Open F1 Python API Docs | dltHub

OpenF1 is an open-source REST API providing real-time and historical Formula 1 data (timing, telemetry, session info) in JSON and CSV formats. The REST API base URL is https://api.openf1.org and OAuth2 access token (Bearer) for authenticated/real-time endpoints; historical data often accessible without auth..

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 Open F1 data in under 10 minutes.

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What data can I load from Open F1?

Here are some of the endpoints you can load from Open F1:

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How do I authenticate with the Open F1 API?

Obtain an OAuth2 access token by POSTing username and password to https://api.openf1.org/token; include the returned access_token in requests using the Authorization: Bearer header. Tokens expire after 3600 seconds.

1. Get your credentials

1. Sponsor or create an account per openf1.org (paid sponsorship for live access). 2) Use your account credentials (username/password) to POST to https://api.openf1.org/token with Content-Type: application/x-www-form-urlencoded. 3) On success, store access_token and expires_in (seconds). 4) Refresh by re-requesting a token when expired.

2. Add them to .dlt/secrets.toml

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[sources.formula_one_source]
api_username = "YOUR_USERNAME"
api_password = "YOUR_PASSWORD"

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 Open F1 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 formula_one_pipeline.py

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

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Pipeline formula_one_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset formula_one_data
The duckdb destination used duckdb:/formula_one.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 formula_one_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 laps and sessions from the Open F1 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 formula_one_source(api_username, api_password=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.openf1.org",
            "auth": {
                "type": "oauth2_password (use dlt's 'bearer' for requests after token exchange)",
                "access_token": api_username, api_password,
            },
        },
        "resources": [
            {"name": "laps", "endpoint": {"path": "v1/laps"}},
            {"name": "sessions", "endpoint": {"path": "v1/sessions"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="formula_one_pipeline",
        destination="duckdb",
        dataset_name="formula_one_data",
    )
    load_info = pipeline.run(formula_one_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("formula_one_pipeline").dataset()
sessions_df = data.laps.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM formula_one_data.laps LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("formula_one_pipeline").dataset()
data.laps.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 Open F1 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 POST to /token returns 4xx, verify username/password and sponsorship status. Tokens expire in 3600s; include Authorization: Bearer <access_token> for protected endpoints.

Rate limits & access tiers

Historical endpoints are generally available without auth; real-time/live data requires authenticated subscription and has higher rate limits. If rate-limited, the API will return HTTP 429 — implement exponential backoff and prefer MQTT/Websocket for live streaming.

Data format & selectors

Most GET endpoints return a top-level JSON array of records (no wrapper key). If you receive an object instead, inspect the response — sample telemetry endpoints (MQTT/Websocket) include additional _id and _key fields.

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