Planday Python API Docs | dltHub

Planday is an employee management platform offering a REST API for workforce data like HR, scheduling, and payroll operations. The REST API base URL is https://openapi.planday.com and all requests require an OAuth2 Bearer token and a specific X-ClientId 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 Planday data in under 10 minutes.

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What data can I load from Planday?

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

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How do I authenticate with the Planday API?

Authentication uses OAuth2 Bearer tokens obtained via authorization code flow. All requests require an 'Authorization: Bearer <access_token>' header and a custom 'X-ClientId: <client_id>' header.

1. Get your credentials

1. Log in to your Planday portal as an Administrator (e.g., https://{yourcompany}.planday.com/).\n2. Navigate to Settings in the top bar.\n3. In the left sidebar, under the Integrations section, select API Access.\n4. Click the Create app button to register your application.\n5. Select the required scopes for your integration. (Note: You cannot add scopes after creation, so consider selecting all necessary scopes initially.)\n6. Click Authorize on your newly created application tile to generate the application credentials.\n7. Retrieve the Client ID (Application ID) and the Token Secret displayed on the application tile.

2. Add them to .dlt/secrets.toml

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[sources.planday_source]
client_id = "REPLACE_ME"

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 harness:

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dlthub 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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dlthub 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 Planday 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 planday_pipeline.py

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

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Pipeline planday_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset planday_data
The duckdb destination used duckdb:/planday.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 planday_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 https://id.planday.com/connect/authorize and https://id.planday.com/connect/token from the Planday 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 planday_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://openapi.planday.com",
            "auth": {"type": "bearer", "token": client_id},
        },
        "resources": [
            {"name": "departments", "endpoint": {"path": "hr/v1/Departments"}},
            {"name": "shifts", "endpoint": {"path": "schedule/v1/Shifts"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="planday_pipeline",
        destination="duckdb",
        dataset_name="planday_data",
    )
    load_info = pipeline.run(planday_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("planday_pipeline").dataset()
sessions_df = data.departments.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM planday_data.departments LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("planday_pipeline").dataset()
data.departments.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 Planday 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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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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dlthub ai toolkit data-exploration install
dlthub ai toolkit dlthub-platform 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