Hexagon PPM Smart API Python API Docs | dltHub

Hexagon PPM Smart API is a family of RESTful OData v4 web APIs used to access data across various Hexagon enterprise software solutions managed through the Smart API Manager platform. The REST API base URL is The base URL is specific to the individual Hexagon Smart API deployment, often following patterns like 'https://<host>/<api-path>/<version>/' or provided via service discovery. 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 pip install "dlt[workspace]" and start loading Hexagon PPM Smart API data in under 10 minutes.

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What data can I load from Hexagon PPM Smart API?

Here are some of the endpoints you can load from Hexagon PPM Smart API:

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How do I authenticate with the Hexagon PPM Smart API API?

All requests to a Smart API require an Authorization header with a Bearer token obtained from a Security Token Service (STS) or Smart API Manager. The header should be formatted as 'Authorization: Bearer <access_token>'.

1. Get your credentials

To obtain credentials for the Hexagon Smart API, you must register your application as a Smart Client within the Smart API Manager (SAM) interface. 1. Log in to the Smart API Manager dashboard with Administrator privileges. 2. Navigate to the Smart Clients section and select the option to register a new client. 3. Provide a name and choose the appropriate authorization flow (e.g., Client Credentials). 4. Upon registration, SAM will generate a Client ID and a Client Secret. Save these credentials securely, as the Client Secret is typically only displayed once. If the selected authorization flow requires it, ensure the client is authorized to access the specific Smart APIs needed.

2. Add them to .dlt/secrets.toml

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[sources.hexagon_ppm_smart_api_source]
token = "REPLACE_ME"

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 harness:

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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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dlthub 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 Hexagon PPM Smart API 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 hexagon_ppm_smart_api_pipeline.py

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

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Pipeline hexagon_ppm_smart_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset hexagon_ppm_smart_api_data
The duckdb destination used duckdb:/hexagon_ppm_smart_api.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 hexagon_ppm_smart_api_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 '/identity/connect/token' (for obtaining access tokens) and the API's base service endpoint (for resource discovery, e.g., '/api/resource'). from the Hexagon PPM Smart API 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 hexagon_ppm_smart_api_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The base URL is specific to the individual Hexagon Smart API deployment, often following patterns like 'https://<host>/<api-path>/<version>/' or provided via service discovery.",
            "auth": {"type": "bearer", "token": token},
        },
        "resources": [
            {"name": "entity_collection", "endpoint": {"path": "{entity_set}", "data_selector": "value"}},
            {"name": "service_document", "endpoint": {"path": "/"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="hexagon_ppm_smart_api_pipeline",
        destination="duckdb",
        dataset_name="hexagon_ppm_smart_api_data",
    )
    load_info = pipeline.run(hexagon_ppm_smart_api_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("hexagon_ppm_smart_api_pipeline").dataset()
sessions_df = data.entity_collection.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM hexagon_ppm_smart_api_data.entity_collection LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("hexagon_ppm_smart_api_pipeline").dataset()
data.entity_collection.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 Hexagon PPM Smart API 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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dlthub ai toolkit data-exploration install
dlthub ai toolkit dlthub-platform 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