Optinmonster Python API Docs | dltHub

OptinMonster is a lead‑generation platform that provides a JavaScript Events API to interact with campaigns on web pages. The REST API base URL is `` and Server‑side requests use an API Username and API Key; client‑side JavaScript uses the om{accountId}_{userId} object..

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 Optinmonster data in under 10 minutes.

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What data can I load from Optinmonster?

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

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How do I authenticate with the Optinmonster API?

Server‑side requests require the API Username and API Key (provided as query parameters or headers). Client‑side integration uses the JavaScript object om{accountId}_{userId} that is injected into the page.

1. Get your credentials

1. Sign up for an OptinMonster account or log in to your existing account.
2. In the dashboard, go to Settings → API (or Integrations).
3. Copy the API Username and API Key displayed there.
4. For WordPress, open the OptinMonster plugin settings in your WP admin and paste the credentials into the provided fields.
5. Save the settings; the credentials are now ready for server‑side API calls.

2. Add them to .dlt/secrets.toml

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[sources.optinmonster_source]
api_username = "your_api_username"
api_key = "your_api_key"

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 Optinmonster 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 optinmonster_pipeline.py

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

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Pipeline optinmonster_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset optinmonster_data
The duckdb destination used duckdb:/optinmonster.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 optinmonster_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 from the Optinmonster 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 optinmonster_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="optinmonster_pipeline",
        destination="duckdb",
        dataset_name="optinmonster_data",
    )
    load_info = pipeline.run(optinmonster_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("optinmonster_pipeline").dataset()
sessions_df = data..df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM optinmonster_data. LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("optinmonster_pipeline").dataset()
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 Optinmonster 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 API Username or API Key is missing, incorrect, or revoked, the WordPress plugin will display a connection error and no data will be returned.

Client‑side integration errors

The JavaScript Events object must follow the exact pattern om{accountId}_{userId}. An incorrectly formatted object name results in the events API not initializing and no campaign data being available.

Rate limiting / pagination

No official documentation on rate limits or pagination was found; developers should monitor HTTP response codes for 429 Too Many Requests and handle pagination manually if endpoints return paged results.

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