Divio Cloud Python API Docs | dltHub

Divio Cloud API is a REST API for managing Divio Cloud applications, environments, deployments, domains, repositories, service instances and related resources. The REST API base URL is https://api.divio.com/apps/v3/ and all requests requiring authentication use a Token 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 Divio Cloud data in under 10 minutes.

---

What data can I load from Divio Cloud?

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

---

How do I authenticate with the Divio Cloud API?

Include the access token in the Authorization header as: "Authorization: Token YOUR_TOKEN". Access tokens are obtained from the Divio Control Panel access token page and must be sent with each authenticated request.

1. Get your credentials

1. Log in to https://control.divio.com. 2) Open Account > Desktop app / Access token (Control Panel access token) or go to https://control.divio.com/account/desktop-app/access-token/. 3) Create/copy the access token. 4) Store securely; send as header Authorization: Token <YOUR_TOKEN>.

2. Add them to .dlt/secrets.toml

Copy
[sources.divio_cloud_source]
api_token = "your_divio_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Divio 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:

Copy
python divio_cloud_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline divio_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.

---

Python pipeline example

This example loads applications and environments from the Divio Cloud 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 divio_cloud_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.divio.com/apps/v3/",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "applications", "endpoint": {"path": "applications/", "data_selector": "results"}},
            {"name": "environments", "endpoint": {"path": "environments/", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="divio_cloud_pipeline",
        destination="duckdb",
        dataset_name="divio_cloud_data",
    )
    load_info = pipeline.run(divio_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.

---

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("divio_cloud_pipeline").dataset()
sessions_df = data.applications.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM divio_cloud_data.applications LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("divio_cloud_pipeline").dataset()
data.applications.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 Divio 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.

---

Troubleshooting

Authentication failures

If you receive "Authentication credentials were not provided." or HTTP 401/403, confirm the Authorization header is set exactly to "Authorization: Token YOUR_TOKEN". Ensure you are not accidentally using netrc or environment-trusted credentials (requests.Session.trust_env) during testing.

Pagination

List endpoints return paginated responses with the structure {"count", "next", "previous", "results"}. Loop or follow the "next" URL to retrieve subsequent pages; the records array is in the "results" key.

Rate limits and errors

API endpoints return standard HTTP status codes. 401 for unauthorized, 403 for forbidden (e.g. insufficient permissions), 404 for missing resources. Some actions may return 400 with details. Check response body for error 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.

---

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.

Copy
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