Veo CLI

A command-line tool for AI video generation using Veo through the AceDataCloud API.

Generate AI videos directly from your terminal — no MCP client required.

Features

• Video Generation — Generate videos from text prompts with multiple models
• Image-to-Video — Create videos from reference images
• Video Upscale — Get 1080p versions of generated videos
• Multiple Models — veo3, veo3-fast, veo31, veo31-fast, veo31-fast-ingredients
• Task Management — Query tasks, batch query, wait with polling
• Rich Output — Beautiful terminal tables and panels via Rich
• JSON Mode — Machine-readable output with --json for piping

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. Install

# Install with pip
pip install veo-cli

# Or with uv (recommended)
uv pip install veo-cli

# Or from source
git clone https://github.com/AceDataCloud/VeoCli.git
cd VeoCli
pip install -e .

3. Configure

# Set your API token
export ACEDATACLOUD_API_TOKEN=your_token_here

# Or use .env file
cp .env.example .env
# Edit .env with your token

4. Use

# Generate a video
veo generate "A test video"

# Generate from reference image
veo image-to-video "Animate this scene" -i https://example.com/photo.jpg

# Generate from ingredient images (uses veo31-fast-ingredients model)
veo ingredients-to-video "Product showcase" -i https://example.com/product.jpg

# Upscale to 1080p
veo upscale <video-id>

# Check task status
veo task <task-id>

# Wait for completion
veo wait <task-id> --interval 5

# List available models
veo models

Commands

Note: The current Veo API supports upscale as a 1080p action only.

Global Options

--token TEXT    API token (or set ACEDATACLOUD_API_TOKEN env var)
--version       Show version
--help          Show help message

Most commands support:

--json                        Output raw JSON (for piping/scripting)
--model TEXT                  Veo model version (default: veo3)
--resolution TEXT             Output resolution: 4k, 1080p, gif (generate and image-to-video)
--translation/--no-translation  Enable automatic prompt translation (generate and image-to-video)

Available Models

Configuration

Environment Variables

Development

Setup Development Environment

git clone https://github.com/AceDataCloud/VeoCli.git
cd VeoCli
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev,test]"

Run Tests

pytest
pytest --cov=veo_cli
pytest tests/test_integration.py -m integration

Code Quality

ruff format .
ruff check .
mypy veo_cli

Docker

docker pull ghcr.io/acedatacloud/veo-cli:latest
docker run --rm -e ACEDATACLOUD_API_TOKEN=your_token \
  ghcr.io/acedatacloud/veo-cli generate "A test video"

Project Structure

VeoCli/
├── veo_cli/                # Main package
│   ├── __init__.py
│   ├── __main__.py            # python -m veo_cli entry point
│   ├── main.py                # CLI entry point
│   ├── core/                  # Core modules
│   │   ├── client.py          # HTTP client for Veo API
│   │   ├── config.py          # Configuration management
│   │   ├── exceptions.py      # Custom exceptions
│   │   └── output.py          # Rich terminal formatting
│   └── commands/              # CLI command groups
│       ├── video.py           # Video generation commands
│       ├── task.py            # Task management commands
│       └── info.py            # Info & utility commands
├── tests/                     # Test suite
├── .github/workflows/         # CI/CD (lint, test, publish to PyPI)
├── Dockerfile                 # Container image
├── deploy/                    # Kubernetes deployment configs
├── .env.example               # Environment template
├── pyproject.toml             # Project configuration
└── README.md

Veo CLI vs MCP Veo

Both tools use the same AceDataCloud API and share the same API token.

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing)
5. Open a Pull Request

Development Requirements

• Python 3.10+
• Dependencies: pip install -e ".[all]"
• Lint: ruff check . && ruff format --check .
• Test: pytest

License

This project is licensed under the MIT License — see the LICENSE file for details.