Sage People Python API Docs | dltHub

Sage 300 People API is a RESTful interface for integrating third-party applications to input and manage data within the Sage 300 People system. The REST API base URL is http://{{HostName}}:{{APIPort}} and all requests require a Bearer token obtained from an API Key-based authentication 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 Sage People data in under 10 minutes.

---

What data can I load from Sage People?

Here are some of the endpoints you can load from Sage People:

---

How do I authenticate with the Sage People API?

The API uses OAuth 2.0-style token authentication. Access tokens are obtained via a POST request to a /token endpoint with application/x-www-form-urlencoded credentials and passed in subsequent requests as a Bearer token.

1. Get your credentials

Sage People is built on the Salesforce platform, so you must authenticate using Salesforce OAuth 2.0. To obtain credentials: 1. Log in to your Salesforce organization as an administrator. 2. Navigate to Setup > Apps > App Manager. 3. Click New Connected App. 4. Enter the application details and check Enable OAuth Settings. 5. Provide a Callback URL (or use a placeholder if testing). 6. Add required scopes (at minimum: api, refresh_token, and offline_access). 7. Save the app and wait a few minutes for propagation. 8. Click Manage Consumer Details to view and copy your Consumer Key (Client ID) and Consumer Secret (Client Secret). Additionally, create a dedicated API-only user in Sage People and assign it the required permission sets (e.g., Sage People API permissions) to perform the necessary data operations.

2. Add them to .dlt/secrets.toml

Copy
[sources.sage_people_source]
client_id = "your_consumer_key_here"
client_secret = "your_consumer_secret_here"
username = "api_user_username"
password = "api_user_password"
instance_url = "https://login.salesforce.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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI harness:

Copy
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:

Copy
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 Sage People 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:

Copy
python sage_people_pipeline.py

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

Copy
Pipeline sage_people_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset sage_people_data
The duckdb destination used duckdb:/sage_people.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline sage_people_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.

---

Python pipeline example

This example loads /employees and /absences from the Sage People API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def sage_people_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://{{HostName}}:{{APIPort}}",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "workers", "endpoint": {"path": "api/workers", "data_selector": "items"}},
            {"name": "absences", "endpoint": {"path": "api/absences", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sage_people_pipeline",
        destination="duckdb",
        dataset_name="sage_people_data",
    )
    load_info = pipeline.run(sage_people_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.

---

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):

Copy
import dlt

data = dlt.pipeline("sage_people_pipeline").dataset()
sessions_df = data.workers.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM sage_people_data.workers LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("sage_people_pipeline").dataset()
data.workers.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Sage People 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.

---

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.

Copy
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