Ask Sage Python API Docs | dltHub

Sage Business Cloud Accounting API provides programmatic access to financial and accounting data within Sage Business Cloud products. The REST API base URL is https://api.accounting.sage.com and all requests require a Bearer token in the Authorization header along with specific headers for site and subscription key identification.

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 Ask Sage data in under 10 minutes.

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What data can I load from Ask Sage?

Here are some of the endpoints you can load from Ask Sage:

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How do I authenticate with the Ask Sage API?

Sage Business Cloud Accounting API uses OAuth 2.0. Requests require an Authorization header with a Bearer token, an X-Site header for the resource owner ID, and an ocp-apim-subscription-key for the developer subscription ID.

1. Get your credentials

To obtain credentials for the Sage Intacct REST API, you must register your application via the Sage Intacct Developer Portal. Once registered, you will receive a Client ID and Client Secret. Additionally, you must authorize this Client ID within your company's Sage Intacct instance by navigating to Company > Security > Authorized client applications and adding the Client ID. For service-to-service communication, ensure you have a Web Services user ID associated with the application. If your specific setup does not involve self-service registration, you may need to contact Sage Intacct support or your partner to receive these OAuth 2.0 credentials.

2. Add them to .dlt/secrets.toml

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[sources.ask_sage_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
username = "your_web_services_user_id"
company_id = "your_company_id"

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 Ask Sage 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 ask_sage_pipeline.py

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

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Pipeline ask_sage_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset ask_sage_data
The duckdb destination used duckdb:/ask_sage.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 /oauth2/authorize and /oauth2/token from the Ask Sage 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 ask_sage_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.accounting.sage.com",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "contacts", "data_selector": "contacts"}},
            {"name": "sales_invoices", "endpoint": {"path": "sales_invoices", "data_selector": "sales_invoices"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ask_sage_pipeline",
        destination="duckdb",
        dataset_name="ask_sage_data",
    )
    load_info = pipeline.run(ask_sage_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("ask_sage_pipeline").dataset()
sessions_df = data.contacts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM ask_sage_data.contacts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("ask_sage_pipeline").dataset()
data.contacts.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 Ask Sage 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