Printavo Python API Docs | dltHub

Printavo is shop management software for screen-printing/embroidery businesses that provides a JSON REST API to access and manipulate account data. The REST API base URL is https://www.printavo.com/api and all requests require an API key (account-generated) passed as the key parameter (or via the sessions route).

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 Printavo data in under 10 minutes.

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What data can I load from Printavo?

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

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How do I authenticate with the Printavo API?

Printavo uses API keys. Keys can be generated in your Printavo account (My Account > Generate API Key) or via the sessions route. API examples in the docs show supplying the key as a request parameter (key) in request bodies or query strings; the same token works for V1 and V2.

1. Get your credentials

1. Log in to your Printavo account. 2) Go to My Account (or Account Settings). 3) Choose Generate API Key (or use the Login Sessions route in the API docs). 4) Copy the generated key and store securely.

2. Add them to .dlt/secrets.toml

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[sources.printavo_source]
api_key = "your_printavo_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 Printavo 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 printavo_pipeline.py

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

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Pipeline printavo_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset printavo_data
The duckdb destination used duckdb:/printavo.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 printavo_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 orders and customers from the Printavo 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 printavo_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.printavo.com/api",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "orders", "endpoint": {"path": "v1/orders", "data_selector": "orders"}},
            {"name": "customers", "endpoint": {"path": "v1/customers", "data_selector": "customers"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM printavo_data.orders LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("printavo_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 Printavo 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 401/403 responses, verify you are using a valid API key generated from My Account > Generate API Key (or via the sessions route). Ensure you include the key parameter exactly as shown in examples. Rotate/regenerate the key if compromised.

Rate limiting

Printavo enforces rate limits: 10 requests per 5 seconds per IP or user login email. On 429 responses, back off and retry after a short wait (exponential backoff).

Pagination

Large list endpoints are paginated in the API. Follow the Apiary docs for the specific pagination parameters returned by each endpoint (page/limit or next links) and iterate until all records are retrieved.

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