Getfeedback Python API Docs | dltHub

GetFeedback is a customer feedback and survey platform that exposes a RESTful JSON API for managing surveys and fetching survey responses. The REST API base URL is https://api.getfeedback.com and all requests require a 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 Getfeedback data in under 10 minutes.

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What data can I load from Getfeedback?

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

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How do I authenticate with the Getfeedback API?

The API uses OAuth2‑style bearer tokens. Include the token in the HTTP Authorization header as: Authorization: Bearer YOUR_TOKEN. Send Accept: application/json on requests.

1. Get your credentials

In GetFeedback: Account Settings > API (Generate an API token). If using GetFeedback for Salesforce or an enterprise plan, an admin can create tokens in Account Settings > API. For EU‑hosted accounts, use the EU API base URL.

2. Add them to .dlt/secrets.toml

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[sources.getfeedback_source]
access_token = "your_getfeedback_bearer_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 Getfeedback 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 getfeedback_pipeline.py

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

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Pipeline getfeedback_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset getfeedback_data
The duckdb destination used duckdb:/getfeedback.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 getfeedback_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 Getfeedback 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 getfeedback_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.getfeedback.com",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "surveys", "endpoint": {"path": "surveys", "data_selector": "surveys"}},
            {"name": "responses", "endpoint": {"path": "surveys/:survey_id/responses", "data_selector": "responses"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="getfeedback_pipeline",
        destination="duckdb",
        dataset_name="getfeedback_data",
    )
    load_info = pipeline.run(getfeedback_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("getfeedback_pipeline").dataset()
sessions_df = data.responses.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM getfeedback_data.responses LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("getfeedback_pipeline").dataset()
data.responses.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 Getfeedback 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 you get 401/403, verify the Authorization header: Authorization: Bearer YOUR_TOKEN. Tokens are created in Account Settings > API. For EU accounts use https://api.eu.getfeedback.com.

Pagination & consistency

Responses use page/per_page (default per_page 30, max 100) and no server cursor; use page numbers and since/since_field for incremental pulls. Note there is no cursor state so repeated requests can return overlapping data if not using since parameters.

Rate limits and 429s

The public docs do not list exact rate limits. If you receive 429 or server throttling, implement exponential backoff and retry. Contact GetFeedback support for account‑specific limits.

Common HTTP errors

200: success. 204: success with no content. 400: bad request (JSON parse error). 422: unprocessable entity — check response body for field‑level errors. 401/403: invalid or missing token.

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