WP Rocket Python API Docs | dltHub

Build a WP Rocket-to-database pipeline in Python using dlt with AI harness support for Claude Code, Cursor, and Codex.

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WP Rocket provides a Reseller REST API for managing orders and tokens for the WP Rocket service. The REST API base URL is https://api.wp-rocket.me/api/v2/reseller/ and all requests require Basic Authentication with App ID and API Key.

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 add "dlt[hub]" and start loading WP Rocket data in under 10 minutes.


What data can I load from WP Rocket?

Here are some of the endpoints you can load from WP Rocket:

ResourceEndpointMethodData selectorDescription
orders/api/v2/reseller/ordersGETRetrieves all orders
websites/api/v2/reseller/websitesGETRetrieves all websites
tokens/api/v2/reseller/tokensGETRetrieves all tokens
order_by_id/api/v2/reseller/orders/{ID}GETRetrieves a specific order by ID
order_count/api/v2/reseller/orders/countGETRetrieves total order count

How do I authenticate with the WP Rocket API?

The WP Rocket Reseller API uses Basic Authentication, requiring the Reseller App ID and Reseller API Key to be passed via the -u parameter in curl, or encoded as a Base64 string in the Authorization header. Requests must be sent over HTTPS.

1. Get your credentials

To obtain your WP Rocket API credentials (Email and API Key), log in to your WP Rocket account dashboard at https://wp-rocket.me/account/. Download the plugin zip file, unzip it, and locate the licence-data.php file within the plugin directory. The file contains the required WP_ROCKET_EMAIL and WP_ROCKET_KEY constants. Alternatively, if the plugin is already installed and validated on your site, these credentials may be visible in the WP Rocket plugin settings page within your WordPress admin dashboard.

2. Add them to .dlt/secrets.toml

[sources.wp_rocket_source] wp_rocket_email = "your_account_email_here" wp_rocket_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.


How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

uv init uv add "dlt[hub]"

1. Install the dlt AI harness:

uv run dlthub 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:

uv run dlthub ai toolkit install rest-api-pipeline

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 Rocket 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:

uv run python wp_rocket_pipeline.py

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

Pipeline wp_rocket_pipeline load step completed in 0.26 seconds 1 load package(s) were loaded to destination duckdb and into dataset wp_rocket_data The duckdb destination used duckdb:/wp_rocket.duckdb location to store data Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

uv run dlthub show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.


Python pipeline example

This example loads /wp-json/mcp/mcp-oauth-server and /wp-json/mcp/mcp-adapter-default-server from the WP Rocket API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def wp_rocket_source(reseller_api_key=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://api.wp-rocket.me/api/v2/reseller/", "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": reseller_api_key}, }, "resources": [ {"name": "orders", "endpoint": {"path": "api/v2/reseller/orders"}}, {"name": "websites", "endpoint": {"path": "api/v2/reseller/websites"}} ], } yield from rest_api_resources(config) def get_data() -> None: pipeline = dlt.pipeline( pipeline_name="wp_rocket_pipeline", destination="duckdb", dataset_name="wp_rocket_data", ) load_info = pipeline.run(wp_rocket_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.


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):

import dlt data = dlt.pipeline("wp_rocket_pipeline").dataset() sessions_df = data.orders.df() print(sessions_df.head())

SQL (DuckDB example):

SELECT * FROM wp_rocket_data.orders LIMIT 10;

In a marimo or Jupyter notebook:

import dlt data = dlt.pipeline("wp_rocket_pipeline").dataset() data.orders.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.


What destinations can I load WP Rocket data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

DestinationExample value
DuckDB (local, default)"duckdb"
PostgreSQL"postgres"
BigQuery"bigquery"
Snowflake"snowflake"
Redshift"redshift"
Databricks"databricks"
Filesystem (S3, GCS, Azure)"filesystem"

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.


Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

  • data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
  • dlthub-platform — Deploy, schedule, and monitor your pipeline in production.
uv run dlthub ai toolkit install data-exploration uv run dlthub ai toolkit install dlthub-platform

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