Zendesk - Conversations Python API Docs | dltHub

Sunshine Conversations is a REST API that provides messaging and conversation management for Zendesk (apps, conversations, messages, users, integrations). The REST API base URL is https://{subdomain}.zendesk.com/sc and Supports Basic auth (API key id as username and secret as password) or JWT (Bearer) for server-to-server calls; OAuth tokens also supported for integration-scoped access..

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 - Conversations data in under 10 minutes.

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What data can I load from Zendesk - Conversations?

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

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How do I authenticate with the Zendesk - Conversations API?

API requests accept Basic authentication using an API key (key id=username, secret=password) or JWTs sent in Authorization: Bearer . Content-Type: application/json.

1. Get your credentials

1. In Zendesk Admin Center create a Conversations Integration (or Custom Integration). 2) In the integration, create an API key (returns key id and secret). 3) Use the key id as username and secret as password for HTTP Basic auth, or use them to sign JWTs (kid header) to produce Bearer tokens.

2. Add them to .dlt/secrets.toml

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[sources.zendesk_conversations_source]
api_key_id = "your_key_id_here"
api_key_secret = "your_key_secret_here"
# or for JWT
jwt = "your_jwt_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 - Conversations 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_conversations_pipeline.py

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

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Pipeline zendesk_conversations_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset zendesk_conversations_data
The duckdb destination used duckdb:/zendesk_conversations.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_conversations_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 - Conversations 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_conversations_source(api_key_id, api_key_secret (for Basic) or jwt (for Bearer)=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{subdomain}.zendesk.com/sc",
            "auth": {
                "type": "http_basic",
                "api_key (for Basic key id/secret pair) / jwt (for JWT)": api_key_id, api_key_secret (for Basic) or jwt (for Bearer),
            },
        },
        "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_conversations_pipeline",
        destination="duckdb",
        dataset_name="zendesk_conversations_data",
    )
    load_info = pipeline.run(zendesk_conversations_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_conversations_pipeline").dataset()
sessions_df = data.conversations.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM zendesk_conversations_data.conversations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("zendesk_conversations_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 - Conversations 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 Basic auth credentials are incorrect you'll receive 401 Unauthorized. Verify API key id (username) and secret (password), or confirm JWT signature and kid. Use the tokenInfo endpoint to validate OAuth tokens.

Rate limits

Sunshine Conversations returns 429 Too Many Requests when rate limits are exceeded. Implement exponential backoff with jitter. Chat Conversations (GraphQL) has specific limits (e.g., 100 req/s) documented separately.

Pagination and cursor usage

Most list endpoints use cursor-based pagination. Use page[after] or page[before] (only one at a time) and optional page[size]. Responses include links and meta objects; for messages the API paginates backwards by default using page[before].

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