Infor M3 Python API Docs | dltHub

Infor M3 REST API allows integration with M3 CloudSuite applications through the Infor ION API Gateway using OAuth 2.0 authentication. The REST API base URL is https://{ion-api-gateway-hostname}/{tenant-id}/M3/m3api-rest/v2/execute/ and All requests require an OAuth 2.0 Bearer token obtained from the Infor Authorization Server..

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 M3 data in under 10 minutes.

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What data can I load from Infor M3?

Here are some of the endpoints you can load from Infor M3:

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How do I authenticate with the Infor M3 API?

Authentication requires an OAuth 2.0 Bearer token, which must be included in the Authorization header of all requests as 'Authorization: Bearer '. The token is obtained by authenticating with the Infor Authorization Server using credentials from an .ionapi file.

1. Get your credentials

To obtain API credentials for the Infor M3 REST API, you must create an authorized app within the Infor OS ION API gateway. 1. Log in to the Infor OS portal for your tenant. 2. Navigate to ION API and click the plus (+) icon to create a new authorized app. 3. Set the Type to 'Backend Service' (or as required for your integration), fill in the name and description, and configure token expiration settings. 4. Once saved, locate your newly created app in the 'Authorized Apps' list. 5. Click 'Download Credentials' to obtain the .ionapi file, which contains the essential client_id (ci), client_secret (cs), and token/auth URL components (pu, ot) required for programmatic OAuth2 authentication.

2. Add them to .dlt/secrets.toml

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[sources.infor_m3_source]
client_id = "your_ci_value_from_ionapi_file"
client_secret = "your_cs_value_from_ionapi_file"
token_url = "your_pu_value_plus_ot_value"
service_account_access_key = "your_access_key_if_applicable"
service_account_secret_key = "your_secret_key_if_applicable"

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 M3 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_m3_pipeline.py

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

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Pipeline infor_m3_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset infor_m3_data
The duckdb destination used duckdb:/infor_m3.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 /m3api-rest/execute/{Program}/{Transaction} and /m3api-rest/v2/execute/{Program}/{Transaction} from the Infor M3 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_m3_source(ionapi_credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{ion-api-gateway-hostname}/{tenant-id}/M3/m3api-rest/v2/execute/",
            "auth": {"type": "bearer", "token": ionapi_credentials},
        },
        "resources": [
            {"name": "m3_api_get", "endpoint": {"path": "api/{apiVersion}/{programId}"}},
            {"name": "m3_api_post", "endpoint": {"path": "api/{apiVersion}/{programId}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="infor_m3_pipeline",
        destination="duckdb",
        dataset_name="infor_m3_data",
    )
    load_info = pipeline.run(infor_m3_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_m3_pipeline").dataset()
sessions_df = data.m3_api_get.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM infor_m3_data.m3_api_get LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("infor_m3_pipeline").dataset()
data.m3_api_get.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 M3 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