Medallia agile research Python API Docs | dltHub

Medallia Agile Research is a RESTful survey platform API for managing surveys, contacts, respondents, results, webhooks, and lookups. The REST API base URL is https://api-us.agileresearch.medallia.com/ and All requests require API key authentication via custom headers (API key in request headers)..

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 Medallia agile research data in under 10 minutes.

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What data can I load from Medallia agile research?

Here are some of the endpoints you can load from Medallia agile research:

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How do I authenticate with the Medallia agile research API?

The API uses API key (management key) authentication. Include the API key in the request headers; documentation refers to "authentication headers" and endpoints such as GET 3/keys/current returning roles for the current key. Use HTTPS and TLS v1.2. Use Accept header application/json for JSON responses.

1. Get your credentials

1. Log in to your Medallia Agile Research account (Management UI). 2) Open the API / Keys management section (API keys or developer keys). 3) Create or copy an API/management key with required roles (e.g., Results (read), Contacts (read)). 4) Store the key securely; the API exposes roles via GET /3/keys/current. 5) Use that key in request authentication headers for API calls.

2. Add them to .dlt/secrets.toml

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[sources.medallia_agile_research_source]
api_key = "your_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 Medallia agile research 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 medallia_agile_research_pipeline.py

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

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Pipeline medallia_agile_research_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset medallia_agile_research_data
The duckdb destination used duckdb:/medallia_agile_research.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 medallia_agile_research_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 respondents from the Medallia agile research 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 medallia_agile_research_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api-us.agileresearch.medallia.com/",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "surveys", "endpoint": {"path": "3/surveys", "data_selector": "Surveys"}},
            {"name": "respondents", "endpoint": {"path": "3/surveys/{surveyid}/respondents", "data_selector": "Respondents"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="medallia_agile_research_pipeline",
        destination="duckdb",
        dataset_name="medallia_agile_research_data",
    )
    load_info = pipeline.run(medallia_agile_research_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("medallia_agile_research_pipeline").dataset()
sessions_df = data.respondents.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM medallia_agile_research_data.respondents LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("medallia_agile_research_pipeline").dataset()
data.respondents.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 Medallia agile research 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 or 403, verify you are sending the API key in the authentication headers and that the key has required roles. Use GET 3/keys/current to inspect roles for the key.

Rate limiting / throttling

The API exposes a /3/throttle endpoint for testing throttling behavior. If you receive throttling errors, back off and retry with exponential backoff. Caching is applied on many GET endpoints (cached times noted per endpoint).

Pagination and expansions

Many list endpoints support filtering and paging; include standard query parameters (page, pageSize, etc.) where applicable. Use expand=Responses on respondents to include nested responses directly.

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