Load Openai Admin data to DuckDB

OpenAI Administration API allows management of organization resources such as users, invites, projects, API keys, and audit logs. Everything needed to build a working Openai Admin → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Openai Admin to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Openai Admin to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Openai Admin API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Openai Admin API at a glance

These values come from the Openai Admin API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Openai Admin API?

All requests require HTTP Bearer authentication using an Admin API key. Include the 'Authorization: Bearer <ADMIN_API_KEY>' header in every request.

1. Get your credentials

To obtain an OpenAI API key, navigate to the OpenAI platform dashboard at https://platform.openai.com/settings/organization/api-keys. Sign in with your OpenAI account. Once logged in, click on the 'Create new secret key' button. Provide a name for the key if desired and ensure you copy the generated key immediately, as it will not be displayed again for security reasons. Store the key in a secure location, such as an environment variable or a secret management service.

2. Add them to .dlt/secrets.toml

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[sources.openai_admin_source]
api_key = "sk-..."

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Openai Admin data can I load into DuckDB?

These are the Openai Admin endpoints dlt can load into DuckDB:

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How do I load only new Openai Admin records?

Openai Admin exposes next on organization/groups/{group_id}/users, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "group_users", "endpoint": {
    "path": "organization/groups/{group_id}/users",
    "data_selector": "data",
    "incremental": {"cursor_path": "next", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Openai Admin pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading models and chat/completions from the Openai Admin API into DuckDB:

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

@dlt.source
def openai_admin_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.openai.com/v1",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "group_users", "endpoint": {"path": "organization/groups/{group_id}/users", "data_selector": "data"}},
            {"name": "audit_logs", "endpoint": {"path": "organization/audit_logs", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_openai_admin_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="openai_admin_pipeline",
        destination="duckdb",
        dataset_name="openai_admin_data",
    )
    load_info = pipeline.run(openai_admin_source())
    print(load_info)


if __name__ == "__main__":
    load_openai_admin_to_duckdb()

Run it with python openai_admin_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Openai Admin data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("openai_admin_pipeline").dataset()
df = data.group_users.df()
print(df.head())

SQL:

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SELECT * FROM openai_admin_data.group_users LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Openai Admin to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Openai Admin loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Openai Admin data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack