SurveySparrow Python API Docs | dltHub

SurveySparrow is an online survey platform and API for creating, sending, and retrieving surveys, responses, contacts and related resources. The REST API base URL is Region-specific; common production base URLs: - US: https://api.surveysparrow.com - EU: https://eu-api.surveysparrow.com - AP: https://ap-api.surveysparrow.com - ME: https://me-api.surveysparrow.com - UK: https://eu-ln-api.surveysparrow.com - AP-SY (Sydney): https://ap-sy-app.surveysparrow.com - CA: https://ca-api.surveysparrow.com and All requests use OAuth 2.0 / private app tokens (Bearer) — access token required..

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 SurveySparrow data in under 10 minutes.

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What data can I load from SurveySparrow?

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

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How do I authenticate with the SurveySparrow API?

SurveySparrow supports OAuth2 flows and private app access tokens. Use Authorization: Bearer <access_token> header (or ?access_token= query param). Token endpoints are region-specific (e.g. https://api.surveysparrow.com/o/oauth/token).

1. Get your credentials

1. Log into SurveySparrow account. 2) Go to Settings → Apps & Integrations. 3) Create a Private App; provide name, description, select scopes. 4) Generate and copy the access token (displayed once). For OAuth2 apps: create app to obtain client_id and client_secret, perform authorization code flow to get code and exchange at the region token URL.

2. Add them to .dlt/secrets.toml

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[sources.survey_sparrow_source]
api_key = "your_access_token_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 SurveySparrow 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_sparrow_pipeline.py

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

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Pipeline survey_sparrow_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset survey_sparrow_data
The duckdb destination used duckdb:/survey_sparrow.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_sparrow_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 responses from the SurveySparrow 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_sparrow_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Region-specific; common production base URLs: - US: https://api.surveysparrow.com - EU: https://eu-api.surveysparrow.com - AP: https://ap-api.surveysparrow.com - ME: https://me-api.surveysparrow.com - UK: https://eu-ln-api.surveysparrow.com - AP-SY (Sydney): https://ap-sy-app.surveysparrow.com - CA: https://ca-api.surveysparrow.com",
            "auth": {
                "type": "bearer",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "surveys", "endpoint": {"path": "v1/surveys", "data_selector": "surveys"}},
            {"name": "responses", "endpoint": {"path": "v1/responses", "data_selector": "responses"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="survey_sparrow_pipeline",
        destination="duckdb",
        dataset_name="survey_sparrow_data",
    )
    load_info = pipeline.run(survey_sparrow_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_sparrow_pipeline").dataset()
sessions_df = data.surveys.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM survey_sparrow_data.surveys LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("survey_sparrow_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 SurveySparrow 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 Authorization: Bearer <access_token> header is present. Private app tokens are shown once — regenerate from Settings → Apps & Integrations if lost. 401 indicates invalid or expired token.

Rate limits and 429

API returns 429 Too Many Requests when client exceeds allowed rate; implement exponential backoff and respect Retry-After header if provided.

Pagination

List endpoints use standard paging parameters; check 'page' and 'per_page' (or limit/offset) in docs; responses include paginated arrays under keys like 'surveys' or 'responses'.

Common HTTP errors

400 Bad Request for missing params, 403 Forbidden for insufficient scopes, 404 Not Found for wrong IDs, 409 Conflict for duplicate resources, 422 Unprocessable Entity for validation errors, 500 for server errors.

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