Fillout Python API Docs | dltHub

Fillout is a form‑builder platform that provides a REST API to access forms and submissions programmatically. The REST API base URL is https://api.fillout.com/v1/api and All requests require a Bearer API key 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 Fillout data in under 10 minutes.

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What data can I load from Fillout?

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

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How do I authenticate with the Fillout API?

Obtain an API key from the Developer settings in your Fillout account and include it in each request as Authorization: Bearer <your-api-key>.

1. Get your credentials

1. Log into your Fillout account.
2. Navigate to Build → Home → Settings → Developer (or open https://build.fillout.com/home/settings/developer).
3. Create a new API key or copy an existing one.
4. Save the key; you will use it as a Bearer token in the Authorization header of every request.

2. Add them to .dlt/secrets.toml

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[sources.fillout_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 Fillout 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 fillout_pipeline.py

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

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Pipeline fillout_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fillout_data
The duckdb destination used duckdb:/fillout.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 fillout_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 submissions from the Fillout 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 fillout_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.fillout.com/v1/api",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "forms", "endpoint": {"path": "forms"}},
            {"name": "submissions", "endpoint": {"path": "forms/{formId}/submissions"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM fillout_data.submissions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("fillout_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 Fillout 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

Ensure the Authorization header uses the exact format Authorization: Bearer <api_key>. Invalid, missing, or revoked keys result in a 401 Unauthorized response.

Rate limiting and errors

The API follows standard HTTP status codes. A 429 Too Many Requests indicates rate limiting; 4xx or 5xx responses should be retried with exponential back‑off. The Create Submissions endpoint notes a maximum of 10 items per request, which can also trigger validation errors.

Pagination

Many GET endpoints return a top‑level JSON array without built‑in pagination. For large result sets, consider using query parameters for date ranges or contacting Fillout support for bulk export options.

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