Alchemer Python API Docs | dltHub

Alchemer is an online survey platform providing a REST API to manage surveys, questions, responses, users and related objects. The REST API base URL is https://api.alchemer.com/v5 and API key + secret (api_token & api_token_secret) or OAuth 1.0; API key query params commonly used..

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 Alchemer data in under 10 minutes.

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What data can I load from Alchemer?

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

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How do I authenticate with the Alchemer API?

Alchemer supports API Key authentication using api_token and api_token_secret passed as query parameters (or in request body). OAuth 1.0 is also supported for apps; OAuth endpoints are under /head/oauth for request/access tokens.

1. Get your credentials

1. Log in to Alchemer. 2) Admins: go to Security > API Access and click Create an API Key (or Account > Integrations > API Key for non‑admin). 3) Copy the generated api_token and api_token_secret. 4) For OAuth register an application at the region‑specific restful‑register URL for your region (US/EU/CA) to obtain consumer key/secret.

2. Add them to .dlt/secrets.toml

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[sources.alchemer_source]
api_token = "YOUR_API_TOKEN"
api_token_secret = "YOUR_API_TOKEN_SECRET"

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 Alchemer 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 alchemer_pipeline.py

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

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Pipeline alchemer_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset alchemer_data
The duckdb destination used duckdb:/alchemer.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 alchemer_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 Alchemer 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 alchemer_source(api_token, api_token_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.alchemer.com/v5",
            "auth": {
                "type": "api_key",
                "api_token": api_token, api_token_secret,
            },
        },
        "resources": [
            {"name": "surveys", "endpoint": {"path": "v5/survey", "data_selector": "data"}},
            {"name": "responses", "endpoint": {"path": "v5/survey/{survey_id}/surveyresponse", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="alchemer_pipeline",
        destination="duckdb",
        dataset_name="alchemer_data",
    )
    load_info = pipeline.run(alchemer_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("alchemer_pipeline").dataset()
sessions_df = data.surveyresponses.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM alchemer_data.surveyresponses LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("alchemer_pipeline").dataset()
data.surveyresponses.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 Alchemer 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 api_token or api_token_secret are invalid you'll receive result_ok:false and HTTP 401 with message such as "Login failed / Invalid auth token". Regenerate keys in Security > API Access for compromised keys.

Pagination and caching

GET requests support page and resultsperpage parameters. Responses may be cached for 60 seconds; identical requests within the cache window return the same data. Use pagination parameters to iterate through large result sets.

Rate limits

Request limits are plan‑dependent; see the Request Limits section in the docs. If limits are exceeded the API will throttle requests. Implement back‑off and retry logic.

OAuth notes

When using OAuth 1.0, register your app at the region‑specific restful‑register URL and follow the OAuth 1.0 flow (request token, user authorize, access token). OAuth endpoints are under /head/oauth.

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