Moloco Python API Docs | dltHub

Moloco Ads is a programmatic advertising platform providing APIs to manage campaigns, generate campaign reports and export log-level event files. The REST API base URL is https://api.moloco.cloud/cm/v1 and All requests require a Bearer token obtained from Moloco using an API key..

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 Moloco data in under 10 minutes.

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What data can I load from Moloco?

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

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How do I authenticate with the Moloco API?

Create an API key in the Moloco Ads portal, then POST {"api_key":"$API_KEY"} to https://api.moloco.cloud/cm/v1/auth/tokens to receive {"token":"$TOKEN"}. Include Authorization: Bearer $TOKEN on all API requests. Tokens are valid for 16 hours.

1. Get your credentials

1. Sign in to the Moloco Ads portal (https://portal.moloco.cloud/signin). 2) Navigate to the API Keys section and create a new API key. 3) Copy the API key. 4) Exchange the API key for an access token by POSTing JSON {"api_key":"YOUR_API_KEY"} to https://api.moloco.cloud/cm/v1/auth/tokens. 5) Use the returned token value in the Authorization header: Authorization: Bearer .

2. Add them to .dlt/secrets.toml

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[sources.moloco_source]
api_key = "your_api_key_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 Moloco 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 moloco_pipeline.py

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

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Pipeline moloco_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset moloco_data
The duckdb destination used duckdb:/moloco.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 moloco_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 report_file_json and logs_status from the Moloco 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 moloco_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.moloco.cloud/cm/v1",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "report_file_json", "endpoint": {"path": "(external URL from `location_json`)", "data_selector": "rows"}},
            {"name": "logs_status", "endpoint": {"path": "cm/v1/logs/{log_id}/status", "data_selector": "location_csv"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="moloco_pipeline",
        destination="duckdb",
        dataset_name="moloco_data",
    )
    load_info = pipeline.run(moloco_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("moloco_pipeline").dataset()
sessions_df = data.report_file_json.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM moloco_data.report_file_json LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("moloco_pipeline").dataset()
data.report_file_json.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 Moloco 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 errors

If you receive errors like { "code":3, "message":"\"invalid token type\" has invalid value." } ensure that the Authorization: Bearer <token> header is present and that the token has not expired (tokens are valid for ~16 hours). Re‑authenticate using the API key to obtain a fresh token.

Report / Log generation and download

Report and log creation return a status URL. Poll the status endpoint until status == "READY". Only then use the location_json, location_csv, or location_avro URLs returned. Attempting to download before the status is READY results in 404 or empty files.

Rate limits and request rejections

Moloco rejects requests without a valid token or when the token has expired (HTTP 401/403). Implement exponential backoff and retry logic for 429 responses indicating rate‑limit exhaustion.

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