Xero Python API Docs | dltHub

Xero is a cloud‑based accounting platform that exposes accounting functions via a REST API. The REST API base URL is https://api.xero.com/api.xro/2.0 and All requests require an OAuth 2.0 Bearer token and the Xero‑tenant‑id 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 Xero data in under 10 minutes.

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What data can I load from Xero?

Here are some of the endpoints you can load from Xero:

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How do I authenticate with the Xero API?

Xero requires OAuth 2.0. Obtain an access token via the authorization code flow and send it in the Authorization: Bearer <token> header; include xero-tenant-id header for the Accounting API.

1. Get your credentials

1. Log in to the Xero Developer portal (developer.xero.com).
2. Click My Apps and choose New App.
3. Fill in the app name, company, and redirect URI.
4. After creation, note the Client ID and Client Secret displayed on the app details page.
5. Configure the required scopes (e.g., accounting.transactions, accounting.settings).
6. Use the client credentials in your OAuth 2.0 flow to obtain an access token.
7. Store the token securely for use in API calls.

2. Add them to .dlt/secrets.toml

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[sources.xero_finance_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_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 Xero 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 xero_finance_pipeline.py

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

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Pipeline xero_finance_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset xero_finance_data
The duckdb destination used duckdb:/xero_finance.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 xero_finance_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 contacts and invoices from the Xero 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 xero_finance_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.xero.com/api.xro/2.0",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "Contacts", "data_selector": "Contacts"}},
            {"name": "invoices", "endpoint": {"path": "Invoices", "data_selector": "Invoices"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM xero_finance_data.contacts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("xero_finance_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 Xero 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 errors

• 401 Unauthorized – Occurs when the Bearer token is missing, expired, or invalid. Refresh the access token using the OAuth flow.
• 403 Forbidden – Returned when the token lacks required scopes or the xero-tenant-id header is missing/incorrect.

Rate limiting

• 429 Too Many Requests – Xero imposes a limit of 60 calls per minute per tenant. Respect the Retry-After header and implement exponential back‑off.

Pagination quirks

• Use the page query parameter (e.g., ?page=1) to navigate pages. The response includes a Pagination object with page, pageSize, pageCount, and itemCount. Ensure you iterate until page equals pageCount.

General errors

• 400 Bad Request – Invalid parameters or malformed request bodies.
• 500–504 Server errors – Transient issues; retry after a short delay.

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