Google Calendar Python API Docs | dltHub

Google Calendar API is a RESTful API that provides programmatic access to calendars, events, settings, ACLs, colors, and free/busy queries for Google Calendar accounts. The REST API base URL is https://www.googleapis.com/calendar/v3 and All requests require OAuth 2.0 access tokens sent 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 Google Calendar data in under 10 minutes.

---

What data can I load from Google Calendar?

Here are some of the endpoints you can load from Google Calendar:

---

How do I authenticate with the Google Calendar API?

Use OAuth 2.0 to obtain an access token with appropriate calendar scopes (e.g. https://www.googleapis.com/auth/calendar or read-only https://www.googleapis.com/auth/calendar.readonly). Send the token in the HTTP header: Authorization: Bearer ACCESS_TOKEN.

1. Get your credentials

1. Go to Google Cloud Console (https://console.developers.google.com/).
2. Create or select a project and enable the Google Calendar API.
3. Under Credentials, create OAuth 2.0 Client ID (choose application type: Web, Desktop, Mobile, etc.).
4. Configure authorized redirect URIs for web apps or download JSON for other app types.
5. Use client_id and client_secret to perform the OAuth 2.0 flow and exchange authorization code for access and refresh tokens.
6. For service-to-service server use, create a service account and use domain-wide delegation if acting on behalf of users.

2. Add them to .dlt/secrets.toml

Copy
[sources.google_calendar_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_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.

---

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 Google Calendar 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 google_calendar_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline google_calendar_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 events and calendar_list from the Google Calendar 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 google_calendar_source(client_id, client_secret, refresh_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.googleapis.com/calendar/v3",
            "auth": {
                "type": "oauth2",
                "access_token": client_id, client_secret, refresh_token,
            },
        },
        "resources": [
            {"name": "events", "endpoint": {"path": "calendars/{calendarId}/events", "data_selector": "items"}},
            {"name": "calendar_list", "endpoint": {"path": "users/me/calendarList", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="google_calendar_pipeline",
        destination="duckdb",
        dataset_name="google_calendar_data",
    )
    load_info = pipeline.run(google_calendar_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("google_calendar_pipeline").dataset()
sessions_df = data.events.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM google_calendar_data.events LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("google_calendar_pipeline").dataset()
data.events.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 Google Calendar 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 Unauthorized or errors indicating invalid credentials, verify your access token, refresh token, and that the token has the required scope (e.g. https://www.googleapis.com/auth/calendar.readonly or https://www.googleapis.com/auth/calendar). Refresh expired tokens using the refresh_token.

Rate limits and quota

The API enforces per-project quotas. Excess quota usage returns 403 with quotaExceeded or dailyLimitExceeded errors. Review API Console quotas and consider exponential backoff on 429/403 quota errors.

Pagination and sync tokens

List endpoints return nextPageToken when results are paginated and nextSyncToken for incremental sync. Use pageToken to iterate and store nextSyncToken for later incremental queries.

Free/busy and POST quirks

freeBusy is a POST endpoint (/freeBusy) that returns a top-level 'calendars' map with each calendar key containing a 'busy' array. It's not a GET and its result is nested under 'calendars', not 'items'.

Common API errors: 400 Bad Request, 401 Unauthorized, 403 Forbidden (including quotaExceeded), 404 Not Found, 429 Too Many Requests, 500/503 Server Errors. Error payloads follow Google API error format, e.g. {"error": {"code": 403, "message": "...", "errors": [{"message":"...","domain":"...","reason":"..."}] }}.

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