Deputy Python API Docs | dltHub

Deputy is an API‑first workforce management platform exposing scheduling, timesheets, employees, locations, leave and related resources via a JSON REST API. The REST API base URL is https://{install}.{geo}.deputy.com/api/v1 and all requests require a Bearer token (permanent token or OAuth 2.0) in the Authorization 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 Deputy data in under 10 minutes.

---

What data can I load from Deputy?

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

---

How do I authenticate with the Deputy API?

Authentication is via OAuth 2.0 or a permanent token. Supply the token in the header: Authorization: Bearer .

1. Get your credentials

1. Sign in to your Deputy install as an admin/developer. 2) For quick dev use create a Permanent Token (see "Using a Permanent Token" in Deputy docs) or register an OAuth2 app (see "Using OAuth 2.0"). 3) For OAuth2: register the app, obtain client_id/client_secret and perform the standard OAuth2 flow to exchange for an access token. 4) For a Permanent Token: generate a token in your account/API registration area and copy it for use in the Authorization header.

2. Add them to .dlt/secrets.toml

Copy
[sources.deputy_source]
# place inside [sources.deputy_source]
token = "your_permanent_token_or_oauth_access_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.

---

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 Deputy 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 deputy_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline deputy_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 resource/Employee and resource/Timesheet from the Deputy 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 deputy_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{install}.{geo}.deputy.com/api/v1",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "employee", "endpoint": {"path": "resource/Employee"}},
            {"name": "timesheet", "endpoint": {"path": "resource/Timesheet"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="deputy_pipeline",
        destination="duckdb",
        dataset_name="deputy_data",
    )
    load_info = pipeline.run(deputy_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("deputy_pipeline").dataset()
sessions_df = data.employee.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM deputy_data.employee LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("deputy_pipeline").dataset()
data.employee.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 Deputy 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 401/403 responses, verify the Authorization header: Authorization: Bearer <token>. Permanent tokens must be valid for the installation (tokens are scoped to an install subdomain). For OAuth2 flows confirm client_id/client_secret and redirect URI; refresh or re‑authorize if the access token expired.

Pagination and max page size

List responses are paginated with a maximum of 500 records per response. If your result set exceeds the max, use the API's query parameters (limit/skip or paging parameters shown in specific endpoint docs) to page through results.

Rate limits and 429 responses

Deputy enforces rate limiting; if you receive 429 Too Many Requests back off and retry after an exponential backoff. Check response headers for any retry‑after information.

Resource‑specific quirks

CustomFieldData returns a top‑level JSON array for queries. Many resource list endpoints return a top‑level array of objects — do not assume a wrapped data key.

Common error format

Errors are returned as standard HTTP statuses (4xx/5xx) with JSON error bodies. Inspect the response body for details. A 403 indicates missing permissions.

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