Sage-accounting Python API Docs | dltHub

Sage Accounting is a REST API that enables CRUD operations on Sage Business Cloud Accounting data. The REST API base URL is https://accounting.sageone.co.za and All requests require a Bearer access token obtained via OAuth 2.0..

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 Sage-accounting data in under 10 minutes.

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What data can I load from Sage-accounting?

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

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How do I authenticate with the Sage-accounting API?

Include an "Authorization: Bearer <access_token>" header with each request. For endpoints that require it, an API key can be added as a query‑string parameter, and basic webforms authentication uses a Base64‑encoded username:password in the Authorization header.

1. Get your credentials

1. Log in to the Sage Developer Portal.
2. Register a new application to obtain a client ID and client secret.
3. Implement the OAuth 2.0 authorization‑code flow: direct the user to the Sage authorization endpoint, have them approve access, and receive an authorization code.
4. Exchange the authorization code for an access token by calling the token endpoint with the client ID, client secret, and code.
5. Store the returned access token for use in API calls.

2. Add them to .dlt/secrets.toml

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[sources.sage_accounting_source]
api_key = "your_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.

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

1. Install the dlt AI Workbench:

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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:

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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 Sage-accounting 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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python sage_accounting_pipeline.py

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

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Pipeline sage_accounting_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset sage_accounting_data
The duckdb destination used duckdb:/sage_accounting.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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dlt pipeline sage_accounting_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.

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Python pipeline example

This example loads invoices and contacts from the Sage-accounting 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 sage_accounting_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://accounting.sageone.co.za",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "invoices", "endpoint": {"path": "api/v3/invoices", "data_selector": "invoices"}},
            {"name": "contacts", "endpoint": {"path": "api/v3/contacts", "data_selector": "contacts"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sage_accounting_pipeline",
        destination="duckdb",
        dataset_name="sage_accounting_data",
    )
    load_info = pipeline.run(sage_accounting_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("sage_accounting_pipeline").dataset()
sessions_df = data.invoices.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM sage_accounting_data.invoices LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("sage_accounting_pipeline").dataset()
data.invoices.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 Sage-accounting 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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Troubleshooting

Authentication failures

If the access token is missing, expired, or invalid, the API returns a 401 Unauthorized response. Ensure the token is refreshed using the OAuth flow and included in the Authorization: Bearer <token> header.

Rate limits

The API enforces a daily limit of 1,296,000 requests per app and a maximum of 150 concurrent requests (overview) as well as a per‑minute cap of 100 results for list methods (developer page). Exceeding these limits returns a 429 Too Many Requests response. Implement exponential back‑off and respect the Retry-After header.

Pagination

Endpoints that return collections support page and items_per_page query parameters (overview). If not paginated correctly, you may receive incomplete data sets. Use the provided pagination parameters to iterate through all pages.

Request size limits

Responses are wrapped with total counts; large result sets are limited to 100 items per minute (developer page). Adjust items_per_page accordingly or retrieve data in multiple calls.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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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.

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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