Wufoo Python API Docs | dltHub

Wufoo is an online form builder that provides a REST API for accessing forms and entries. The REST API base URL is https://{subdomain}.wufoo.com/api/v3 and Wufoo uses HTTP Basic Auth with the API key as the username..

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 Wufoo data in under 10 minutes.

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What data can I load from Wufoo?

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

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How do I authenticate with the Wufoo API?

All requests must include an HTTP Basic Authorization header where the Wufoo API key is supplied as the username and any string (e.g., X) as the password.

1. Get your credentials

1. Log in to your Wufoo account.
2. Open any form in the Form Manager.
3. Click the "More" dropdown and select "API Information".
4. Copy the 16‑digit API key displayed on that page.
5. Use this key as the username in HTTP Basic Auth; you may set the password to any non‑empty string (e.g., X).

2. Add them to .dlt/secrets.toml

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[sources.wufoo_forms_source]
api_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.

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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 Wufoo 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 wufoo_forms_pipeline.py

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

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Pipeline wufoo_forms_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset wufoo_forms_data
The duckdb destination used duckdb:/wufoo_forms.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 wufoo_forms_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 forms and form_entries from the Wufoo 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 wufoo_forms_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{subdomain}.wufoo.com/api/v3",
            "auth": {
                "type": "http_basic",
                "username": api_key,
            },
        },
        "resources": [
            {"name": "forms", "endpoint": {"path": "forms.json", "data_selector": "Forms"}},
            {"name": "form_entries", "endpoint": {"path": "forms/{form_id}/entries.json", "data_selector": "Entries"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="wufoo_forms_pipeline",
        destination="duckdb",
        dataset_name="wufoo_forms_data",
    )
    load_info = pipeline.run(wufoo_forms_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("wufoo_forms_pipeline").dataset()
sessions_df = data.forms.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM wufoo_forms_data.forms LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("wufoo_forms_pipeline").dataset()
data.forms.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 Wufoo 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 the API key is missing or incorrect, Wufoo returns a 401 Unauthorized response. Verify that the API key is supplied as the Basic Auth username and that a non‑empty password is included.

Rate limits

Wufoo does not publish a fixed rate‑limit header, but excessive requests may result in 429 Too Many Requests. throttle requests or implement exponential back‑off.

Pagination

List endpoints support page and per_page query parameters. If omitted, the API returns the first page with a default page size. Use the page value from the response to request subsequent pages.

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