "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 Atomic PayLink 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 atomic_paylink_pipeline.py

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

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Pipeline atomic_paylink_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset atomic_paylink_data
The duckdb destination used duckdb:/atomic_paylink.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 atomic_paylink_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 accounts and access_token from the Atomic PayLink 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 atomic_paylink_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Production: https://api.atomicfi.com (PayLink: https://api.atomicfi.com/pay-link and PCI flows: https://pci.atomicfi.com or https://sandbox-pci.atomicfi.com for sandbox). Common sandbox base URLs shown in docs: https://sandbox-api.atomicfi.com and https://sandbox-pci.atomicfi.com.",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "accounts", "endpoint": {"path": "pay-link/accounts", "data_selector": "data.accounts"}},
            {"name": "orders", "endpoint": {"path": "pay-link/orders", "data_selector": "data.orders"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="atomic_paylink_pipeline",
        destination="duckdb",
        dataset_name="atomic_paylink_data",
    )
    load_info = pipeline.run(atomic_paylink_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("atomic_paylink_pipeline").dataset()
sessions_df = data.accounts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM atomic_paylink_data.accounts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("atomic_paylink_pipeline").dataset()
data.accounts.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 Atomic PayLink 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 x-api-key and x-api-secret headers are present and correct. Ensure keys are used from server-side only. For access-token flows, ensure x-public-token is valid and not expired.

Rate limiting (429)

The API may return 429 Too Many Requests. Implement exponential backoff and retry logic.

Pagination and response wrapping

Many endpoints return results wrapped under a top-level "data" object and then a resource-specific key (e.g. data.accounts, data.orders). Check the specific endpoint docs for the exact list key. Some endpoints are lightweight and intended for polling (task status).

Common API errors: 400 Bad Request, 401 Unauthorized, 404 Not Found, 422 Validation Error, 429 Too Many Requests, 50X Internal Server Error.

(Concise developer notes: verify precise data selector for action-history and recurring-financial-transactions when implementing by inspecting live responses — docs show nested data keys but vary by resource.)

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