SmartEngage Python API Docs | dltHub

SmartEngage is a campaign management and marketing automation platform (SmartEngage by Intelepeer) that provides REST APIs to manage campaigns, lists, and related resources. The REST API base URL is https://engage.intelepeer.com and All requests require an Authorization token (API token) in request headers..

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 SmartEngage data in under 10 minutes.

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What data can I load from SmartEngage?

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

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How do I authenticate with the SmartEngage API?

API requests use a token sent in the Authorization header (Authorization: or Authorization: Bearer depending on portal). Content-Type: application/json is required for JSON requests.

1. Get your credentials

1. Log in to the CPaaS/Customer Portal or Intelepeer Engage dashboard. 2) Navigate to the SmartEngage or API/Auth section (Account/API tokens or Integrations). 3) Create or copy the provided API token. 4) Use that token in the Authorization header for API requests.

2. Add them to .dlt/secrets.toml

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[sources.smart_engage_source]
api_token = "your_engage_api_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 SmartEngage 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 smart_engage_pipeline.py

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

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Pipeline smart_engage_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset smart_engage_data
The duckdb destination used duckdb:/smart_engage.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 smart_engage_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 campaigns and campaigns_errors from the SmartEngage 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 smart_engage_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://engage.intelepeer.com",
            "auth": {
                "type": "api_key",
                "api_token": api_token,
            },
        },
        "resources": [
            {"name": "campaigns", "endpoint": {"path": "_rest/v2/campaigns"}},
            {"name": "campaigns_errors", "endpoint": {"path": "_rest/v2/campaigns/errors"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="smart_engage_pipeline",
        destination="duckdb",
        dataset_name="smart_engage_data",
    )
    load_info = pipeline.run(smart_engage_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("smart_engage_pipeline").dataset()
sessions_df = data.campaigns.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM smart_engage_data.campaigns LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("smart_engage_pipeline").dataset()
data.campaigns.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 SmartEngage 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 the Authorization token is missing or invalid the API returns 401 responses with a JSON body like { "message":"string", "status":401 }. Verify the token and that the account admin has required permissions.

Rate limits

Some related WebEngage APIs return 429 when rate limits are exceeded and include X-RateLimit-Limit and X-RateLimit-Remaining response headers. Implement retry/backoff when receiving HTTP 429.

Not found / bad host

Using the wrong base URL or license/account code can produce 404 responses. Ensure base_url is https://engage.intelepeer.com and include required query path parameters (id or name) where applicable.

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