Python Executor

> Install the belt CLI skill: npx skills add belt-sh/cli

Python Code Executor

Execute Python code in a safe, sandboxed environment with 100+ pre-installed libraries.

Quick Start

> Requires inference.sh CLI (belt). Install instructions

belt login

Run Python code

belt app run infsh/python-executor --input '{ code: import pandas as pd\nprint(pd.version) }'

App Details

Property	Value
App ID	infsh/python-executor
Environment	Python 3.10, CPU-only
RAM	8GB (default) / 16GB (high_memory)
Timeout	1-300 seconds (default: 30)

Input Schema

{
  "code": "print('Hello World!')",
  "timeout": 30,
  "capture_output": true,
  "working_dir": null
}

## Pre-installed Libraries

### Web Scraping & HTTP

- `requests`, `httpx`, `aiohttp` - HTTP clients

- `beautifulsoup4`, `lxml` - HTML/XML parsing

- `selenium`, `playwright` - Browser automation

- `scrapy` - Web scraping framework

### Data Processing

- `numpy`, `pandas`, `scipy` - Numerical computing

- `matplotlib`, `seaborn`, `plotly` - Visualization

### Image Processing

- `pillow`, `opencv-python-headless` - Image manipulation

- `scikit-image`, `imageio` - Image algorithms

### Video & Audio

- `moviepy` - Video editing

- `av` (PyAV), `ffmpeg-python` - Video processing

- `pydub` - Audio manipulation

### 3D Processing

- `trimesh`, `open3d` - 3D mesh processing

- `numpy-stl`, `meshio`, `pyvista` - 3D file formats

### Documents & Graphics

- `svgwrite`, `cairosvg` - SVG creation

- `reportlab`, `pypdf2` - PDF generation

## Examples

### Web Scraping

belt app run infsh/python-executor --input '{ code: import requests\nfrom bs4 import BeautifulSoup\n\nresponse = requests.get(\https://example.com\)\nsoup = BeautifulSoup(response.content, \html.parser\)\nprint(soup.find(\title\).text) }'

### Data Analysis with Visualization

belt app run infsh/python-executor --input '{ code: import pandas as pd\nimport matplotlib.pyplot as plt\n\ndata = {\name\: [\Alice\, \Bob\], \sales\: [100, 150]}\ndf = pd.DataFrame(data)\n\nplt.bar(df[\name\], df[\sales\])\nplt.savefig(\outputs/chart.png\)\nprint(\Chart saved!\) }'

### Image Processing

belt app run infsh/python-executor --input '{ code: from PIL import Image\nimport numpy as np\n\n# Create gradient image\narr = np.linspace(0, 255, 256*256, dtype=np.uint8).reshape(256, 256)\nimg = Image.fromarray(arr, mode=\L\)\nimg.save(\outputs/gradient.png\)\nprint(\Image created!\) }'

### Video Creation

belt app run infsh/python-executor --input '{ code: from moviepy.editor import ColorClip, TextClip, CompositeVideoClip\n\nclip = ColorClip(size=(640, 480), color=(0, 100, 200), duration=3)\ntxt = TextClip(\Hello!\, fontsize=70, color=\white\).set_position(\center\).set_duration(3)\nvideo = CompositeVideoClip([clip, txt])\nvideo.write_videofile(\outputs/hello.mp4\, fps=24)\nprint(\Video created!\), timeout: 120 }'

### 3D Model Processing

belt app run infsh/python-executor --input '{ code: import trimesh\n\nsphere = trimesh.creation.icosphere(subdivisions=3, radius=1.0)\nsphere.export(\outputs/sphere.stl\)\nprint(f\Created sphere with {len(sphere.vertices)} vertices\) }'

### API Calls

belt app run infsh/python-executor --input '{ code: import requests\nimport json\n\nresponse = requests.get(\https://api.github.com/users/octocat\)\ndata = response.json()\nprint(json.dumps(data, indent=2)) }'

## File Output

Files saved to `outputs/` are automatically returned:

These files will be in the response

plt.savefig('outputs/chart.png') df.to_csv('outputs/data.csv') video.write_videofile('outputs/video.mp4') mesh.export('outputs/model.stl')

## Variants

Default (8GB RAM)

belt app run infsh/python-executor --input input.json

High memory (16GB RAM) for large datasets

belt app run infsh/python-executor@high_memory --input input.json

## Use Cases

- **Web scraping** - Extract data from websites

- **Data analysis** - Process and visualize datasets

- **Image manipulation** - Resize, crop, composite images

- **Video creation** - Generate videos with text overlays

- **3D processing** - Load, transform, export 3D models

- **API integration** - Call external APIs

- **PDF generation** - Create reports and documents

- **Automation** - Run any Python script

## Important Notes

- **CPU-only** - No GPU/ML libraries (use dedicated AI apps for that)

- **Safe execution** - Runs in isolated subprocess

- **Non-interactive** - Use `plt.savefig()` not `plt.show()`

- **File detection** - Output files are auto-detected and returned

## Related Skills

AI image generation (for ML-based images)

npx skills add inference-sh/skills@ai-image-generation

AI video generation (for ML-based videos)

npx skills add inference-sh/skills@ai-video-generation

LLM models (for text generation)

npx skills add inference-sh/skills@llm-models

## Documentation

- [Running Apps](https://inference.sh/docs/apps/running) - How to run apps via CLI

- [App Code](https://inference.sh/docs/extend/app-code) - Understanding app execution

- [Sandboxed Code Execution](https://inference.sh/blog/tools/sandboxed-execution) - Safe code execution for agents