IBM Maximo Python API Docs | dltHub

IBM Maximo Manage is an enterprise asset-management platform whose REST APIs expose Maximo artifacts such as integration object structures, system APIs, and MBO-backed resources. The REST API base URL is https://<your-maximo-host>/maximo/oslc and API-key authentication is supported; the key is supplied on each request.

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 IBM Maximo data in under 10 minutes.

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What data can I load from IBM Maximo?

Here are some of the endpoints you can load from IBM Maximo:

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How do I authenticate with the IBM Maximo API?

Send the generated API key in the apikey request header; IBM also documents an apikey query parameter, but recommends the header for security. The documented request form is GET /oslc/os/mxapiasset with apikey: (or ?apikey=). Maximo deployments may alternatively use native MAXAUTH with base64-encoded userid

, BASIC authentication, LDAP, or FORM authentication, so the exact mechanism is deployment-dependent. The API root may be /oslc or /api; /api is an alternate route documented for API-key calls in configurations where the route is enabled. No universal hostname can be supplied because it is deployment-specific.

1. Get your credentials

To obtain API credentials for IBM Maximo, you can use either the administrative dashboard or the REST API itself. 1. Via Dashboard: Log in to the Maximo Application Suite or Maximo Manage interface. Navigate to the 'API keys' application (often found in the side navigation menu). Click 'Create API key', select the target user, define the description and expiration settings, and submit. Copy the generated authentication token immediately, as it cannot be recovered once the dialog is closed. 2. Via REST API: If you have an existing administrative API key, you can programmatically generate keys for other users by sending a POST request to the /oslc/os/mxapiapikey endpoint with the target user's ID and desired expiration in the request body. Alternatively, a user can generate their own key by calling POST /oslc/apitoken/create while authenticated.

2. Add them to .dlt/secrets.toml

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[sources.ibm_maximo_source]
apikey = "your_generated_api_key_here"
maximo_base_url = "https://your-maximo-instance.com"

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 IBM Maximo 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 ibm_maximo_pipeline.py

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

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Pipeline ibm_maximo_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset ibm_maximo_data
The duckdb destination used duckdb:/ibm_maximo.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 /oslc/whoami and /oslc/systeminfo from the IBM Maximo 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 ibm_maximo_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<your-maximo-host>/maximo/oslc",
            "auth": {"type": "api_key", "api_key": api_key},
        },
        "resources": [
            {"name": "object_structures", "endpoint": {"path": "oslc/os/{object_structure}", "data_selector": "member"}},
            {"name": "asset_collection", "endpoint": {"path": "oslc/os/mxapiasset", "data_selector": "member"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ibm_maximo_pipeline",
        destination="duckdb",
        dataset_name="ibm_maximo_data",
    )
    load_info = pipeline.run(ibm_maximo_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("ibm_maximo_pipeline").dataset()
sessions_df = data.object_structures.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM ibm_maximo_data.object_structures LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("ibm_maximo_pipeline").dataset()
data.object_structures.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 IBM Maximo 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