Listrak Python API Docs | dltHub

Listrak is a platform that provides REST APIs for integrating with email features, cross-channel marketing, and data import functionalities. The REST API base URL is https://api.listrak.com/crosschannel/v1 and All requests require a Bearer token for authentication using OAuth 2.0..

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 Listrak data in under 10 minutes.

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What data can I load from Listrak?

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

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How do I authenticate with the Listrak API?

Authentication uses OAuth 2.0 with the client_credentials grant type. After obtaining a token, it must be included in all API requests via the 'Authorization: Bearer ' header.

1. Get your credentials

1. Log in to the Listrak application. 2. Navigate to 'Integrations' → 'Integration Management' (or 'Manage' → 'Integrations'). 3. Create a new Integration, specifying the appropriate integration type (e.g., 'Cross Channel'). 4. Securely store the provided Client ID and Client Secret, as the Client Secret cannot be retrieved if lost. 5. Ensure API access is not paused on the Integrations page, as paused access will reject all requests, including token issuance.

2. Add them to .dlt/secrets.toml

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[sources.listrak_email_source]
client_id = "your_client_id_here"
client_secret = "your_client_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 Listrak 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 listrak_email_pipeline.py

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

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Pipeline listrak_email_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset listrak_email_data
The duckdb destination used duckdb:/listrak_email.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 listrak_email_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 event_configurations and email_campaign from the Listrak 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 listrak_email_source(client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.listrak.com/crosschannel/v1",
            "auth": {
                "type": "bearer",
                "token": client_secret,
            },
        },
        "resources": [
            {"name": "event_configurations", "endpoint": {"path": "eventConfigurations", "data_selector": "data"}},
            {"name": "email_campaign", "endpoint": {"path": "v1/List/{listId}/Campaign/{campaignId}", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="listrak_email_pipeline",
        destination="duckdb",
        dataset_name="listrak_email_data",
    )
    load_info = pipeline.run(listrak_email_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("listrak_email_pipeline").dataset()
sessions_df = data.event_configurations.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM listrak_email_data.event_configurations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("listrak_email_pipeline").dataset()
data.event_configurations.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 Listrak 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 API requests are rejected, including requests to issue tokens, ensure that API access has not been paused on the Integrations page within the Listrak application. All requests will be rejected while API access is paused.

Common Error Codes

The Listrak API returns standard HTTP status codes. Common error body codes include:

• ERROR_INVALID_CREDENTIALS: Indicates issues with the provided authentication credentials.
• ERROR_INVALID_PARAMETER: Occurs when a request parameter is invalid.
• ERROR_MALFORMED_REQUEST_BODY: Signifies an issue with the structure of the request body.
• ERROR_UNABLE_TO_LOCATE_RESOURCE: The requested resource could not be found.
• ERROR_UNAUTHORIZED: The request lacks valid authentication credentials.
• ERROR_UNSUPPORTED_CONTENT_TYPE: The Content-Type header is not supported.
• ERROR_UNSUPPORTED_METHOD: The HTTP method used is not allowed for the resource.
• ERROR_UNSUPPORTED_PROTOCOL: An unsupported protocol was used.

Missing Client Secret

For security reasons, the Client Secret cannot be retrieved if it is lost. It is crucial to securely store a copy of your Client ID and Client Secret immediately after creating an Integration.

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