Klarna Python API Docs | dltHub

Klarna is a payments platform that provides APIs to create payment sessions, authorise payments, and manage orders (order management, captures, refunds) for merchants. The REST API base URL is https://api.klarna.com/ and All API requests use HTTP Basic authentication (merchant credentials tied to environment)..

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 Klarna data in under 10 minutes.

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What data can I load from Klarna?

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

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How do I authenticate with the Klarna API?

Klarna uses HTTP Basic auth where the API username and password (merchant id / shared secret) are sent in the Authorization header (Authorization: Basic base64(username:password)). Use environment‑specific credentials (playground vs live).

1. Get your credentials

1. Sign up or log into Klarna Merchant/Partner Portal.
2. Create or select the merchant account/environment (playground vs production).
3. Navigate to API credentials / developer settings and create API credentials (merchant id and shared secret) for the environment.
4. Copy username (merchant id) and password (shared secret); keep secrets secure.
5. Use playground credentials against playground base URLs; production credentials against live base URLs.

2. Add them to .dlt/secrets.toml

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[sources.klarna_settlements_source]
username = "your_merchant_id_here"
password = "your_shared_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 Klarna 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 klarna_settlements_pipeline.py

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

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Pipeline klarna_settlements_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset klarna_settlements_data
The duckdb destination used duckdb:/klarna_settlements.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 klarna_settlements_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_session and ordermanagement_order from the Klarna 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 klarna_settlements_source(username=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.klarna.com/",
            "auth": {
                "type": "http_basic",
                "password": username,
            },
        },
        "resources": [
            {"name": "payments_session", "endpoint": {"path": "payments/v1/sessions/{session_id}"}},
            {"name": "ordermanagement_order", "endpoint": {"path": "ordermanagement/v1/orders/{order_id}", "data_selector": "order_lines"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="klarna_settlements_pipeline",
        destination="duckdb",
        dataset_name="klarna_settlements_data",
    )
    load_info = pipeline.run(klarna_settlements_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("klarna_settlements_pipeline").dataset()
sessions_df = data.payments_session.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM klarna_settlements_data.payments_session LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("klarna_settlements_pipeline").dataset()
data.payments_session.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 Klarna 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

Klarna APIs require HTTP Basic auth; 401/403 indicate invalid credentials or use of playground credentials on live endpoints. Verify Authorization: Basic base64(username:password), and ensure credentials match the environment.

Rate limits and request limits

Klarna documents request/rate limits per API; excessive requests can result in 429 or throttling. Implement exponential backoff and respect retry policies described in the API docs.

Pagination and list endpoints

Some endpoints return top-level arrays (for example captures list and refunds list) — handle both top-level array responses and responses where arrays are nested (e.g., order_lines inside an order object). Check the specific endpoint response sample to pick the correct data selector (order_lines, captures, refunds, options).

Common API error payload

Error responses include JSON with fields such as correlation_id, error_code and error_messages (array). Examples: { "correlation_id": "...", "error_code": "NOT_ALLOWED", "error_messages": [] } — use correlation_id when contacting support.

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