Alexa voice monkey Python API Docs | dltHub

Voice Monkey is a smart‑home Alexa skill and API that lets you trigger Alexa routines, start Flows, and make text‑to‑speech announcements (including audio, images and video) on your Alexa devices. The REST API base URL is https://api-v2.voicemonkey.io and all requests require a secret token (passed as token parameter or Authorization header).

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 Alexa voice monkey data in under 10 minutes.

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What data can I load from Alexa voice monkey?

Here are some of the endpoints you can load from Alexa voice monkey:

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How do I authenticate with the Alexa voice monkey API?

Voice Monkey uses a single secret token. Supply it either as a query parameter token=YOUR_TOKEN or set an HTTP header Authorization: YOUR_TOKEN on requests.

1. Get your credentials

1. Sign in to the Voice Monkey Console (https://voicemonkey.io/)
2. Open Settings → API Credentials or the API Playground
3. Copy the secret token shown (rotate if needed)
4. Keep the token secret and store it securely.

2. Add them to .dlt/secrets.toml

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[sources.alexa_voice_monkey_source]
token = "your_voicemonkey_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 Alexa voice monkey 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 alexa_voice_monkey_pipeline.py

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

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Pipeline alexa_voice_monkey_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset alexa_voice_monkey_data
The duckdb destination used duckdb:/alexa_voice_monkey.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 alexa_voice_monkey_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 announcement and trigger from the Alexa voice monkey 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 alexa_voice_monkey_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api-v2.voicemonkey.io",
            "auth": {
                "type": "api_key",
                "token": token,
            },
        },
        "resources": [
            {"name": "announcement", "endpoint": {"path": "announcement"}},
            {"name": "devices", "endpoint": {"path": "devices", "data_selector": "devices"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="alexa_voice_monkey_pipeline",
        destination="duckdb",
        dataset_name="alexa_voice_monkey_data",
    )
    load_info = pipeline.run(alexa_voice_monkey_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("alexa_voice_monkey_pipeline").dataset()
sessions_df = data.announcement.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM alexa_voice_monkey_data.announcement LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("alexa_voice_monkey_pipeline").dataset()
data.announcement.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 Alexa voice monkey 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 receive 401 or requests are ignored, verify you supplied the correct secret token either as token query parameter or Authorization header. Rotate the token in Settings → API Credentials and update stored secret.

Missing device or invalid device id

If an announcement or trigger fails, ensure the target device id is valid by calling /devices (devices list contains device id strings). Use the device id exactly as listed.

Rate limits and errors

The docs do not publish explicit rate limit numbers. Implement retries with exponential backoff for 5xx responses and treat 429 as a transient throttling error. Check the API Playground and console for additional error details.

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