Samanage Python API Docs | dltHub

Samanage is a cloud-based IT service management (Service Desk) platform that exposes a REST API for managing incidents, changes, assets, users and related ITSM resources. The REST API base URL is For US: https://api.samanage.com For EU: https://apieu.samanage.com For APJ: https://apiau.samanage.com and All requests require a token supplied in the X-Samanage-Authorization header as a Bearer token..

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 Samanage data in under 10 minutes.

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What data can I load from Samanage?

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

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How do I authenticate with the Samanage API?

Authentication is token-based. Include header: X-Samanage-Authorization: Bearer YOUR_API_TOKEN. Include Accept header with API version e.g. Accept: application/vnd.samanage.v2.1+json.

1. Get your credentials

1. Log into the Samanage / SolarWinds Service Desk web console as an admin. 2) Open the user setup / profile page for the account that will own API access. 3) Generate or regenerate the API token from the user setup page (note: regenerating invalidates previous tokens). 4) Copy the token and use it in the X-Samanage-Authorization header.

2. Add them to .dlt/secrets.toml

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[sources.samanage_source]
api_token = "your_api_token_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 Samanage 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 samanage_pipeline.py

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

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Pipeline samanage_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset samanage_data
The duckdb destination used duckdb:/samanage.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 samanage_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 incidents and users from the Samanage 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 samanage_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "For US: https://api.samanage.com For EU: https://apieu.samanage.com For APJ: https://apiau.samanage.com",
            "auth": {
                "type": "bearer",
                "api_token": api_token,
            },
        },
        "resources": [
            {"name": "incidents", "endpoint": {"path": "incidents.json", "data_selector": "incidents"}},
            {"name": "users", "endpoint": {"path": "users.json", "data_selector": "users"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="samanage_pipeline",
        destination="duckdb",
        dataset_name="samanage_data",
    )
    load_info = pipeline.run(samanage_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("samanage_pipeline").dataset()
sessions_df = data.incidents.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM samanage_data.incidents LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("samanage_pipeline").dataset()
data.incidents.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 Samanage 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 failures

If you receive 401 Unauthorized: verify X-Samanage-Authorization header is present and formatted exactly: "Bearer " and the token has not been revoked or regenerated. Ensure TLS (https) is used.

Version/Accept header errors

If you receive 406 Not Acceptable, set Accept header to a supported version such as application/vnd.samanage.v2.1+json.

Pagination and rate limits

Large lists are paginated. Check pagination response headers: X-Per-Page, X-Total-Count, X-Total-Pages and Link (rel="first/prev/next/last"); use page and per_page query params. API plans enforce per-minute call limits (e.g. Advanced 1000/min, Premier 1500/min).

Common HTTP errors

400 Bad Request — invalid parameters or payload. 404 Not Found — wrong resource id or path. 500 Server Error — try again or contact support. 429 may be used if rate limits exceeded.

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