Zendesk sunshine Python API Docs | dltHub

Sunshine Conversations is a REST API for building and managing messaging experiences across channels. The REST API base URL is https://{subdomain}.zendesk.com/sc (licensed customers) or https://api.smooch.io (legacy) – EU legacy: https://api.eu-1.smooch.io and API requests require an API key (key id + secret) and support HTTP Basic authentication or JWT Bearer tokens..

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 Zendesk sunshine data in under 10 minutes.

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What data can I load from Zendesk sunshine?

Here are some of the endpoints you can load from Zendesk sunshine:

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How do I authenticate with the Zendesk sunshine API?

Requests can be authenticated using HTTP Basic auth (username = key_id, password = secret) or by supplying a JWT in the Authorization header as 'Bearer '.

1. Get your credentials

1. Sign in to your Zendesk/Sunshine Conversations account. 2) Navigate to the Admin area → Apps → App keys (or Account → API keys). 3) Click “Create new API key”. 4) Record the generated key id and secret. 5) Store the credentials securely for use in API calls.

2. Add them to .dlt/secrets.toml

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[sources.zendesk_sunshine_source]
key_id = "your_key_id_here"
secret = "your_key_secret_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 Zendesk sunshine 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 zendesk_sunshine_pipeline.py

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

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Pipeline zendesk_sunshine_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset zendesk_sunshine_data
The duckdb destination used duckdb:/zendesk_sunshine.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 zendesk_sunshine_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 conversations and messages from the Zendesk sunshine 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 zendesk_sunshine_source(key_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{subdomain}.zendesk.com/sc (licensed customers) or https://api.smooch.io (legacy) – EU legacy: https://api.eu-1.smooch.io",
            "auth": {
                "type": "http_basic",
                "secret": key_id,
            },
        },
        "resources": [
            {"name": "conversations", "endpoint": {"path": "v2/apps/{appId}/conversations", "data_selector": "conversations"}},
            {"name": "messages", "endpoint": {"path": "v2/apps/{appId}/conversations/{conversationId}/messages", "data_selector": "messages"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="zendesk_sunshine_pipeline",
        destination="duckdb",
        dataset_name="zendesk_sunshine_data",
    )
    load_info = pipeline.run(zendesk_sunshine_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("zendesk_sunshine_pipeline").dataset()
sessions_df = data.conversations.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM zendesk_sunshine_data.conversations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("zendesk_sunshine_pipeline").dataset()
data.conversations.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 Zendesk sunshine 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 a 401 or invalid_auth response, verify that the key id is used as the HTTP Basic auth username and the secret as the password. For JWT, ensure the token is correctly signed and sent as Authorization: Bearer <jwt>.

Rate limits (429 Too Many Requests)

Sunshine Conversations returns a 429 status with Retry-After headers when limits are exceeded. Implement exponential backoff with jitter and respect the rate‑limit headers.

Pagination quirks

All list endpoints use cursor‑based pagination (page[after], page[before], page[size]). The messages endpoint returns results in reverse chronological order by default and provides beforeCursor/afterCursor fields along with a hasMore flag.

Common error responses

The API returns structured error objects with codes such as bad_request (400), invalid_auth (401), forbidden (403), not_found (404), and too_many_requests (429).

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