Cal Python API Docs | dltHub

Cal.com is a scheduling platform that provides a REST API for managing bookings, calendars, and related resources. The REST API base URL is https://api.cal.com/v2 and All requests require 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 Cal data in under 10 minutes.

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What data can I load from Cal?

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

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How do I authenticate with the Cal API?

Authentication is performed by setting the HTTP header Authorization: Bearer <token>, where the token is the API key (prefixed with cal_) or a managed user access token.

1. Get your credentials

1. Log into your Cal.com account.
2. Navigate to Settings → Security.
3. Under the API Keys section, click Create New API Key.
4. Give the key a name, set desired permissions, and click Create.
5. Copy the generated key – it will be shown only once.
6. Store the key securely; it will be used as the Bearer token in API requests.

2. Add them to .dlt/secrets.toml

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[sources.cal_com_source]
api_key = "your_cal_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 Cal 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 cal_com_pipeline.py

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

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Pipeline cal_com_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cal_com_data
The duckdb destination used duckdb:/cal_com.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 cal_com_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 bookings and calendars from the Cal 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 cal_com_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.cal.com/v2",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "bookings", "endpoint": {"path": "v2/bookings", "data_selector": "data"}},
            {"name": "calendars", "endpoint": {"path": "v2/calendars", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cal_com_pipeline",
        destination="duckdb",
        dataset_name="cal_com_data",
    )
    load_info = pipeline.run(cal_com_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("cal_com_pipeline").dataset()
sessions_df = data.bookings.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cal_com_data.bookings LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cal_com_pipeline").dataset()
data.bookings.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 Cal 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

• Symptom: 401 Unauthorized response.
• Cause: Missing, malformed, or expired Bearer token.
• Resolution: Verify that the Authorization: Bearer <your_api_key> header is present and that the API key is active in the Cal.com dashboard under Settings > Security.

Rate limiting

• Symptom: 429 Too Many Requests response.
• Cause: Exceeding the limit of 120 requests per minute.
• Resolution: Implement client‑side throttling or exponential backoff. Consider consolidating requests or using pagination to reduce call volume.

Pagination quirks

• Symptom: Incomplete result sets when retrieving large collections.
• Cause: The API returns a pagination object with nextPageToken (or similar) that must be used for subsequent calls.
• Resolution: After the initial request, check the response for a pagination field and repeat the request with the provided token until no further pages are returned. The data array always contains the records for the current page.

Generic API errors

• Symptom: Non‑200 status codes with a JSON body containing status: "error".
• Cause: Validation errors, missing parameters, or internal server errors.
• Resolution: Inspect the error object in the response for code and message fields to adjust the request accordingly.

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