Openai Admin Python API Docs | dltHub

OpenAI Admin API is used to programmatically manage an organization. The REST API base URL is https://api.openai.com/v1 and All requests require an Admin API key for authentication, provided via HTTP Bearer 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 Openai Admin data in under 10 minutes.

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What data can I load from Openai Admin?

Here are some of the endpoints you can load from Openai Admin:

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How do I authenticate with the Openai Admin API?

Authentication is done using an Admin API key provided in the Authorization header as a Bearer token. For organizations with multiple projects or legacy user API keys, OpenAI-Organization and OpenAI-Project headers may also be required.

1. Get your credentials

To obtain Admin API credentials, you must generate an Admin API Key using the 'create admin API key endpoint'. Admin API keys are specifically for administration endpoints and cannot be used for other API calls.

2. Add them to .dlt/secrets.toml

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[sources.openai_admin_source]
api_key = "your_admin_api_key_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 Openai Admin 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 openai_admin_pipeline.py

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

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Pipeline openai_admin_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset openai_admin_data
The duckdb destination used duckdb:/openai_admin.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 openai_admin_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 admin_api_keys and audit_logs from the Openai Admin 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 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": "admin_api_keys", "endpoint": {"path": "organization/admin_api_keys", "data_selector": "data"}},
            {"name": "audit_logs", "endpoint": {"path": "organization/audit_logs", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> 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)

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("openai_admin_pipeline").dataset()
sessions_df = data.admin_api_keys.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM openai_admin_data.admin_api_keys LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("openai_admin_pipeline").dataset()
data.admin_api_keys.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 Openai Admin 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

Admin API keys are distinct from regular API keys and can only be used for administration endpoints. Attempting to use an Admin API key for non-administration endpoints will result in authentication failures. Ensure you are using the correct type of API key for the intended endpoint.

Rate Limits

OpenAI APIs are subject to rate limits. Responses include specific HTTP headers that provide information about the current rate limit status:

• x-ratelimit-limit-requests: The maximum number of requests allowed.
• x-ratelimit-remaining-requests: The number of remaining requests before the limit is hit.
• x-ratelimit-reset-requests: The time when the rate limit will reset.

Monitor these headers to manage your request volume and avoid hitting rate limits. Implement retry mechanisms with exponential backoff if you encounter rate limit errors.

Admin Key Scope

Admin API keys are specifically designed for programmatic management of your organization and cannot be used for general API requests (e.g., chat completions, image generation). Using an Admin API key outside its intended scope will lead to authorization errors.

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