WHAPI Cloud Python API Docs | dltHub

WHAPI Cloud is a RESTful service that provides an API to interact with WhatsApp for sending and receiving messages, automations, groups, channels, and related features. The REST API base URL is https://api.whapi.cloud and All requests require a Bearer token supplied in the 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 WHAPI Cloud data in under 10 minutes.

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What data can I load from WHAPI Cloud?

Here are some of the endpoints you can load from WHAPI Cloud:

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How do I authenticate with the WHAPI Cloud API?

Authentication uses a Bearer token; include an Authorization header with "Bearer " on all requests.

1. Get your credentials

1. Register an account at panel.whapi.cloud/register. 2) Pair a WhatsApp number in the dashboard. 3) Copy the API token from the dashboard or the "Get API token" section. 4) Use that token in the Authorization: Bearer header.

2. Add them to .dlt/secrets.toml

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[sources.whapi_cloud_source]
api_token = "your_whapi_bearer_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 WHAPI Cloud 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 whapi_cloud_pipeline.py

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

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Pipeline whapi_cloud_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset whapi_cloud_data
The duckdb destination used duckdb:/whapi_cloud.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 whapi_cloud_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 contacts from the WHAPI Cloud 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 whapi_cloud_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.whapi.cloud",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "messages", "endpoint": {"path": "messages", "data_selector": "data"}},
            {"name": "contacts", "endpoint": {"path": "contacts", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM whapi_cloud_data.messages LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("whapi_cloud_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 WHAPI Cloud 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 401/403 responses, verify your Authorization header is present and uses the exact format "Authorization: Bearer " and that the token copied from the dashboard is not expired or revoked. Use the dashboard to regenerate tokens if needed.

Rate limits and delivery statuses

The gateway returns immediate send responses with a message id and Pending status; final delivery statuses (Delivered/Read) are delivered via configured webhooks. If you hit rate limits, observe 429 responses and back off; reattempt with exponential backoff.

Pagination and data selectors

Most GET list endpoints return an object with a data key containing the records array (data selector = "data"). Some endpoints (health) return top-level values (no data key). When paginated, use standard query parameters as documented in the API reference (page/limit or similar in query string).

Common API errors

• 401 Unauthorized: missing or invalid Bearer token.
• 403 Forbidden: insufficient permission or token not authorized for resource.
• 429 Too Many Requests: rate limit exceeded.
• 5xx: server errors; retry with backoff.

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