ADP Workforce Now Python API Docs | dltHub

ADP Workforce Now is a cloud-based human capital management suite providing APIs for workforce, payroll, and HR data integration through the ADP developer ecosystem. The REST API base URL is https://api.adp.com and all requests require a Bearer token obtained via OAuth 2.0 authentication.

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 ADP Workforce Now data in under 10 minutes.

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What data can I load from ADP Workforce Now?

Here are some of the endpoints you can load from ADP Workforce Now:

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How do I authenticate with the ADP Workforce Now API?

Authentication is handled via OAuth 2.0; requests to protected endpoints require an Authorization header with a Bearer token. Additionally, the platform requires mutual TLS (mTLS) with client certificates provided during application registration.

1. Get your credentials

To obtain API credentials, you must first access either the ADP API Central portal (for clients) or the ADP Marketplace Partner Self-Service Tool (for partners). Once logged in, navigate to the Projects section and create a new project. Within your project settings, locate the Development API Credentials tab. On this page, you will find your OAuth Client ID and Client Secret. Additionally, you are required to generate a Mutual SSL certificate (a private .KEY file and a signed .PEM file) within the portal's Certificate tab, as this is mandatory for all API requests to the ADP API Gateway.

2. Add them to .dlt/secrets.toml

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[sources.adp_workforce_now_source]
adp_client_id = "your_client_id_here"
adp_client_secret = "your_client_secret_here"
adp_ssl_cert_path = "/path/to/your_certificate.pem"
adp_ssl_key_path = "/path/to/your_private_key.key"

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 ADP Workforce Now 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 adp_workforce_now_pipeline.py

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

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Pipeline adp_workforce_now_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset adp_workforce_now_data
The duckdb destination used duckdb:/adp_workforce_now.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 adp_workforce_now_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 /hr/v2/workers and /events from the ADP Workforce Now 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 adp_workforce_now_source(credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.adp.com",
            "auth": {"type": "bearer", "token": credentials},
        },
        "resources": [
            {"name": "workers", "endpoint": {"path": "hr/v2/workers", "data_selector": "workers"}},
            {"name": "work_assignments", "endpoint": {"path": "hr/v2/work-assignments", "data_selector": "workAssignments"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="adp_workforce_now_pipeline",
        destination="duckdb",
        dataset_name="adp_workforce_now_data",
    )
    load_info = pipeline.run(adp_workforce_now_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("adp_workforce_now_pipeline").dataset()
sessions_df = data.workers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM adp_workforce_now_data.workers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("adp_workforce_now_pipeline").dataset()
data.workers.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 ADP Workforce Now 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