Fitbit Python API Docs | dltHub

Fitbit is a health and activity platform that provides a Web API for accessing data from Fitbit trackers, Aria scales, and user‑entered logs. The REST API base URL is https://api.fitbit.com and all requests require a Bearer access token (OAuth 2.0).

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 Fitbit data in under 10 minutes.

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What data can I load from Fitbit?

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

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How do I authenticate with the Fitbit API?

OAuth 2.0 (Authorization Code or Client Credentials) with Bearer access token. Add header: Authorization: Bearer <access_token>.

1. Get your credentials

1. Create a Fitbit developer account and register an app at https://dev.fitbit.com/apps. 2) Choose OAuth 2.0 Authorization Code or Client Credentials grant depending on app type. 3) Configure redirect URI(s) and required scopes (e.g., activity, heartrate, sleep). 4) For Authorization Code flow: direct the user to /oauth2/authorize to obtain an authorization code, then exchange the code at POST https://api.fitbit.com/oauth2/token to receive an access_token and refresh_token. 5) Store the access_token in the dlt secret and refresh it using POST /oauth2/token with grant_type=refresh_token when it expires.

2. Add them to .dlt/secrets.toml

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[sources.fitbit_source]
access_token = "your_fitbit_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 Fitbit 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 fitbit_pipeline.py

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

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Pipeline fitbit_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fitbit_data
The duckdb destination used duckdb:/fitbit.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 fitbit_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 activities_summary and sleep_by_date from the Fitbit 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 fitbit_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.fitbit.com",
            "auth": {
                "type": "bearer",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "activities_summary", "endpoint": {"path": "1/user/-/activities/date/{date}.json", "data_selector": "summary"}},
            {"name": "sleep_by_date", "endpoint": {"path": "1.2/user/-/sleep/date/{date}.json", "data_selector": "sleep"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fitbit_pipeline",
        destination="duckdb",
        dataset_name="fitbit_data",
    )
    load_info = pipeline.run(fitbit_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("fitbit_pipeline").dataset()
sessions_df = data.activities_summary.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM fitbit_data.activities_summary LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("fitbit_pipeline").dataset()
data.activities_summary.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 Fitbit 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 (401/403)

If the API returns 401 Unauthorized or 403 Forbidden: verify the Authorization header is present and formatted exactly as Authorization: Bearer <access_token>. Ensure the token is not expired; if it is, refresh it using POST /oauth2/token with grant_type=refresh_token. Also confirm the app has the required scopes and the user granted consent.

Rate limits and 429

Fitbit enforces rate limits per client and per user. When limits are exceeded the API returns 429 Too Many Requests. Inspect the Retry-After header (seconds) and back off before retrying. Batch requests and limit the time range if possible.

Pagination quirks

List endpoints (e.g., /sleep/list.json) return paginated collections using nextPageID or next links. Include query parameters such as limit, offset, beforeDate, or afterDate as documented to navigate pages.

Intraday and permission requirements

Intraday endpoints require the special intraday scope and, for some apps, explicit permission from Fitbit. Responses include a nested key like activities-heart-intraday containing a dataset array; use that key as the data selector.

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