Everee Python API Docs | dltHub

Everee API is a REST API for managing workers, companies, payrolls, payments, and timesheets. The REST API base URL is https://api.everee.com and All requests require HTTP Basic Authentication using an API token and an 'x-everee-tenant-id' 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 Everee data in under 10 minutes.

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What data can I load from Everee?

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

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How do I authenticate with the Everee API?

Authentication uses HTTP Basic with an API token in the Authorization header (basic ) and an 'x-everee-tenant-id' header. If the API token starts with 'sk_', it needs to be Base64-encoded.

1. Get your credentials

To obtain API credentials, navigate to the Integrations Hub > Everee API section in the Everee web app. There, you can generate an API token and note your company tenant ID.

2. Add them to .dlt/secrets.toml

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[sources.everee_source]
api_token = "your_api_token_here"
x_everee_tenant_id = "your_tenant_id_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 Everee 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 everee_pipeline.py

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

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Pipeline everee_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset everee_data
The duckdb destination used duckdb:/everee.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 everee_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 workers and companies from the Everee 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 everee_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.everee.com",
            "auth": {
                "type": "http_basic",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "workers", "endpoint": {"path": "api/v2/workers", "data_selector": "data"}},
            {"name": "companies", "endpoint": {"path": "api/v2/companies", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="everee_pipeline",
        destination="duckdb",
        dataset_name="everee_data",
    )
    load_info = pipeline.run(everee_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("everee_pipeline").dataset()
sessions_df = data.workers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM everee_data.workers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("everee_pipeline").dataset()
data.workers.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 Everee 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 you encounter an HTTP 401 error, it indicates an invalid or expired API token. Ensure your API token is correct and has not been deleted or rotated. If your token starts with sk_, confirm it is Base64-encoded.

Bad Requests

An HTTP 400 error typically signifies a bad request, such as an incorrect Content-Type header or invalid JSON in the request body. Ensure your requests include Content-Type: application/json and Accept: application/json headers, and that your JSON payload is well-formed.

Permission Errors

An HTTP 403 error indicates that you do not have the necessary permissions to access the requested resource.

Not Found Errors

An HTTP 404 error means the requested resource could not be found.

Rate Limiting

While not explicitly detailed, standard API practices suggest that an HTTP 429 error may occur if rate limits are exceeded.

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