Everfit Python API Docs | dltHub

Everfit is a fitness management platform that provides a REST API for accessing programs, workouts, clients and resources. The REST API base URL is https://public-api.everfit.io/public-api and All requests require an API token passed in the api-token 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 Everfit data in under 10 minutes.

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What data can I load from Everfit?

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

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How do I authenticate with the Everfit API?

Provide the token string in the request header api-token: <your_token> for every API call.

1. Get your credentials

1. Log in to your Everfit workspace.
2. Navigate to the Settings or Integrations section.
3. Locate the API Tokens or Developer Access page.
4. Click "Create New Token" (or request a token from Everfit support).
5. Copy the generated token and store it securely; you will use it as the value for api-token in request headers.

2. Add them to .dlt/secrets.toml

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[sources.everfit_source]
api_token = "your_api_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 Everfit 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 everfit_pipeline.py

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

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Pipeline everfit_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset everfit_data
The duckdb destination used duckdb:/everfit.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 everfit_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 programs and on_demand_workout_collection from the Everfit 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 everfit_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://public-api.everfit.io/public-api",
            "auth": {
                "type": "api_key",
                "api_key": api_token,
            },
        },
        "resources": [
            {"name": "programs", "endpoint": {"path": "programs", "data_selector": "data"}},
            {"name": "on_demand_workout_collection", "endpoint": {"path": "on-demand-workout/get-list-collection", "data_selector": "data.list"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="everfit_pipeline",
        destination="duckdb",
        dataset_name="everfit_data",
    )
    load_info = pipeline.run(everfit_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("everfit_pipeline").dataset()
sessions_df = data.programs.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM everfit_data.programs LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("everfit_pipeline").dataset()
data.programs.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 Everfit 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 Errors

• 403 INVALID_API_KEY – The provided API token is missing, malformed, or revoked. Verify that the api-token header contains a valid token.
• 403 CANNOT_ACCESS – The token does not have permission to access the requested resource.

Quota and Rate Limits

• 403 QUOTA_EXCEEDED – You have exceeded the allowed number of requests for the current period. Reduce request frequency or contact Everfit for higher limits.

Parameter Issues

• 400 MISSING_PARAMETER – Required query parameters such as page or limit are absent. Ensure all required parameters are supplied.

Server Errors

• 500 E_SERVER_ERROR – Internal server problem on Everfit’s side. Retry later.

Pagination

• Endpoints that return paginated data include page and limit query parameters and wrap the records in data.list. Use these parameters to iterate through all pages.

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