Fitbit Python API Docs | dltHub

Fitbit Web API is an interface for accessing and managing data from Fitbit activity trackers, Aria scales, and manual logs. The REST API base URL is https://api.fitbit.com and all requests require an OAuth 2.0 Bearer access token.

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 add "dlt[hub]" 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?

Authentication is performed using OAuth 2.0. API requests must include an Authorization header containing a Bearer token: 'Authorization: Bearer <access_token>'.

1. Get your credentials

1. Create/register a Fitbit app in the Fitbit developer portal

• Go to https://dev.fitbit.com/apps/new.
• Fill out the application form.
• Set “Application Type” (commonly “Client” for a traditional app; choose appropriately for your use case).
• Set “Callback URL”/“Redirect URL” (must match exactly what you will use in your OAuth flow).
• Agree to the terms and click Register. 2) Copy your API application identifiers from the app settings
• After registration, record the “OAuth 2.0 Client ID” and “Client Secret” shown on the application details page. If you lose the client secret, you must generate a new one. 3) Obtain an OAuth 2.0 access token (the token you’ll use as the API credential) Option A (common for user data): Authorization Code flow
• First, redirect the user to authorize the app.
• Fitbit then redirects back to your callback URL with an authorization code.
• Exchange that authorization code for tokens by calling POST https://api.fitbit.com/oauth2/token with grant_type=authorization_code, client_id, and (depending on your flow) either your client secret via the required Authorization header or using the flow’s required parameters.
• Fitbit returns an access token and a refresh token. Option B (server-to-server token): Client Credentials flow
• Call POST https://api.fitbit.com/oauth2/token with grant_type=client_credentials using your client_id and client_secret.
• Fitbit authenticates your app and issues an access token. 4) For dlt REST extraction, store the access token as the credential
• For production ingestion into your pipeline, persist the OAuth “access token” securely (and refresh it using the refresh token flow when it expires).
• dlt REST integrations typically use a Bearer access token in the Authorization header.

2. Add them to .dlt/secrets.toml

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[sources.fitbit_source]
# Put your Fitbit OAuth 2.0 access token here (Bearer token)
FITBIT_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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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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uv run 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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uv run dlthub 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 POST /oauth2/token and GET /1/user/-/activities/date/{date}.json 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(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.fitbit.com",
            "auth": {"type": "bearer", "token": client_id},
        },
        "resources": [
            {"name": "activities_list", "endpoint": {"path": "1/user/-/activities/list.json", "data_selector": "activities"}},
            {"name": "sleep_list", "endpoint": {"path": "1.2/user/-/sleep/list.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.sleep_list.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM fitbit_data.sleep_list LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("fitbit_pipeline").dataset()
data.sleep_list.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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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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