Jamf Pro Python API Docs | dltHub

Jamf Pro is an enterprise Apple device management platform providing device inventory, management, and policy automation via a REST API. The REST API base URL is https://{your-instance}.jamfcloud.com/api and All requests require a Bearer token for authentication..

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 Jamf Pro data in under 10 minutes.

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What data can I load from Jamf Pro?

Here are some of the endpoints you can load from Jamf Pro:

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How do I authenticate with the Jamf Pro API?

Obtain a Bearer token by POSTing Basic auth (username:password) to /v1/auth/token; include the returned token in the Authorization header as "Authorization: Bearer TOKEN".

1. Get your credentials

1. In Jamf Pro, create or identify a user account with API privileges. 2) Navigate to Settings → System Settings → Jamf Pro User Accounts & Groups (or Administrator Accounts) and create the user. 3) Grant the minimal required API privileges for the endpoints your pipeline will use. 4) Use that username and password with Basic auth to POST to /v1/auth/token and retrieve the token.

2. Add them to .dlt/secrets.toml

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[sources.jamf_pro_source]
jamf_token = "your_jamf_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 Jamf Pro 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 jamf_pro_pipeline.py

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

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Pipeline jamf_pro_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset jamf_pro_data
The duckdb destination used duckdb:/jamf_pro.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 jamf_pro_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 computers_inventory and policies from the Jamf Pro 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 jamf_pro_source(jamf_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{your-instance}.jamfcloud.com/api",
            "auth": {
                "type": "bearer",
                "token": jamf_token,
            },
        },
        "resources": [
            {"name": "computers_inventory", "endpoint": {"path": "v1/computers-inventory", "data_selector": "results"}},
            {"name": "policies", "endpoint": {"path": "v1/policies", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="jamf_pro_pipeline",
        destination="duckdb",
        dataset_name="jamf_pro_data",
    )
    load_info = pipeline.run(jamf_pro_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("jamf_pro_pipeline").dataset()
sessions_df = data.computers_inventory.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM jamf_pro_data.computers_inventory LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("jamf_pro_pipeline").dataset()
data.computers_inventory.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 Jamf Pro 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 POST /v1/auth/token returns 401/403, verify the username/password are correct and the account has API privileges. Confirm you are using Basic auth for the token request and include the returned token in subsequent requests as "Authorization: Bearer TOKEN".

Rate limits and session stickiness

Jamf Cloud may be load‑balanced; use documented session stickiness guidance for sequences of related writes. Some Jamf Cloud deployments enforce rate limits—if you see 429, implement exponential backoff and respect pagination to reduce load.

Pagination and filtering quirks

List endpoints return paginated results under the "results" key. Use page and page-size query params. For computers‑inventory, include section=... query params to request specific sections (defaults to GENERAL). Filtering uses RSQL (e.g., filter=hardware.serialNumber==SERIAL).

Common API errors (examples): 401 Unauthorized - invalid token/credentials; 403 Forbidden - insufficient privileges; 404 Not Found - invalid resource or id; 429 Too Many Requests - rate limiting; 500/502/503 - server errors (retry/backoff).

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