Nice Python API Docs | dltHub

NICE CXone is a customer experience platform exposing REST APIs for agent/interaction management, real-time metrics, reporting, digital engagement, and admin automation. The REST API base URL is The API uses tenant-specific endpoints served from the CXone token/service domains; the official docs reference the CXone API catalog rather than a single universal fixed base URL. Implementations must use the tenant-specific base URL returned/assigned in your NICE CXone tenant (see Authentication/Getting Started). and All requests require an API authentication token (OAuth2 / Access Key / OpenID Connect)..

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 Nice data in under 10 minutes.

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What data can I load from Nice?

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

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How do I authenticate with the Nice API?

Requests require an API Access Token obtained from the CXone Authentication service (OAuth2 Client Credentials, Access Key or OpenID Connect). Include the token in the Authorization header as: Authorization: Bearer <access_token>. Some APIs also accept Access Key based flows and support injecting a CorrelationId header.

1. Get your credentials

1. Log in to NICE CXone Central or UserHub with an admin account. 2) Go to Security / API or OAuth client management (or the Authentication API docs). 3) Create an API client (Client ID/Secret) or generate an Access Key and assign required scopes/permissions in your Central Security Profile or UserHub Role. 4) Use the token endpoint from the CXone Authentication service (per-tenant URL) to request a token (client_credentials or Access Key flow). 5) Use returned Bearer token for API calls.

2. Add them to .dlt/secrets.toml

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[sources.nice_source]
api_key = "your_access_token_or_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 Nice 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 nice_pipeline.py

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

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Pipeline nice_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset nice_data
The duckdb destination used duckdb:/nice.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 nice_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 real_time_data and digital_engagement from the Nice 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 nice_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The API uses tenant-specific endpoints served from the CXone token/service domains; the official docs reference the CXone API catalog rather than a single universal fixed base URL. Implementations must use the tenant-specific base URL returned/assigned in your NICE CXone tenant (see Authentication/Getting Started).",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "real_time_data", "endpoint": {"path": "api/realtimedataapi", "data_selector": "(varies by endpoint; list key depends on specific call)"}},
            {"name": "digital_engagement", "endpoint": {"path": "api/digitalengagementapi", "data_selector": "(varies by endpoint)"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="nice_pipeline",
        destination="duckdb",
        dataset_name="nice_data",
    )
    load_info = pipeline.run(nice_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("nice_pipeline").dataset()
sessions_df = data.real_time_data.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM nice_data.real_time_data LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("nice_pipeline").dataset()
data.real_time_data.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 Nice 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

Authentication failures require a valid token and correct scopes. Ensure your API access token is current and has the necessary permissions for the requested resources.

Rate Limits

Rate limits are described in the Digital Engagement API documentation, with detailed throttling tables. For example, 'Get contact' has a limit of 120,000 requests per hour per tenant. Exceeding these limits will result in errors.

CorrelationId Header

The CorrelationId header can be injected into API requests and may be echoed in responses, which can be useful for tracing and debugging.

Permission and Restrict Data Settings

Permission and 'Restrict Data' settings may limit the records returned by API calls. Ensure that the API client has the appropriate permissions and that no data restrictions are in place that would prevent the retrieval of expected data.

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