Getform Python API Docs | dltHub

Getform is a hosted form backend that receives, stores, and exposes form submissions via a REST API. The REST API base URL is https://api.forminit.com/v1 and Requests require an API key (X-API-Key) or a form‑specific token..

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 Getform data in under 10 minutes.

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What data can I load from Getform?

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

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How do I authenticate with the Getform API?

Authentication supports two methods: (1) include your account API key in the X-API-Key header; (2) for form‑specific access, pass the token as a query parameter token={getform_api_token}.

1. Get your credentials

1. Log in to the Getform/Forminit dashboard. 2) Navigate to Account → API Tokens (or Form Settings for a form‑specific token). 3) Click Create Token or Refresh Token to generate a secret. 4) Copy and store the API key securely. For form‑specific tokens, open the form’s Settings page and view or regenerate the token.

2. Add them to .dlt/secrets.toml

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[sources.getform_source]
api_key = "sk_live_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 Getform 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 getform_pipeline.py

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

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Pipeline getform_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset getform_data
The duckdb destination used duckdb:/getform.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 getform_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 submissions and forms_submit from the Getform 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 getform_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.forminit.com/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "submissions", "endpoint": {"path": "v1/forms/{formId}", "data_selector": "data.submissions"}},
            {"name": "forms_submit", "endpoint": {"path": "f/{formId}", "data_selector": "submission"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="getform_pipeline",
        destination="duckdb",
        dataset_name="getform_data",
    )
    load_info = pipeline.run(getform_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("getform_pipeline").dataset()
sessions_df = data.submissions.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM getform_data.submissions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("getform_pipeline").dataset()
data.submissions.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 Getform 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 invalid you will receive 401 responses with payloads such as {"success": false, "error": "MISSING_API_KEY", "code": 401, "message": "Missing X-API-KEY key in the header"} or {"success": false, "error": "INVALID_API_KEY", "code": 401, "message": "Invalid API key provided."}. Verify the X-API-Key header is set correctly; for form‑specific token usage verify the token and formId.

Rate limits and 429 responses

With an API key the rate limit is typically 5 requests/sec; without an API key legacy/anonymous endpoints are limited (e.g., 1 request per 5 seconds or 60 requests/hour for free plans). Exceeding limits returns 429 with {"success": false, "error": "TOO_MANY_REQUESTS", "code": 429, "message": "Rate limit exceeded."}. Implement exponential backoff and paginate with size up to 100 to reduce requests.

Pagination quirks

List submissions returns page‑based pagination and a pagination object under data.pagination with fields count, currentPage, total, firstPage, lastPage, size. Use ?page= and ?size= (max 100) and iterate until currentPage == lastPage; combine pages to fetch all submissions.

Common error codes

400 Bad Request examples: {"success": false, "error": "FORM_ID_REQUIRED", "code": 400, "message": "Form ID is required."} and validation errors (EMPTY_SUBMISSION, FI_FIELD_TYPE_MISSING). 403 Forbidden: {"success": false, "error": "FORM_DISABLED", "code": 403, "message": "This form is currently disabled."}. 404 Not Found: {"success": false, "error": "FORM_NOT_FOUND", "code": 404, "message": "This form has been deleted."}. 500 Internal Server Error — retry with backoff.

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