Infor Baan Python API Docs | dltHub

Infor LN REST API is a framework for OData-based services supporting machine integrations and public REST APIs managed via Infor API Gateway. The REST API base URL is {{baseUrl}} and all requests require a Bearer token obtained via OAuth 2.0 flow.

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 Infor Baan data in under 10 minutes.

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What data can I load from Infor Baan?

Here are some of the endpoints you can load from Infor Baan:

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How do I authenticate with the Infor Baan API?

Authentication is handled via Infor API Gateway using OAuth 2.0. Requests require an Authorization header with a Bearer token.

1. Get your credentials

To obtain API credentials for Infor LN/Baan, you must register the application in the Infor OS API Gateway: 1. Log in to the Infor Ming.le Portal and open the API Gateway application. 2. Ensure you have the IONAPI-Administrator security role. 3. Navigate to the 'Authorized Apps' tab and select 'Add New App'. 4. Provide a name and select the application type (e.g., 'Backend Service'). 5. Configure grant type settings (e.g., 'Password Credentials' or 'Client Credentials'). 6. Save the application; the system will generate a ClientID and Client Secret. 7. Download the credentials as a .ionapi file, which contains the necessary keys and configuration parameters (e.g., ci, cs, saak, sask, pu, ot).

2. Add them to .dlt/secrets.toml

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[sources.infor_baan_source]
tenant_id = "your_tenant_id"
client_id = "your_ci_value"
client_secret = "your_cs_value"
service_account_access_key = "your_saak_value"
service_account_secret_key = "your_sask_value"
auth_token_url = "your_pu_value_plus_ot_path"

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 Infor Baan 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 infor_baan_pipeline.py

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

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Pipeline infor_baan_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset infor_baan_data
The duckdb destination used duckdb:/infor_baan.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 The most commonly used entry points for interacting with the API are accessed via the API Gateway base URL, typically documented as lnapi or specific service-level resource endpoints discovered via the Swagger documentation in the API Gateway. from the Infor Baan 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 infor_baan_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "{{baseUrl}}",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "purchase_orders", "endpoint": {"path": "purchaseorders", "data_selector": "collection"}},
            {"name": "sales_orders", "endpoint": {"path": "salesorders", "data_selector": "collection"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="infor_baan_pipeline",
        destination="duckdb",
        dataset_name="infor_baan_data",
    )
    load_info = pipeline.run(infor_baan_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("infor_baan_pipeline").dataset()
sessions_df = data.purchase_orders.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM infor_baan_data.purchase_orders LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("infor_baan_pipeline").dataset()
data.purchase_orders.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 Infor Baan 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