OpenAI CLI

A command-line tool for OpenAI-compatible APIs via AceDataCloud.

Installation

pip install openai-pro-cli

Quick Start

1. Get API Token

1. Open Ace Data Cloud → Applications. Sign in, or register an account first.
2. Find General application. If it has not been created, choose Initialize General Application. A general key can use the services available to your account; a dedicated application's key is limited to its service. Check the required service/model access and current pricing.
3. Copy the API Key from the application card, or open Keys / Manage Keys → Create to create a separate key for this integration. Give it a recognizable name so you can revoke or rotate it independently.
4. When creating a key, review Expiration Time, Set Usage Limitation, and Allowed APIs. Enable only the restrictions you need, allow the operations you will call, then click Create and copy the token from the key list. Task lookup does not grant permission to submit a generation. Confirm the application's balance can cover your first request; any trial allowance is whatever your account currently displays.
5. Copy only the token value, without quotation marks, spaces, or Bearer . An API Key / API Token field expects this raw value; only a manually configured HTTP Authorization header needs the Bearer prefix. A platform-... account-management token cannot replace a generation API key.

Save the key in the client's credential store or the environment variable shown below. Never put it in a prompt, shared workflow, screenshot, or Git commit. If authentication fails, check the full copied value, application/service access, expiration, usage limit, and balance before creating another key.

2. Configure

export ACEDATACLOUD_API_TOKEN=your_token_here

Or save it to a .env file:

cp .env.example .env
# Edit .env and set ACEDATACLOUD_API_TOKEN

3. Use

# Chat with a model
openai-cli chat "What is the capital of France?"

# Chat with a specific model
openai-cli chat "Explain quantum computing" -m gpt-5.4

# Generate embeddings
openai-cli embed "Hello, world!" -m text-embedding-3-small

# Generate an image
openai-cli image "A futuristic city skyline at night"

# Edit an image
openai-cli edit "Add a rainbow" --image-url https://example.com/photo.jpg

# Use the Responses API
openai-cli response "Summarize this article" -m gpt-4o

# Synthesize speech audio
openai-cli speech "Hello from AceDataCloud" --voice nova --output hello.mp3

# Show realtime WebSocket connection details
openai-cli realtime --model gpt-realtime

# Retrieve an async task result
openai-cli tasks retrieve --id 7489df4c-ef03-4de0-b598-e9a590793434
openai-cli tasks retrieve --trace-id my-custom-trace-001

# Retrieve a batch of task results
openai-cli tasks batch --trace-ids trace-001 trace-002

# List available models
openai-cli models

# Show configuration
openai-cli config

Commands

Embedding models

embed supports text-embedding-3-small (the default) and text-embedding-3-large. The legacy text-embedding-ada-002 model is retired. When migrating an existing index, regenerate its vectors and rebuild the index; do not mix vectors from the old and new models.

Nano Banana 2.1 images

Use the public nano-banana-2.1 model ID for generation or editing. There is no nano-banana-2.1:official variant; specify the model explicitly to use it instead of the existing default.

openai-cli image "A blue ceramic vase on a cream background" -m nano-banana-2.1 -n 2
openai-cli edit "Make the vase green" --image-url https://example.com/photo.jpg -m nano-banana-2.1
openai-cli edit "Make the vase green" --image-file photo.png -m nano-banana-2.1

Use --image-url for JSON requests or --image-file for multipart uploads, not both. For dedicated 1K/2K/4K resolution controls, use nano-banana-pro generate or nano-banana-pro edit from the NanoBanana CLI; the OpenAI-compatible commands use their own request fields.

Environment Variables

Docker

docker compose run --rm openai-cli chat "Hello!" -m gpt-4o