SurveyMonkey Python API Docs | dltHub

SurveyMonkey is an online survey platform that provides a REST API (v3) to create, manage, and retrieve surveys, collectors, responses, pages, questions, webhooks and related resources. The REST API base URL is https://api.surveymonkey.com and All requests require an OAuth2 Bearer token 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 SurveyMonkey data in under 10 minutes.

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What data can I load from SurveyMonkey?

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

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How do I authenticate with the SurveyMonkey API?

SurveyMonkey uses OAuth2 (authorization_code). After exchanging the authorization code for an access_token at /oauth/token, include header Authorization: Bearer {access_token} and Accept: application/json.

1. Get your credentials

1. Register an app at https://developer.surveymonkey.com/apps/ to obtain a client_id and client_secret.
2. Direct the user to the SurveyMonkey OAuth authorize URL with client_id and redirect_uri to obtain an authorization code.
3. POST to https://api.surveymonkey.com/oauth/token with client_id, client_secret, code, redirect_uri, and grant_type=authorization_code to receive an access_token (and access_url).
4. Use the returned access_token in the Authorization header for API calls.

2. Add them to .dlt/secrets.toml

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[sources.survey_monkey_source]
client_id = "your_client_id"
client_secret = "your_client_secret"
access_token = "your_access_token"

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 SurveyMonkey 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 survey_monkey_pipeline.py

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

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Pipeline survey_monkey_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset survey_monkey_data
The duckdb destination used duckdb:/survey_monkey.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 survey_monkey_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 surveys and collectors_responses from the SurveyMonkey 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 survey_monkey_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.surveymonkey.com",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "surveys", "endpoint": {"path": "v3/surveys", "data_selector": "data"}},
            {"name": "collectors_responses", "endpoint": {"path": "v3/collectors/{collector_id}/responses", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="survey_monkey_pipeline",
        destination="duckdb",
        dataset_name="survey_monkey_data",
    )
    load_info = pipeline.run(survey_monkey_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("survey_monkey_pipeline").dataset()
sessions_df = data.surveys.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM survey_monkey_data.surveys LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("survey_monkey_pipeline").dataset()
data.surveys.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 SurveyMonkey 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

401 Unauthorized

• Cause: Missing or invalid Bearer token, or revoked token.
• Fix: Re‑run the OAuth flow to obtain a new access token.

403 Forbidden

• Cause: Insufficient scopes for the requested operation.
• Fix: Ensure the application requests the necessary scopes during authorization.

404 Not Found

• Cause: Invalid resource identifier (survey_id, collector_id, etc.).
• Fix: Verify that IDs are correct and belong to the authenticated account.

429 Rate Limit

• Cause: Exceeded the allowed number of requests.
• Fix: Respect the X-RateLimit-* headers and implement back‑off/retry logic.

Pagination

• All list endpoints are paginated using page (1‑based) and per_page query parameters. Responses include a data array and pagination links. Use the next link or increment page until no further records are returned.

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