Mogo Python API Docs | dltHub

Mogo API is an SMS messaging platform that allows sending SMS messages, retrieving delivery reports, checking account credits, and receiving inbound SMS via webhooks. The REST API base URL is https://api.mogo.io/ (staging: https://api-staging.mogo.io/, sandbox: https://sandbox-api.mogo.io/) and all requests require an API key pair in HTTP 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 Mogo data in under 10 minutes.

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What data can I load from Mogo?

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

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How do I authenticate with the Mogo API?

The API requires a matching pair of API Keys supplied in request headers. Contact Mogo to obtain keys. Excessive requests with incorrect auth may result in IP blocking for 30 minutes.

1. Get your credentials

1. Contact Mogo support or your Mogo account manager requesting API credentials. 2) Mogo will provide an API key pair (key_id and key_value). 3) Use the provided keys in request headers on every API call. 4) For sandbox testing request separate sandbox keys from Mogo.

2. Add them to .dlt/secrets.toml

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[sources.mogo_source]
key_id = "YOUR_KEY_ID"
key_value = "YOUR_KEY_VALUE"

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 Mogo 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 mogo_pipeline.py

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

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Pipeline mogo_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset mogo_data
The duckdb destination used duckdb:/mogo.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 mogo_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 send_message and delivery_report from the Mogo 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 mogo_source(api_key_pair=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.mogo.io/ (staging: https://api-staging.mogo.io/, sandbox: https://sandbox-api.mogo.io/)",
            "auth": {
                "type": "api_key",
                "api_key (config holds keys: key_id and key_value; dlt config should store both)": api_key_pair,
            },
        },
        "resources": [
            {"name": "send_message", "endpoint": {"path": "sendmessage"}},
            {"name": "delivery_report", "endpoint": {"path": "deliveryreport"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mogo_pipeline",
        destination="duckdb",
        dataset_name="mogo_data",
    )
    load_info = pipeline.run(mogo_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("mogo_pipeline").dataset()
sessions_df = data.delivery_report.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM mogo_data.delivery_report LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("mogo_pipeline").dataset()
data.delivery_report.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 Mogo 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 requests return 401 Unauthorized or 402 Payment Required, verify key_id and key_value are correct and included in headers. Too many incorrect auth attempts may cause the client IP to be blocked for 30 minutes.

Rate limits and throttling

If you receive 429 Too Fast, you have exceeded throttling limits; implement exponential backoff and reduce request rate.

Insufficient credits

402 Payment Required indicates insufficient credits; top up account to resume sending messages.

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