Sora CLI
A command-line tool for AI video generation using Sora 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
- Multiple Models — sora-2, sora-2-pro
- Task Management — Query tasks, batch query, wait with polling
- Rich Output — Beautiful terminal tables and panels via Rich
- JSON Mode — Machine-readable output with
--jsonfor piping
Quick Start
1. Get API Token
Get your API token from AceDataCloud Platform:
- Sign up or log in
- Navigate to the Sora API page
- Click "Acquire" to get your token
2. Install
# Install with pip
pip install sora-pro-cli
# Or with uv (recommended)
uv pip install sora-pro-cli
# Or from source
git clone https://github.com/AceDataCloud/SoraCli.git
cd SoraCli
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
sora generate "A test video"
# Generate from reference image
sora image-to-video "Animate this scene" -i https://example.com/photo.jpg
# Check task status
sora task <task-id>
# Wait for completion
sora wait <task-id> --interval 5
# List available models
sora models
Commands
| Command | Description |
|---|---|
sora generate <prompt> |
Generate a video from a text prompt |
sora image-to-video <prompt> -i <url> |
Generate a video from reference image(s) |
sora task <task_id> |
Query a single task status |
sora tasks <id1> <id2>... |
Query multiple tasks at once |
sora wait <task_id> |
Wait for task completion with polling |
sora models |
List available Sora models |
sora config |
Show current configuration |
sora orientations |
List available orientations |
sora sizes |
List available video sizes |
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 Sora model version (default: sora-2)
--duration CHOICE Duration in seconds: 4, 8, 10, 12, 15, 25 (default: 15)
--size TEXT Video size (default: small). Use pixel resolutions for --version 2.0
--version TEXT API version: 1.0 or 2.0 (default: 1.0)
Available Models
| Model | Version | Notes |
|---|---|---|
sora-2 |
Standard | Fast generation, good quality (default) |
sora-2-pro |
Pro | Highest quality, more detailed, longer duration support |
Available Sizes
| Size | API Version | Description |
|---|---|---|
small |
1.0 | Standard definition (default) |
large |
1.0 | HD — sora-2-pro only |
720x1280 |
2.0 | Portrait HD resolution |
1280x720 |
2.0 | Landscape HD resolution |
1024x1792 |
2.0 | Portrait Full HD resolution |
1792x1024 |
2.0 | Landscape Full HD resolution |
Available Durations
Valid duration values (seconds): 4, 8, 10, 12, 15, 25
- Version 1.0: 10 or 15s (sora-2); 10, 15, or 25s (sora-2-pro). Default: 15
- Version 2.0: 4, 8, or 12s. Default: 4
Configuration
Environment Variables
| Variable | Description | Default |
|---|---|---|
ACEDATACLOUD_API_TOKEN |
API token from AceDataCloud | Required |
ACEDATACLOUD_API_BASE_URL |
API base URL | https://api.acedata.cloud |
SORA_DEFAULT_MODEL |
Default model | sora-2 |
SORA_REQUEST_TIMEOUT |
Timeout in seconds | 1800 |
Development
Setup Development Environment
git clone https://github.com/AceDataCloud/SoraCli.git
cd SoraCli
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev,test]"
Run Tests
pytest
pytest --cov=sora_cli
pytest tests/test_integration.py -m integration
Code Quality
ruff format .
ruff check .
mypy sora_cli
Docker
docker pull ghcr.io/acedatacloud/sora-pro-cli:latest
docker run --rm -e ACEDATACLOUD_API_TOKEN=your_token \
ghcr.io/acedatacloud/sora-pro-cli generate "A test video"
Project Structure
SoraCli/
├── sora_cli/ # Main package
│ ├── __init__.py
│ ├── __main__.py # python -m sora_cli entry point
│ ├── main.py # CLI entry point
│ ├── core/ # Core modules
│ │ ├── client.py # HTTP client for Sora 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
Sora CLI vs Sora MCP
| Feature | Sora CLI | Sora MCP |
|---|---|---|
| Interface | Terminal commands | MCP protocol |
| Usage | Direct shell, scripts, CI/CD | Claude, VS Code, MCP clients |
| Output | Rich tables / JSON | Structured MCP responses |
| Automation | Shell scripts, piping | AI agent workflows |
| Install | pip install sora-pro-cli |
pip install mcp-sora |
Both tools use the same AceDataCloud API and share the same API token.
Contributing
Contributions are welcome! Please:
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing) - 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.
Metadata
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