Whatsapp-notifications Python API Docs | dltHub

Twilio WhatsApp is a service that enables sending and receiving WhatsApp messages (notifications, templated messages and media) via the Twilio Programmable Messaging REST API. The REST API base URL is https://api.twilio.com/2010-04-01 and All requests use HTTP Basic auth with Account SID and Auth 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 Whatsapp-notifications data in under 10 minutes.

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What data can I load from Whatsapp-notifications?

Here are some of the endpoints you can load from Whatsapp-notifications:

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How do I authenticate with the Whatsapp-notifications API?

Use HTTP Basic authentication where the username is your Twilio Account SID and the password is your Twilio Auth Token. Set Authorization: Basic base64(AccountSid:AuthToken) or use client libraries which handle auth.

1. Get your credentials

1. Sign in to https://www.twilio.com/console.
2. Go to Project Info (Console dashboard) to copy your Account SID and Auth Token.
3. If you don’t have WhatsApp access, enable the WhatsApp Sandbox under Messaging > Try it Out > WhatsApp or onboard a WhatsApp Sender under Messaging > Senders.

2. Add them to .dlt/secrets.toml

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[sources.whatsapp_notifications_source]
account_sid = "ACxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
auth_token = "your_auth_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 Whatsapp-notifications 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 whatsapp_notifications_pipeline.py

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

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Pipeline whatsapp_notifications_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset whatsapp_notifications_data
The duckdb destination used duckdb:/whatsapp_notifications.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 whatsapp_notifications_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 messages and senders from the Whatsapp-notifications 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 whatsapp_notifications_source(account_sid, auth_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.twilio.com/2010-04-01",
            "auth": {
                "type": "http_basic",
                "password": account_sid, auth_token,
            },
        },
        "resources": [
            {"name": "messages", "endpoint": {"path": "2010-04-01/Accounts/{AccountSid}/Messages.json", "data_selector": "messages"}},
            {"name": "media", "endpoint": {"path": "2010-04-01/Accounts/{AccountSid}/Messages/{MessageSid}/Media.json", "data_selector": "media"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="whatsapp_notifications_pipeline",
        destination="duckdb",
        dataset_name="whatsapp_notifications_data",
    )
    load_info = pipeline.run(whatsapp_notifications_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("whatsapp_notifications_pipeline").dataset()
sessions_df = data.messages.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM whatsapp_notifications_data.messages LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("whatsapp_notifications_pipeline").dataset()
data.messages.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 Whatsapp-notifications 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 Unauthorized, verify you are using your Account SID as username and Auth Token as password. Ensure the Auth Token has not been rotated and that Basic auth header is correctly formed. Use Twilio Console to regenerate Auth Token if compromised.

Rate limits and 429 responses

Twilio returns 429 Too Many Requests when you exceed API rate limits. Implement exponential backoff and retry logic. For high‑throughput WhatsApp messaging, contact Twilio sales/support to request rate limit increases or use Messaging Services to distribute traffic.

Template (Content) errors and template statuses

Sending templated WhatsApp messages requires approved templates (Content Templates). Unapproved, paused or disabled templates will cause message send failures; Twilio provides error codes for template rejection/paused/disabled (see console alerts: errors 63040–63046). Monitor template status in the Console Content Template Builder or via the Content API.

Common message errors

Twilio message errors are returned with an HTTP error code and an error_code/error_message in responses and status callback. Examples: invalid 'To' number format, sandbox not enabled for the recipient, template not approved, or insufficient permissions. Check the message resource fields 'error_code' and 'error_message' for details and consult Twilio error docs.

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