Quickbooks time Python API Docs | dltHub

QuickBooks Time is a REST API for time‑tracking (formerly TSheets) allowing management and retrieval of users, timesheets, jobcodes, clients, groups, and related time‑tracking data. The REST API base URL is https://rest.tsheets.com/api/v1 and All requests require an OAuth 2.0 access token presented as a Bearer token 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 Quickbooks time data in under 10 minutes.

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What data can I load from Quickbooks time?

Here are some of the endpoints you can load from Quickbooks time:

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How do I authenticate with the Quickbooks time API?

The API uses OAuth 2.0. Include header Authorization: Bearer <ACCESS_TOKEN> on every request. Tokens can be obtained via the /authorize OAuth flow or created via the QuickBooks Time web UI API Add‑on for development tokens.

1. Get your credentials

1. Enable the API Add‑On in your QuickBooks Time account (Feature Add‑ons → API → Add new application) or register an OAuth app in the developer portal.
2. Obtain OAuth client_id and client_secret from the Add‑On or developer app settings.
3. Direct users to the /authorize endpoint with response_type=code, client_id and redirect_uri to receive an authorization code.
4. Exchange the authorization code at the token endpoint to receive an access token and refresh token.
5. Store tokens securely and refresh using the refresh token before expiry.

2. Add them to .dlt/secrets.toml

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[sources.quickbooks_time_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
access_token = "your_access_token_here"
refresh_token = "your_refresh_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.

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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 Quickbooks time 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 quickbooks_time_pipeline.py

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

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Pipeline quickbooks_time_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset quickbooks_time_data
The duckdb destination used duckdb:/quickbooks_time.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 quickbooks_time_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 users and timesheets from the Quickbooks time 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 quickbooks_time_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://rest.tsheets.com/api/v1",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "users", "data_selector": "results.users"}},
            {"name": "timesheets", "endpoint": {"path": "timesheets", "data_selector": "results.timesheets"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="quickbooks_time_pipeline",
        destination="duckdb",
        dataset_name="quickbooks_time_data",
    )
    load_info = pipeline.run(quickbooks_time_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("quickbooks_time_pipeline").dataset()
sessions_df = data.timesheets.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM quickbooks_time_data.timesheets LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("quickbooks_time_pipeline").dataset()
data.timesheets.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 Quickbooks time 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 Unauthorized, verify the Authorization header is present and the token is not expired. Obtain a new access token via the OAuth refresh flow or re‑run the authorization flow.

Rate limiting (429)

The API limits requests (documented as 300 calls per 5‑minute window). On 429 responses, back off and retry after the window expires; implement exponential backoff.

Pagination and response structure

Most GET responses wrap data under a top‑level "results" object containing a plural key for the resource (e.g., results.users). Responses include pagination fields (page, per_page, total) — use query params (page, per_page, since, start_date, end_date) as supported.

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