ASIC Company Lookup Python API Docs | dltHub

ASIC provides various machine-to-machine (M2M) web services to support business, company, and financial registry searches and lodgements for registered intermediaries and software developers. The REST API base URL is https://www.gateway.asic.gov.au/gateway/ and requests require HTTP Basic Authentication with a username and password issued by ASIC.

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 ASIC Company Lookup data in under 10 minutes.

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What data can I load from ASIC Company Lookup?

Here are some of the endpoints you can load from ASIC Company Lookup:

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How do I authenticate with the ASIC Company Lookup API?

ASIC M2M web services typically require HTTP Basic Authentication using a username and password issued by ASIC upon registration. The authorization information is often embedded within the message payload (e.g., senderId) as specified in the relevant technical Message Implementation Guides.

1. Get your credentials

To obtain access to ASIC's official web services, you must apply to become an ASIC digital service provider. Contact webservices@asic.gov.au to request the application information, specifying which API you require and your purpose for access. Upon approval and registration, ASIC will issue you the necessary credentials (typically a unique username and password or a senderId) and provide access to the required technical specifications, WSDLs, and schema files. Note that ASIC does not provide a self-service dashboard for API key generation; credential management is handled via this formal application process.

2. Add them to .dlt/secrets.toml

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[sources.asic_company_lookup_source]
asic_username = "your_username_here"
asic_password = "your_password_here"
asic_sender_id = "your_sender_id_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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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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:

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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 ASIC Company Lookup 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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uv run python asic_company_lookup_pipeline.py

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

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Pipeline asic_company_lookup_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset asic_company_lookup_data
The duckdb destination used duckdb:/asic_company_lookup.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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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.

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Python pipeline example

This example loads getNni and searchNniName from the ASIC Company Lookup 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 asic_company_lookup_source(username=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.gateway.asic.gov.au/gateway/",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": username},
        },
        "resources": [
            {"name": "nni_details", "endpoint": {"path": "getNni"}},
            {"name": "nni_search", "endpoint": {"path": "searchNniName"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="asic_company_lookup_pipeline",
        destination="duckdb",
        dataset_name="asic_company_lookup_data",
    )
    load_info = pipeline.run(asic_company_lookup_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("asic_company_lookup_pipeline").dataset()
sessions_df = data.getNni.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM asic_company_lookup_data.getNni LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("asic_company_lookup_pipeline").dataset()
data.getNni.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 ASIC Company Lookup 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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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.

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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