Python Code Executor

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

Quick Start

Requires inference.sh CLI (infsh ). Get installation instructions: npx skills add inference-sh/skills@agent-tools

infsh login

Run Python code

infsh 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

infsh 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

infsh 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

infsh 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

infsh 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

infsh 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

infsh 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)

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

High memory (16GB RAM) for large datasets

infsh 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 - How to run apps via CLI

• App Code - Understanding app execution

• Sandboxed Code Execution - Safe code execution for agents