Wp-website-creator Python API Docs | dltHub

Elementor is a WordPress page builder plugin that stores its content and settings in post meta and is accessed via the WordPress REST API. The REST API base URL is https://{site}/wp-json and Requests require authentication via WordPress Application Passwords (HTTP Basic) or JWT 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 pip install "dlt[workspace]" and start loading Wp-website-creator data in under 10 minutes.

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What data can I load from Wp-website-creator?

Here are some of the endpoints you can load from Wp-website-creator:

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How do I authenticate with the Wp-website-creator API?

Use HTTP Basic authentication with username and application password, sent in the Authorization header as 'Basic base64(username:password)'.

1. Get your credentials

1. Log into the WordPress admin dashboard.
2. Navigate to your user profile (Users → Your Profile).
3. Scroll to the 'Application Passwords' section.
4. Enter a name for the password and click 'Add New Application Password'.
5. Copy the generated 24‑character password; it will not be shown again.
6. Use this password together with your username for HTTP Basic authentication.

2. Add them to .dlt/secrets.toml

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[sources.wp_website_creator_source]
application_password = "your_application_password_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 Wp-website-creator 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 wp_website_creator_pipeline.py

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

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Pipeline wp_website_creator_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset wp_website_creator_data
The duckdb destination used duckdb:/wp_website_creator.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 wp_website_creator_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 posts and pages from the Wp-website-creator 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 wp_website_creator_source(application_password=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{site}/wp-json",
            "auth": {
                "type": "http_basic",
                "password": application_password,
            },
        },
        "resources": [
            {"name": "posts", "endpoint": {"path": "wp/v2/posts"}},
            {"name": "pages", "endpoint": {"path": "wp/v2/pages"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="wp_website_creator_pipeline",
        destination="duckdb",
        dataset_name="wp_website_creator_data",
    )
    load_info = pipeline.run(wp_website_creator_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("wp_website_creator_pipeline").dataset()
sessions_df = data.posts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM wp_website_creator_data.posts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("wp_website_creator_pipeline").dataset()
data.posts.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 Wp-website-creator 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

• 401 Unauthorized – occurs when the Application Password is missing or invalid.
• 403 Forbidden – user does not have permission to edit posts or pages.

Endpoint errors

• 404 Not Found – the requested route does not exist; Elementor does not provide native REST endpoints.
• 400 Bad Request – malformed payload when updating post meta.

General API issues

• 500 Internal Server Error – plugin conflicts or server misconfiguration can cause failures.

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