HoorayHR Python API Docs | dltHub

HoorayHR is an HRIS platform providing employee profiles, time tracking, leave and absence management, contracts and related HR data via a REST API. The REST API base URL is https://api.hoorayhr.io and all requests require a Bearer token (OAuth2 access token or personal API key).

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 HoorayHR data in under 10 minutes.

---

What data can I load from HoorayHR?

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

---

How do I authenticate with the HoorayHR API?

HoorayHR supports OAuth2 (authorization code flow) and Personal API Keys. Include the credential in the Authorization header as: Authorization: Bearer <token_or_api_key>.

1. Get your credentials

1. For Personal API Keys (admins): In the HoorayHR web app go to Settings → API Keys (https://app.hoorayhr.io/settings/api-keys), click New API Key, set name/scopes/expiration, and copy the generated key (prefixed with pk_) shown once.
2. For OAuth2 (partners): Request a client_id and client_secret from HoorayHR (support@hoorayhr.io). Redirect users to https://app.hoorayhr.io/oauth/authorize to obtain an authorization code, then exchange the code at https://api.hoorayhr.io/oauth/token for an access token.

2. Add them to .dlt/secrets.toml

Copy
[sources.hooray_hr_source]
api_key = "pk_your_personal_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 HoorayHR 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:

Copy
python hooray_hr_pipeline.py

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

Copy
Pipeline hooray_hr_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset hooray_hr_data
The duckdb destination used duckdb:/hooray_hr.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline hooray_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.

---

Python pipeline example

This example loads users and time_tracking from the HoorayHR API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def hooray_hr_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.hoorayhr.io",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "users", "data_selector": "data"}},
            {"name": "time_tracking", "endpoint": {"path": "time-tracking", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="hooray_hr_pipeline",
        destination="duckdb",
        dataset_name="hooray_hr_data",
    )
    load_info = pipeline.run(hooray_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.

---

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):

Copy
import dlt

data = dlt.pipeline("hooray_hr_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM hooray_hr_data.users LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("hooray_hr_pipeline").dataset()
data.users.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load HoorayHR 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.

---

Troubleshooting

Authentication failures

If you receive 401 Unauthorized, verify your Authorization header is: Authorization: Bearer <token>. For Personal API Keys ensure the key starts with pk_ and has not expired. For OAuth ensure the access_token is valid and not expired.

Inactive company or overdue payments

When a company is inactive or has overdue payments the API returns 403 Forbidden with a body like { "code":403, "data": { "errorCode": "INACTIVE_COMPANY" } } or { "code":403, "data": { "errorCode": "PAYMENT_OVERDUE" } }. Reactivate the company or resolve payments.

Pagination and filtering

Most GET endpoints support filtering operators ($in, $gt, $gte, $lt, $lte) via query parameters (e.g. ?createdAt[$gte]=2023-01-01). Use $in for multiple values. List responses are wrapped in the data key; include paging parameters if supported by the endpoint.

Rate limits and retries

The public docs do not document explicit rate limits — implement exponential backoff and handle 429 responses. On 429 Too Many Requests, retry after a backoff; check response headers for Retry-After if present.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

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.

Copy
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