Lucca Python API Docs | dltHub

Lucca API is a unified, documented, and long-term supported API for all of Lucca's enterprise applications, with a beta phase for new features and continued support for Legacy APIs. The REST API base URL is https://{your-instance}.ilucca.net and Authentication for the Lucca API primarily uses OAuth2 (client_credentials), while legacy APIs and some integrations also support API keys..

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 Lucca data in under 10 minutes.

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What data can I load from Lucca?

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

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How do I authenticate with the Lucca API?

API requests require an Api-Version header and either a standard Authorization header with a Bearer token for OAuth2, or an X-API-Key header for legacy API key flows.

1. Get your credentials

For API keys, navigate to Lucca admin, then go to Settings > Authentication, SSO and API > API Keys to generate a new key. For OAuth2, obtain an access token via the POST Create Access-Token endpoint.

2. Add them to .dlt/secrets.toml

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[sources.lucca_source]
api_key = "your_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 Lucca 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 lucca_pipeline.py

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

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Pipeline lucca_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset lucca_data
The duckdb destination used duckdb:/lucca.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 lucca_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 employees and employments from the Lucca 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 lucca_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{your-instance}.ilucca.net",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "employees", "endpoint": {"path": "employees"}},
            {"name": "employments", "endpoint": {"path": "employments"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="lucca_pipeline",
        destination="duckdb",
        dataset_name="lucca_data",
    )
    load_info = pipeline.run(lucca_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("lucca_pipeline").dataset()
sessions_df = data.employees.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM lucca_data.employees LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("lucca_pipeline").dataset()
data.employees.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 Lucca 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

Common API Errors

• 401 Unauthorized: Occurs due to invalid or missing authentication tokens/API keys.
• 403 Forbidden: Indicates that the authenticated user lacks the necessary scope or permissions to access the requested resource.
• 429 Too Many Requests: Signifies that rate limits have been exceeded. The Lucca API is rate and size limited.
• 400 Bad Request: Returned for invalid request parameters.
• 404 Not Found: The requested resource does not exist.
• 422 Unprocessable Entity: Indicates validation errors with the request.

Pagination and Rate Limiting

All collection endpoints enforce pagination, requiring scripts to handle paginated responses. The Lucca API is also subject to rate and size limits, meaning many small requests are preferred over a few large ones.

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