HR Partner Python API Docs | dltHub

HR Partner is a cloud‑based HR management platform providing a REST API for accessing employee, company and other HR data. The REST API base URL is https://api.hrpartner.io and All requests require an x-api-key header containing the API token..

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 HR Partner data in under 10 minutes.

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What data can I load from HR Partner?

Here are some of the endpoints you can load from HR Partner:

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How do I authenticate with the HR Partner API?

Include an HTTP header x-api-key: <your_api_token> with every request. The token is generated in the HR Partner UI.

1. Get your credentials

1. Log in to the HR Partner web UI.
2. Navigate to Setup → Configure → Integrations.
3. Locate the API Token field – the current token is shown there.
4. Click Reset API Token if you need a new token, confirm the action, and copy the generated token.
5. Store the token securely; it will be used as the value of the x-api-key header.

2. Add them to .dlt/secrets.toml

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[sources.hr_partner_source]
api_key = "your_api_token_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 HR Partner 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 hr_partner_pipeline.py

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

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Pipeline hr_partner_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset hr_partner_data
The duckdb destination used duckdb:/hr_partner.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 hr_partner_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 employees and positions from the HR Partner 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 hr_partner_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.hrpartner.io",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "employees", "endpoint": {"path": "employees"}},
            {"name": "positions", "endpoint": {"path": "positions"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="hr_partner_pipeline",
        destination="duckdb",
        dataset_name="hr_partner_data",
    )
    load_info = pipeline.run(hr_partner_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("hr_partner_pipeline").dataset()
sessions_df = data.employees.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM hr_partner_data.employees LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("hr_partner_pipeline").dataset()
data.employees.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 HR Partner 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

• Symptom: HTTP 401 Unauthorized.
• Cause: Missing, misspelled, or invalid x-api-key header.
• Fix: Verify that the API token from the HR Partner UI is correct and included exactly as x-api-key: <token>.

Rate limiting

• Symptom: HTTP 429 Too Many Requests.
• Cause: Exceeding the limit of 60 GET/POST requests per second (burst up to 900 per minute).
• Fix: Implement client‑side throttling or exponential back‑off; keep requests below the documented thresholds.

Pagination / large result sets

• Symptom: Very long response times or timeouts on large endpoints.
• Cause: The API does not implement paging; it returns full collections as top‑level arrays.
• Fix: Request only the necessary endpoints, filter server‑side if possible, and increase client timeout if needed.

General timeouts

• Symptom: HTTP 504 Gateway Timeout or client‑side timeout after ~60 seconds.
• Cause: Large payloads or slow backend processing.
• Fix: Reduce request scope, split into multiple calls where feasible, and adjust timeout settings on the client.

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