Check Python API Docs | dltHub

Check is a payroll platform API that handles taxes, money movement, and tax document generation for building payroll products. The REST API base URL is https://api.checkhq.com and all requests require a Bearer API key in the Authorization header.

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 Check data in under 10 minutes.

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What data can I load from Check?

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

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How do I authenticate with the Check API?

Check uses API keys issued from the Console. The key is sent as an 'Authorization: Bearer <API_KEY>' header on every request.

1. Get your credentials

1. Sign in to the Check Console at https://app.checkhq.com (or contact Check sales if you don't have an account).
2. Navigate to the Developers or API Keys section.
3. Create a new API key or request one, then copy the generated key value.
4. Use this key as a Bearer token in the Authorization header for all API calls.

2. Add them to .dlt/secrets.toml

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[sources.check_source]
api_key = "your_check_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 Check 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 check_pipeline.py

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

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Pipeline check_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset check_data
The duckdb destination used duckdb:/check.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 check_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 companies and employees from the Check 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 check_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.checkhq.com",
            "auth": {
                "type": "bearer",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "companies", "endpoint": {"path": "companies"}},
            {"name": "employees", "endpoint": {"path": "employees"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="check_pipeline",
        destination="duckdb",
        dataset_name="check_data",
    )
    load_info = pipeline.run(check_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("check_pipeline").dataset()
sessions_df = data.companies.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM check_data.companies LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("check_pipeline").dataset()
data.companies.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 Check 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

Ensure the Authorization header is present and formatted exactly as: Authorization: Bearer <API_KEY>. Requests made without a valid API key will be rejected; review Console API Logs for request details.

Rate limits and request logs

Check Console surfaces API Logs for requests made with an API key and retains logs for the last 14 days. If you experience rate limiting, inspect response status codes and Console logs for details; contact Check support for quota increases.

Validation and structured errors

When submitting invalid payloads (for example to External Payroll endpoints), Check returns structured validation_error responses containing input_errors with field_path values to pinpoint failures. Handle 4xx responses by surfacing input_errors to callers.

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