Strava Python API Docs | dltHub

Strava is a social fitness platform that provides a REST API for accessing athlete activities, routes, segments, and related data. The REST API base URL is https://www.strava.com/api/v3 and All requests require a Bearer token provided via 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 Strava data in under 10 minutes.

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What data can I load from Strava?

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

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How do I authenticate with the Strava API?

Strava uses OAuth2. Provide the access token in the header Authorization: Bearer <access_token> for every request.

1. Get your credentials

1. Log in to Strava and go to https://www.strava.com/settings/api.
2. Click “Create an Application” and fill in the required details.
3. Note the generated Client ID and Client Secret.
4. Implement the OAuth2 authorization code flow to obtain an authorization code.
5. POST the code, Client ID, and Client Secret to https://www.strava.com/oauth/token to receive an access token (and refresh token).

2. Add them to .dlt/secrets.toml

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[sources.strava_source]
access_token = "your_access_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 Strava 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 strava_pipeline.py

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

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Pipeline strava_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset strava_data
The duckdb destination used duckdb:/strava.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 strava_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 athlete_activities and clubs_activities from the Strava 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 strava_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.strava.com/api/v3",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "athlete_activities", "endpoint": {"path": "athlete/activities"}},
            {"name": "clubs_activities", "endpoint": {"path": "clubs/{id}/activities"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="strava_pipeline",
        destination="duckdb",
        dataset_name="strava_data",
    )
    load_info = pipeline.run(strava_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("strava_pipeline").dataset()
sessions_df = data.athlete_activities.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM strava_data.athlete_activities LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("strava_pipeline").dataset()
data.athlete_activities.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 Strava 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

• Response code 401 with message "Authorization Error" indicates a missing or expired token. Refresh the token using the refresh token endpoint.

Rate limits

• Strava enforces 200 requests per 15‑minute window and 2,000 requests per day per application. Exceeding limits returns 429 Too Many Requests. Implement back‑off and respect the X-RateLimit-Remaining header.

Pagination quirks

• List endpoints use page and per_page query parameters. Defaults are page=1 and per_page=30. If you receive fewer items than per_page, you have reached the last page.
• Some endpoints (e.g., streams) may return a JSON object instead of an array when key_by_type=true; handle both structures.

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