KSEF API Python API Docs | dltHub

KSeF API is a REST interface for issuing, sending, receiving and storing structured electronic invoices within Poland's National e-Invoice System (KSeF). The REST API base URL is Test: https://ksefapi.pl/api-test/ Production: https://ksefapi.pl/api/ and All requests require HTTP Basic authentication using an API key ID and 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 KSEF API data in under 10 minutes.

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What data can I load from KSEF API?

Here are some of the endpoints you can load from KSEF API:

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How do I authenticate with the KSEF API API?

Clients must send an Authorization header with the value "Basic <base64(key_id:key)>", where key_id and key are the credentials obtained from the KSeF dashboard.

1. Get your credentials

1. Register an account on the KSeF API site (test registration: https://ksefapi.pl/portal-test/register/).\n2) Log in and open the API Keys / KSeF Tokens tab.\n3) Copy the automatically generated key_id (public) and key (private).\n4) Generate or paste a KSeF Client Token from the official KSeF system.\n5) Use key_id:key encoded in Base64 as the Basic auth header.

2. Add them to .dlt/secrets.toml

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[sources.ksef_api_source]
api_key = "your_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 KSEF API 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 ksef_api_pipeline.py

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

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Pipeline ksef_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset ksef_api_data
The duckdb destination used duckdb:/ksef_api.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 ksef_api_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 invoice_generate and invoice_download from the KSEF API 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 ksef_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Test: https://ksefapi.pl/api-test/  Production: https://ksefapi.pl/api/",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "invoice_generate", "endpoint": {"path": "invoice/generate"}},
            {"name": "invoice_download", "endpoint": {"path": "invoice/download"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ksef_api_pipeline",
        destination="duckdb",
        dataset_name="ksef_api_data",
    )
    load_info = pipeline.run(ksef_api_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("ksef_api_pipeline").dataset()
sessions_df = data.invoice_download.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM ksef_api_data.invoice_download LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("ksef_api_pipeline").dataset()
data.invoice_download.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 KSEF API 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 401/403 responses verify that the Authorization header contains Basic <base64(key_id:key)>. Ensure you are using the token generated for the correct environment (test vs production) and that the KSeF Client Token is set if required.

Rate limits and quotas

Production accounts are subject to package‑based limits. Check the /package or account endpoints in the API docs or your account dashboard for limits. Exceeding limits returns appropriate HTTP 4xx/429 responses.

Pagination and list responses

Some GET list endpoints may use pagination; consult the OpenAPI documentation (swagger) for parameters (page, size, nextLink) and the exact JSON key containing the records array.

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