Asknicely Python API Docs | dltHub

AskNicely is a customer feedback platform for collecting NPS and survey responses and integrating them programmatically via a REST API. The REST API base URL is https://{your_domain}.asknice.ly/api/v1 and All requests require an API key passed as X-apikey header or as X-apikey query parameter..

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 Asknicely data in under 10 minutes.

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What data can I load from Asknicely?

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

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How do I authenticate with the Asknicely API?

Authentication is via a single API key found in Settings → API. Include it in requests as header X-apikey: YOURAPIKEY or as query parameter X-apikey=YOURAPIKEY.

1. Get your credentials

1. Log in to your AskNicely account (use your sub‑domain URL).
2. Click the Settings (cog) icon and navigate to the API section.
3. Click Show API Key (or similar) and copy the displayed key for use in API requests.

2. Add them to .dlt/secrets.toml

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[sources.asknicely_source]
api_key = "your_asknicely_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 Asknicely 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 asknicely_pipeline.py

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

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Pipeline asknicely_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset asknicely_data
The duckdb destination used duckdb:/asknicely.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 asknicely_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 responses_json and contacts from the Asknicely 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 asknicely_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{your_domain}.asknice.ly/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "responses_json", "endpoint": {"path": "responses/desc/{limit}/{page}/{from}/json/raw"}},
            {"name": "contacts", "endpoint": {"path": "contacts/add", "data_selector": "contacts"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="asknicely_pipeline",
        destination="duckdb",
        dataset_name="asknicely_data",
    )
    load_info = pipeline.run(asknicely_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("asknicely_pipeline").dataset()
sessions_df = data.responses_json.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM asknicely_data.responses_json LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("asknicely_pipeline").dataset()
data.responses_json.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 Asknicely 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 receive 401 Unauthorized or 403 Forbidden, verify that you are sending the correct API key. The key must be included either as the X-apikey header or as the X-apikey query parameter, and the sub‑domain in the base URL must match your account.

Pagination and large exports

Responses endpoints accept limit and page parameters. For very large result sets increase limit or iterate through page. If data appears truncated, request a higher limit or use the Excel export endpoint which is not paginated.

Data format quirks

The /responses/.../json/raw endpoint returns a top‑level JSON array (no wrapping key). Bulk contact import expects a JSON object with a top‑level contacts array. Datafeed endpoints (e.g., /datafeed/v1/sentstats/...) return nested objects; handle accordingly.

Rate limiting

A 429 Too Many Requests response indicates the API rate limit has been exceeded. Implement exponential back‑off and retry after the period indicated in the Retry-After header.

Bad request errors

A 400 Bad Request generally means malformed JSON (e.g., missing the required contacts array) or invalid query parameters. Review the request payload against the API documentation.

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