Clockify Python API Docs | dltHub

Clockify is a time-tracking platform offering REST APIs to programmatically access workspaces, users, projects, time entries and reports. The REST API base URL is https://api.clockify.me/api/v1 and All requests require an X-Api-Key header (or X-Addon-Token for addons)..

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 Clockify data in under 10 minutes.

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What data can I load from Clockify?

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

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How do I authenticate with the Clockify API?

Authentication uses an API key sent in request headers. Include the X-Api-Key header (or X-Addon-Token for addons) with your generated API key.

1. Get your credentials

1. Log into the Clockify web app.
2. Click the profile icon and select Profile Settings.
3. Scroll to the API section.
4. Click Generate to create a new API key or copy the existing key.
5. For sub‑domain or regional workspaces, regenerate the key after any workspace transfer.

2. Add them to .dlt/secrets.toml

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[sources.clockify_source]
api_key = "your_clockify_api_key_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 Clockify 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 clockify_pipeline.py

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

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Pipeline clockify_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset clockify_data
The duckdb destination used duckdb:/clockify.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 clockify_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 workspaces and time_entries from the Clockify 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 clockify_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.clockify.me/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "workspaces", "endpoint": {"path": "workspaces"}},
            {"name": "time_entries", "endpoint": {"path": "workspaces/{workspaceId}/user/{userId}/time-entries"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="clockify_pipeline",
        destination="duckdb",
        dataset_name="clockify_data",
    )
    load_info = pipeline.run(clockify_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("clockify_pipeline").dataset()
sessions_df = data.workspaces.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM clockify_data.workspaces LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("clockify_pipeline").dataset()
data.workspaces.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 Clockify 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 401/403, verify the X-Api-Key header is present and the key is valid for the workspace/region. For subdomain workspaces ensure you generated a workspace‑specific key.

Rate limiting

If you hit "Too many requests", slow down to under 50 req/s per addon per workspace when using X-Addon-Token. Implement exponential backoff and respect Last-Page pagination headers to avoid excessive requests.

Pagination

Use page and pageSize query params. Check Last-Page response header to stop fetching further pages.

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