Sage HR Python API Docs | dltHub

Sage HR is a cloud HRIS platform exposing a public REST API to read and manage company HR data. The REST API base URL is Requests are sent to your company‑specific subdomain; use the company API host provided in your account (example pattern in docs: https://{your_company}.sagehr or the interactive Apiary console host). See docs for your exact subdomain. and All requests require an API key passed in the X-Auth-Token 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 Sage HR data in under 10 minutes.

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What data can I load from Sage HR?

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

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How do I authenticate with the Sage HR API?

Sage HR uses a per-user API key. Include header X-Auth-Token: <your_api_key> on every request. Admin rights are required to enable API access; if admin privileges are removed the key is invalidated.

1. Get your credentials

1. Sign in to Sage HR as an Admin. 2) Click your name (top‑right) -> Settings -> INTEGRATIONS -> API. 3) Click ENABLE API ACCESS to generate your unique API key. 4) Copy and store the API key securely (it is shown once; re‑enabling API regenerates keys).

2. Add them to .dlt/secrets.toml

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[sources.sage_hr_source]
api_key = "your_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 Sage HR 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 sage_hr_pipeline.py

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

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Pipeline sage_hr_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset sage_hr_data
The duckdb destination used duckdb:/sage_hr.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 sage_hr_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 employee from the Sage HR 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 sage_hr_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Requests are sent to your company‑specific subdomain; use the company API host provided in your account (example pattern in docs: https://{your_company}.sagehr or the interactive Apiary console host). See docs for your exact subdomain.",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "employees", "endpoint": {"path": "employee/employees", "data_selector": "employees"}},
            {"name": "employee", "endpoint": {"path": "employee/employees/{id}"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM sage_hr_data.employees LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("sage_hr_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 Sage HR 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 check that X-Auth-Token header contains the API key for an admin user and that API access is enabled for that account. Admin permission removal invalidates keys.

Rate limits and throttling

The public docs do not publish global rate limits; implement exponential backoff and respect 429 responses. Inspect response headers for any rate‑limit fields returned by your company subdomain.

Pagination

List endpoints use pagination. Pass query params (page, per_page or similar) as shown in the interactive Apiary examples; responses include the list under the resource key (e.g., "employees"). If you need exact paging parameter names, use the Apiary console for your account‑specific API host.

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