H5P Python API Docs | dltHub

The H5P API Reference provides documentation for H5P libraries, including namespaces, classes, and methods like getCopyrights. The H5P Dialog class creates popup dialogs. The API supports initializing H5P instances by scanning for ".h5p-content". The REST API base URL is No single base URL – use the host site URL (e.g., https://your‑wordpress‑site.com) when REST endpoints are provided by a specific integration. and Authentication is platform‑specific; no single method is defined by H5P core..

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 H5P data in under 10 minutes.

---

What data can I load from H5P?

Here are some of the endpoints you can load from H5P:

---

How do I authenticate with the H5P API?

Authentication varies by host: WordPress may require a JWT or cookie token, while Drupal uses its own API token or session cookie. The required headers are therefore implementation‑specific.

1. Get your credentials

1. Log into the CMS hosting H5P (WordPress, Drupal, etc.).
2. Navigate to the plugin/module settings page.
3. Locate the API or authentication section.
4. Generate or copy the existing API token/JWT or note the cookie authentication method.
5. Save the token for use in dlt configuration.

2. Add them to .dlt/secrets.toml

Copy
[sources.h5p_source]
api_token = "your_api_token_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:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 H5P 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:

Copy
python h5p_pipeline.py

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

Copy
Pipeline h5p_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset h5p_data
The duckdb destination used duckdb:/h5p.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline h5p_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.

---

Python pipeline example

This example loads content and libraries from the H5P API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def h5p_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "No single base URL – use the host site URL (e.g., https://your‑wordpress‑site.com) when REST endpoints are provided by a specific integration.",
            "auth": {
                "type": "api_key",
                "api_key": api_token,
            },
        },
        "resources": [
            {"name": "content", "endpoint": {"path": "wp-json/h5p/v1/content", "data_selector": "data"}},
            {"name": "libraries", "endpoint": {"path": "wp-json/h5p/v1/libraries", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="h5p_pipeline",
        destination="duckdb",
        dataset_name="h5p_data",
    )
    load_info = pipeline.run(h5p_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):

Copy
import dlt

data = dlt.pipeline("h5p_pipeline").dataset()
sessions_df = data.content.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM h5p_data.content LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("h5p_pipeline").dataset()
data.content.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 H5P 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.

---

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.

Copy
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