360dialog Python API Docs | dltHub

360dialog is a platform providing partner and messaging APIs for managing WhatsApp Business API integration and accounts. The REST API base URL is https://hub.360dialog.io/api/v2 and all requests require an API key in a specific header depending on the API variant (Partner API vs Messaging API).

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 add "dlt[hub]" and start loading 360dialog data in under 10 minutes.

---

What data can I load from 360dialog?

Here are some of the endpoints you can load from 360dialog:

---

How do I authenticate with the 360dialog API?

The Partner API uses an x-api-key header for the recommended API key authentication. The Messaging API uses a D360-API-KEY header for its unique API key.

1. Get your credentials

To obtain API credentials for 360dialog, log in to the 360dialog Partner Hub (https://hub.360dialog.com/) or Client Hub (https://app.360dialog.com/) depending on your access level. Navigate to the 'API Keys' section (or 'API Settings' within a specific channel's details page). Click 'Generate API Key', provide a name if requested, and confirm the creation (you may need to perform an OTP verification). Ensure you copy the key immediately, as it is displayed only once. For Partner API access, keys are managed in the Partner Dashboard under the 'API Keys' tab. For Messaging API access, keys are managed per WhatsApp channel via the Partner Hub or Client Hub.

2. Add them to .dlt/secrets.toml

Copy
[sources._360dialog_source]
# For Partner API access (recommended)
x_api_key = "your_partner_api_key_here" # For Messaging API access (WhatsApp channel specific)
d360_api_key = "your_channel_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv init
uv add "dlt[hub]"

1. Install the dlt AI harness:

Copy
uv run dlthub 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:

Copy
uv run dlthub ai toolkit install rest-api-pipeline

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 360dialog 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:

Copy
uv run python _360dialog_pipeline.py

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

Copy
Pipeline _360dialog_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset _360dialog_data
The duckdb destination used duckdb:/_360dialog.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
uv run dlthub show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

---

Python pipeline example

This example loads https://hub.360dialog.io/api/v2 and https://waba-v2.360dialog.io from the 360dialog API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def _360dialog_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://hub.360dialog.io/api/v2",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "groups", "endpoint": {"path": "groups", "data_selector": "data.groups"}},
            {"name": "channels", "endpoint": {"path": "channels", "data_selector": "channels"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="_360dialog_pipeline",
        destination="duckdb",
        dataset_name="_360dialog_data",
    )
    load_info = pipeline.run(_360dialog_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.

---

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):

Copy
import dlt

data = dlt.pipeline("_360dialog_pipeline").dataset()
sessions_df = data.groups.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM _360dialog_data.groups LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("_360dialog_pipeline").dataset()
data.groups.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load 360dialog 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.

---

Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

Copy
uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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