Everhour Python API Docs | dltHub

Everhour is a time‑tracking and project management platform exposing a REST API for programmatic access to team, project, task, time and reporting data. The REST API base URL is https://api.everhour.com and all requests require an API key supplied in X‑Api‑Key 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 Everhour data in under 10 minutes.

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What data can I load from Everhour?

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

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How do I authenticate with the Everhour API?

Everhour uses a single API key. Include the API key in every request using the X‑Api‑Key HTTP header. Optional X‑Accept‑Version header can pin API version (default 1.2).

1. Get your credentials

1. Sign in to your Everhour account at app.everhour.com.
2. Click your profile/avatar and open Profile/Settings.
3. Scroll to the bottom to the 'Application Access' or 'API key' section.
4. Copy the shown API key and use it as the value for X‑Api‑Key in requests.

2. Add them to .dlt/secrets.toml

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[sources.everhour_source]
api_key = "your_everhour_api_key_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 Everhour 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 everhour_pipeline.py

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

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Pipeline everhour_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset everhour_data
The duckdb destination used duckdb:/everhour.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 everhour_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 users and projects from the Everhour 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 everhour_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.everhour.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "users", "data_selector": "users"}},
            {"name": "projects", "endpoint": {"path": "projects", "data_selector": "projects"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="everhour_pipeline",
        destination="duckdb",
        dataset_name="everhour_data",
    )
    load_info = pipeline.run(everhour_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("everhour_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM everhour_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("everhour_pipeline").dataset()
data.users.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 Everhour 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 receive 401/403, verify X‑Api‑Key header is present and the key is valid. Ensure there are no extra spaces and the key copied from Profile > Application Access.

Rate limiting (429)

Everhour enforces rate limits (~20 requests / 10s per API key). On 429 responses respect the Retry‑After header before retrying; implement exponential backoff for bulk operations and contact Everhour for higher limits.

Pagination and large result sets

Many list endpoints are paginated. Check response metadata for paging fields and iterate pages. If you see truncated results, request subsequent pages.

API versioning

You can set X‑Accept‑Version to pin behavior (default 1.2). When unexpected changes occur, pin the version and contact Everhour support.

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