Toggl Track Python API Docs | dltHub

Toggl Track is a time‑tracking SaaS platform that provides a REST API for managing time entries, projects, workspaces, and other resources. The REST API base URL is https://api.track.toggl.com/api/v9 and All requests require HTTP Basic authentication using the API token as the username and the string "api_token" as the password..

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 Toggl Track data in under 10 minutes.

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What data can I load from Toggl Track?

Here are some of the endpoints you can load from Toggl Track:

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How do I authenticate with the Toggl Track API?

The API uses HTTP Basic authentication. Include an Authorization: Basic <base64‑encoded api_token:api_token> header on every request.

1. Get your credentials

1. Log into the Toggl Track web application.
2. Click your profile avatar in the top‑right corner and select Profile.
3. Scroll to the API Token section at the bottom of the profile page.
4. Copy the token shown, or click Reset to generate a new one.
5. Store the token securely for use in API requests.

2. Add them to .dlt/secrets.toml

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[sources.toggl_track_source]
api_token = "your_api_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 Toggl Track 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 toggl_track_pipeline.py

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

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Pipeline toggl_track_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset toggl_track_data
The duckdb destination used duckdb:/toggl_track.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 toggl_track_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 me_time_entries and workspaces from the Toggl Track 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 toggl_track_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.track.toggl.com/api/v9",
            "auth": {
                "type": "http_basic",
                "api_token": api_token,
            },
        },
        "resources": [
            {"name": "time_entries", "endpoint": {"path": "me/time_entries"}},
            {"name": "workspaces", "endpoint": {"path": "workspaces"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="toggl_track_pipeline",
        destination="duckdb",
        dataset_name="toggl_track_data",
    )
    load_info = pipeline.run(toggl_track_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("toggl_track_pipeline").dataset()
sessions_df = data.time_entries.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM toggl_track_data.time_entries LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("toggl_track_pipeline").dataset()
data.time_entries.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 Toggl Track 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 the API token is missing or incorrect, the server returns 403 Forbidden. Verify that the token is supplied as the username and the password is the literal string api_token.

Rate limits

When the request rate exceeds the safe window (≈1 request per second), the API responds with 429 Too Many Requests. Back‑off and retry after a short delay.

Quota exhaustion

If the daily quota is exhausted, the API returns 402 Payment Required. Quota information is provided in response headers X‑Toggl‑Quota‑Remaining and X‑Toggl‑Quota‑Resets‑In.

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