Ato Gov Au Python API Docs | dltHub

The ATO API Portal provides a gateway for digital service providers to access and develop against various Australian Taxation Office APIs for business reporting and service integration. The REST API base URL is https://apiportal.ato.gov.au/ and All requests require a Bearer token in the Authorization header and an apikey in a custom 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 add "dlt[hub]" and start loading Ato Gov Au data in under 10 minutes.

---

What data can I load from Ato Gov Au?

Here are some of the endpoints you can load from Ato Gov Au:

---

How do I authenticate with the Ato Gov Au API?

The ATO API Gateway requires a two-part authentication mechanism: an access token obtained via OAuth 2.0 (client_credentials flow with signed JWT) must be provided in the 'Authorization' header as a Bearer token, and an 'apikey' (consumer key) must be provided in a custom header.

1. Get your credentials

1. Register on the ATO API Portal using your myID credential. 2. Create a team and then create a team application within that team. 3. Once the team application is created, navigate to the Team apps page and select your application. 4. Your sandbox consumer key (API key) will be displayed there immediately. 5. For production access, complete the security questionnaire and request production approval from the Digital Partnership Office (DPO); once approved, your production consumer key will become available in the same team application view.

2. Add them to .dlt/secrets.toml

Copy
[sources.ato_gov_au_source]
api_key = "REPLACE_ME"

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 Ato Gov Au 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 ato_gov_au_pipeline.py

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

Copy
Pipeline ato_gov_au_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset ato_gov_au_data
The duckdb destination used duckdb:/ato_gov_au.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 The two most common endpoints for OAuth-based interaction are the token endpoint (for requesting access tokens) and the register endpoint (from the OAuth Dynamic Client Registration API). from the Ato Gov Au 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 ato_gov_au_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://apiportal.ato.gov.au/",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "individuals", "endpoint": {"path": "individuals"}},
            {"name": "organisations", "endpoint": {"path": "organisations"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ato_gov_au_pipeline",
        destination="duckdb",
        dataset_name="ato_gov_au_data",
    )
    load_info = pipeline.run(ato_gov_au_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("ato_gov_au_pipeline").dataset()
sessions_df = data.individuals.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM ato_gov_au_data.individuals LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("ato_gov_au_pipeline").dataset()
data.individuals.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 Ato Gov Au 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