Pdfmonkey Python API Docs | dltHub

Pdfmonkey is a REST API service to generate, manage and automate PDF documents from dynamic templates. The REST API base URL is https://api.pdfmonkey.io/api/v1 and All requests require a Bearer token in the Authorization 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 Pdfmonkey data in under 10 minutes.

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What data can I load from Pdfmonkey?

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

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How do I authenticate with the Pdfmonkey API?

Obtain your API Secret Key from the PDFMonkey dashboard (My Account). Send it in the Authorization header prefixed with Bearer, e.g. Authorization: Bearer YOUR_API_SECRET_KEY.

1. Get your credentials

1. Sign in to https://dashboard.pdfmonkey.io. 2) Open My Account (top-left menu). 3) Copy the API Secret Key labeled in your account. 4) Use it as the Bearer token in requests.

2. Add them to .dlt/secrets.toml

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[sources.pdfmonkey_source]
api_secret = "your_api_secret_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 Pdfmonkey 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 pdfmonkey_pipeline.py

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

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Pipeline pdfmonkey_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset pdfmonkey_data
The duckdb destination used duckdb:/pdfmonkey.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 pdfmonkey_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 templates and document_cards from the Pdfmonkey 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 pdfmonkey_source(api_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.pdfmonkey.io/api/v1",
            "auth": {
                "type": "bearer",
                "api_secret": api_secret,
            },
        },
        "resources": [
            {"name": "templates", "endpoint": {"path": "templates", "data_selector": "templates"}},
            {"name": "document_cards", "endpoint": {"path": "document_cards", "data_selector": "document_cards"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pdfmonkey_pipeline",
        destination="duckdb",
        dataset_name="pdfmonkey_data",
    )
    load_info = pipeline.run(pdfmonkey_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("pdfmonkey_pipeline").dataset()
sessions_df = data.templates.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM pdfmonkey_data.templates LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("pdfmonkey_pipeline").dataset()
data.templates.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 Pdfmonkey 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 Unauthorized, verify your API Secret Key and that the Authorization header is set to: Authorization: Bearer YOUR_API_SECRET_KEY. Ensure there are no extra quotes or whitespace.

Rate limits and quota

When you reach your quota for the period, new document generations will be blocked; you can still create Documents in draft status but setting status to pending (to trigger generation) will fail. Monitor HTTP 429 or error messages in responses.

Pagination and listing

Document listings return DocumentCard collections (lightweight objects) and are paginated with a 24-item limit per page. Use the list endpoint (document_cards) and the pagination metadata returned alongside the document_cards array.

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