Marketo Python API Docs | dltHub

Marketo REST API allows for remote execution of many of the system's capabilities, interacting with data such as leads, companies, and custom objects. The REST API base URL is The base URL for the Marketo REST API is unique for each Marketo subscription and can be found in the Admin > Integration > Web Services menu, labeled as "Endpoint:" under the "REST API" section. An example format is https://284-RPR-133.mktorest.com/rest`.` and All requests require an access token for authentication, which must be included as an HTTP Bearer token..

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 Marketo data in under 10 minutes.

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What data can I load from Marketo?

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

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How do I authenticate with the Marketo API?

Authentication requires an access token to be sent in the 'Authorization' HTTP header using the 'Bearer' scheme, for example, 'Authorization: Bearer your_access_token'. Support for passing the access token as a query parameter will be removed on June 30, 2025.

1. Get your credentials

Specific step-by-step instructions for obtaining API credentials (access token) from the Marketo dashboard are not explicitly detailed in the provided documentation. Typically, you would log into your Marketo account, navigate to the Admin section, and look for API access or integration settings to generate or retrieve your access token and other necessary credentials.

2. Add them to .dlt/secrets.toml

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[sources.marketo_source]
access_token = "your_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 Marketo 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 marketo_pipeline.py

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

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Pipeline marketo_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset marketo_data
The duckdb destination used duckdb:/marketo.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 marketo_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 leads and activities from the Marketo 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 marketo_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The base URL for the Marketo REST API is unique for each Marketo subscription and can be found in the Admin > Integration > Web Services menu, labeled as "Endpoint:" under the "REST API" section. An example format is `https://284-RPR-133.mktorest.com/rest`.",
            "auth": {
                "type": "bearer",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "leads", "endpoint": {"path": "v1/leads", "data_selector": "result"}},
            {"name": "activities", "endpoint": {"path": "v1/activities", "data_selector": "result"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="marketo_pipeline",
        destination="duckdb",
        dataset_name="marketo_data",
    )
    load_info = pipeline.run(marketo_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("marketo_pipeline").dataset()
sessions_df = data.leads.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM marketo_data.leads LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("marketo_pipeline").dataset()
data.leads.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 Marketo 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

Support for authentication using the access_token query parameter is being removed on June 30, 2025. Ensure your project uses the Authorization header with a Bearer token to avoid authentication failures after this date.

Rate Limits

API access is limited to 100 calls per 20 seconds per instance. Exceeding this limit will result in errors.

Daily Quota

Subscriptions are allocated 50,000 API calls per day, which resets daily at 12:00 AM CST. Exceeding this quota will prevent further API calls until the next reset.

Concurrency Limit

A maximum of ten concurrent API calls are allowed. Attempting more concurrent calls may lead to errors.

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