ServiceNow Python API Docs | dltHub

ServiceNow is a cloud‑based platform for IT service management (ITSM) and enterprise service workflows, exposing REST APIs to query and modify records across its tables. The REST API base URL is https://{instance}.service-now.com and Supports Basic auth (username/password) and OAuth 2.0 (Bearer tokens); all requests must be authenticated..

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 ServiceNow data in under 10 minutes.

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What data can I load from ServiceNow?

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

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How do I authenticate with the ServiceNow API?

ServiceNow REST APIs accept HTTP Basic Authentication (instance username and password) or OAuth 2.0 bearer tokens. Include credentials in the Authorization header (Basic base64(user:pass) or Bearer <access_token>); also set Accept: application/json and Content-Type: application/json for JSON responses.

1. Get your credentials

1. Basic auth: Create or use an existing ServiceNow user with API access (create a dedicated integration user in Users > Users, assign roles like web_service_admin or appropriate table access). Use that username and password in requests.
2. OAuth 2.0: In ServiceNow, create an OAuth application registry entry (System OAuth > Application Registry > New > Create an OAuth API endpoint for external clients). Save client_id and client_secret. Use the token endpoint on your instance (/oauth_token.do) to obtain an access token using client_credentials or password grant as configured.

2. Add them to .dlt/secrets.toml

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[sources.service_now_source]
username = "your_servicenow_user"
password = "your_password"
# OR for OAuth
oauth_client_id = "your_client_id"
oauth_client_secret = "your_client_secret"
oauth_token = "your_access_token"

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 ServiceNow 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 service_now_pipeline.py

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

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Pipeline service_now_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset service_now_data
The duckdb destination used duckdb:/service_now.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 service_now_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 ServiceNow 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 service_now_source(username=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{instance}.service-now.com",
            "auth": {
                "type": "http_basic",
                "password": username,
            },
        },
        "resources": [
            {"name": "incidents", "endpoint": {"path": "api/now/table/incident", "data_selector": "result"}},
            {"name": "users", "endpoint": {"path": "api/now/table/sys_user", "data_selector": "result"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM service_now_data.incidents LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("service_now_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 ServiceNow 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

401 Unauthorized or 403 Forbidden: verify Basic credentials or OAuth token; ensure integration user has required roles and that OAuth client is correctly configured. For Basic auth confirm the user password isn't expired and account isn't locked.

Rate limiting and throttling

ServiceNow enforces rate limits (per‑instance and per‑user). If you receive 429 Too Many Requests or intermittent 503s, implement exponential backoff and respect sysparm_limit to reduce page size.

Pagination quirks

Table API uses sysparm_limit and sysparm_offset (or sysparm_query and encoded query). Responses return partial result sets; use sysparm_offset to page through records. When using large datasets prefer incremental sync via sys_updated_on and sysparm_query conditions.

Common errors

400 Bad Request: malformed query parameters or invalid sysparm_query. 404 Not Found: incorrect endpoint or table name. 415 Unsupported Media Type: missing Content-Type: application/json header.

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