Adobe Marketo Python API Docs | dltHub

Marketo is a marketing automation platform exposing REST APIs for managing leads, activities, assets, programs, campaigns and other marketing data. The REST API base URL is The base REST URL is instance-specific and provided in Admin > Integration > Web Services as the Endpoint (example format: https://<your-munchkin-id>.mktorest.com/rest) and all requests require an OAuth2 access token (Bearer) obtained from the Identity endpoint..

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 Adobe Marketo data in under 10 minutes.

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What data can I load from Adobe Marketo?

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

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How do I authenticate with the Adobe Marketo API?

Marketo uses two-legged OAuth 2.0. Obtain a Client ID and Client Secret from a Custom Service (LaunchPoint) and request a token from the Identity URL: GET <Identity_URL>/oauth/token?grant_type=client_credentials&client_id=<CLIENT_ID>&client_secret=<CLIENT_SECRET>. Pass the token in the Authorization header: Authorization: Bearer <access_token>.

1. Get your credentials

1. In Marketo UI go to Admin > Users and Roles and create a Role with API permissions. 2) Invite a new user as an API-Only user and assign the role. 3) In Admin > LaunchPoint create a New Service (Custom) and select the API-Only user. 4) Click View Details for the service to get Client ID and Client Secret. 5) In Admin > Web Services copy the Identity URL (Endpoint). 6) Call Identity URL /oauth/token with client_credentials to get access_token.

2. Add them to .dlt/secrets.toml

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[sources.adobe_marketo_source]
client_id = "your_client_id"
client_secret = "your_client_secret"
identity_url = "https://<your-instance>.mktorest.com/identity"
base_url = "https://<your-instance>.mktorest.com/rest"

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 Adobe 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 adobe_marketo_pipeline.py

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

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Pipeline adobe_marketo_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset adobe_marketo_data
The duckdb destination used duckdb:/adobe_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 adobe_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 lead from the Adobe 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 adobe_marketo_source(client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The base REST URL is instance-specific and provided in Admin > Integration > Web Services as the Endpoint (example format: https://<your-munchkin-id>.mktorest.com/rest)",
            "auth": {
                "type": "oauth2",
                "access_token": client_secret,
            },
        },
        "resources": [
            {"name": "leads", "endpoint": {"path": "rest/v1/leads.json", "data_selector": "result"}},
            {"name": "activities", "endpoint": {"path": "rest/v1/activities.json", "data_selector": "result"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM adobe_marketo_data.leads LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("adobe_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 Adobe 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

If you get error code 601 (invalid token) or 602 (expired token) renew the token by calling the Identity endpoint. Ensure you pass the token in the Authorization: Bearer header (support for access_token query param removed).

Rate limits and quotas

Default daily quota: 50,000 calls/day. Rate limit: 100 calls per 20 seconds; concurrency: 10 concurrent calls. Exceeding limits may return HTTP 429 or API errors — implement exponential backoff and concurrency control.

Pagination and paging tokens

Many endpoints return partial results and require paging parameters or a paging token (for activities use /rest/v1/activities/pagingtoken.json). Responses include a 'nextPageToken' (or paging tokens) and you must use provided tokens/params to fetch subsequent pages. Bulk export endpoints use separate bulk/v1 job/status/file endpoints.

Common API error notes: standard responses often return HTTP 200 with success:false and an errors array. Check 'success' boolean and 'errors' array. Use token refresh on 601/602 errors. Observe body fields like requestId, success, errors, result.

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