Outgrow Python API Docs | dltHub

Outgrow is a platform for creating interactive calculators, quizzes and assessments, with features to fetch third‑party data and use webhooks. The REST API base URL is https://app.outgrow.co and API access uses an account‑level API key..

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 Outgrow data in under 10 minutes.

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What data can I load from Outgrow?

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

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How do I authenticate with the Outgrow API?

Outgrow exposes an API key in the dashboard; include it in an Authorization header (or as required by the specific connector). Some integrations accept the key as a Basic auth header with base64‑encoded user:api_key.

1. Get your credentials

1. Log into your Outgrow dashboard.
2. Open Account / Settings (or look for the API Key section).
3. Locate "API Key" and copy it.
4. Use this key in your Authorization header or connector token field.

2. Add them to .dlt/secrets.toml

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[sources.outgrow_source]
api_key = "your_outgrow_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 Outgrow 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 outgrow_pipeline.py

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

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Pipeline outgrow_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset outgrow_data
The duckdb destination used duckdb:/outgrow.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 outgrow_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 webhooks and call_api_feature from the Outgrow 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 outgrow_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.outgrow.co",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "webhooks", "endpoint": {"path": "webhooks"}},
            {"name": "call_api_feature", "endpoint": {"path": "call_api"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="outgrow_pipeline",
        destination="duckdb",
        dataset_name="outgrow_data",
    )
    load_info = pipeline.run(outgrow_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("outgrow_pipeline").dataset()
sessions_df = data.webhooks.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM outgrow_data.webhooks LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("outgrow_pipeline").dataset()
data.webhooks.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 Outgrow 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 requests return 401/Unauthorized, verify you copied the API key from Account > API Key and include it in the Authorization header. Some integrations expect Basic <base64(user:api_key)> or an Authorization header value; try both patterns per connector instructions.

Missing public REST endpoints / limited API surface

Outgrow does not publish a full public REST API with documented GET endpoints; core functionality exposes a Call External API feature and webhooks. For programmatic access, use webhooks (to receive submissions) and Call External API (to fetch third‑party data into widgets). For integrations requiring a REST listing of account resources, contact Outgrow support.

Webhook delivery issues

Ensure your webhook endpoint accepts POST requests and responds with 2xx. Log and inspect raw POST payloads; Outgrow sends the submission as a single JSON object containing fields for each submitted item.

Common API errors: 401 Unauthorized (invalid/missing API key), 4xx bad request (malformed payload), 5xx server errors. Pagination and data selectors depend on third‑party APIs called via Call API.

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