Zendesk Chat Python API Docs | dltHub

Zendesk Chat is a live chat solution that enables businesses to engage website visitors and manage chat conversations via REST APIs. The REST API base URL is https://rtm.zopim.com/stream and All requests require an OAuth Bearer access token in the Authorization header..

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 Chat data in under 10 minutes.

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What data can I load from Zendesk Chat?

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

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How do I authenticate with the Zendesk Chat API?

Use an OAuth Bearer token: include Authorization: Bearer {access_token} in every request. All calls must be made over HTTPS.

1. Get your credentials

1. Log in to your Zendesk account as an administrator.
2. Navigate to Admin Center → Apps and integrations → Zendesk API (or Admin → Channels → Chat → OAuth settings).
3. Click Add OAuth client and fill in the required name, redirect URI, and scopes.
4. Save the client to obtain a client_id and client_secret.
5. Use the OAuth Authorization Code flow to exchange the code for an access_token, which will be used in API calls.

2. Add them to .dlt/secrets.toml

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[sources.zendesk_chat_source]
access_token = "your_oauth_access_token_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 Chat 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_chat_pipeline.py

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

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Pipeline zendesk_chat_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset zendesk_chat_data
The duckdb destination used duckdb:/zendesk_chat.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_chat_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 chats_metrics and agents_metrics from the Zendesk Chat 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_chat_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://rtm.zopim.com/stream",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "chats_metrics", "endpoint": {"path": "stream/chats", "data_selector": "content.data"}},
            {"name": "agents_metrics", "endpoint": {"path": "stream/agents", "data_selector": "content.data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="zendesk_chat_pipeline",
        destination="duckdb",
        dataset_name="zendesk_chat_data",
    )
    load_info = pipeline.run(zendesk_chat_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_chat_pipeline").dataset()
sessions_df = data.chats_metrics.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM zendesk_chat_data.chats_metrics LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("zendesk_chat_pipeline").dataset()
data.chats_metrics.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 Chat 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 (401)

If the Authorization header is missing or the bearer token is invalid, the API returns a 401 Unauthorized response. Verify that the token is current and has the required scopes.

Rate limiting (429)

Chat API endpoints are limited to 200 requests per minute. When the limit is exceeded the service returns 429 Too Many Requests along with X-RateLimit-Remaining: 0. Back‑off and retry after the time indicated by the Retry-After header.

Not Computed responses (200 with message)

For real‑time metric streams that have not yet generated data the API returns a 200 OK payload containing { "status_code": 200, "message": "Not Computed!" }. Poll the endpoint later; the data will appear once computed.

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