Printful Python API Docs | dltHub

Printful is a print‑on‑demand and fulfillment platform providing a RESTful API to manage stores, products, orders, shipping, and related resources. The REST API base URL is https://api.printful.com and All requests require a private API token via HTTP Basic authentication..

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 Printful data in under 10 minutes.

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What data can I load from Printful?

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

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How do I authenticate with the Printful API?

Authentication uses a private API token sent via HTTP Basic Auth (username = API key, password empty) or an Authorization header with the token encoded as Basic <base64(api_key:)>.

1. Get your credentials

1. Log in to your Printful dashboard.
2. Go to Settings → API (or Developers → API).
3. Create a new private token (API key) and copy it.
4. Use the token as the HTTP Basic auth username (leave password blank) or include it in the Authorization header as Basic <base64(api_key:)>.

2. Add them to .dlt/secrets.toml

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[sources.printful_source]
api_key = "your_printful_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 Printful 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 printful_pipeline.py

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

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Pipeline printful_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset printful_data
The duckdb destination used duckdb:/printful.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 printful_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 stores and products from the Printful 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 printful_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.printful.com",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "stores", "endpoint": {"path": "stores", "data_selector": "result"}},
            {"name": "products", "endpoint": {"path": "products", "data_selector": "result"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="printful_pipeline",
        destination="duckdb",
        dataset_name="printful_data",
    )
    load_info = pipeline.run(printful_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("printful_pipeline").dataset()
sessions_df = data.orders.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM printful_data.orders LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("printful_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.

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What destinations can I load Printful 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 key is correct, used as the HTTP Basic auth username (password blank), or encoded in Authorization: Basic <base64(api_key:)> header. Ensure the token is active and belongs to the account.

Rate limiting

Printful exposes rate limit headers (X-Ratelimit-Limit, X-Ratelimit-Remaining, X-Ratelimit-Reset). On 429 responses, back off and retry after the reset time. V2 docs mention a leaky-bucket algorithm; implement exponential backoff and respect X-Ratelimit-* headers.

Pagination

List endpoints return a JSON object with a top-level "result" containing either an array or an object, and an optional "paging" object: {"total", "offset", "limit"}. Use offset and limit query parameters to page through results.

Common error responses

The API always returns a JSON object with "code" and "result". Error responses include HTTP status codes (e.g., 400, 401, 404, 429, 500) and may include descriptive messages in the response body. Handle and log message and code fields.

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