whosat 0.1.0

Terminal dashboard for ports, processes, and Docker containers

A terminal dashboard for ports, processes, and Docker containers.
Like htop but focused on network ports.

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Built with Textual for a rich TUI experience.

Features

• Port & Process Monitoring -- See all listening ports with owning processes, CPU/memory usage, and uptime
• Memory View -- System-wide process memory usage with GPU memory tracking (NVIDIA)
• Docker Integration -- Optional container monitoring alongside system processes
• Group & Flat Views -- Group processes by name or view as a flat list
• Detail Panel -- Expand any process to see full network info, open ports, resource usage, and CPU sparkline
• Actions -- Ping, curl, and kill processes directly from the TUI
• Live Refresh -- Configurable auto-refresh (15s / 30s / 1m / 2m / off)
• Theming -- 6 built-in themes (matrix, nord, dracula, tokyo-night, gruvbox, solarized)
• Search & Filter -- Search by process name, port, or IP; filter by scope (system / docker)
• Keyboard Driven -- Full keyboard navigation with vim-style shortcuts
• Cross-platform -- Linux, macOS, and WSL2

Install

pip install whosat

With Docker support:

pip install 'whosat[docker]'

Usage

whosat

That's it. whosat will scan your system and display all listening ports and their processes.

Keyboard Shortcuts

Key	Action
1 / 2	Switch to Ports / Memory view
Up / Down	Navigate rows
Enter	Expand/collapse row + open detail panel
Esc	Close detail panel / modal / clear search
/	Focus search
R	Refresh data
G	Toggle group/flat view
K	Kill selected process
S	Cycle scope (ALL / SYS / DOCKER)
T	Cycle theme
Y	Copy IP:PORT to clipboard
Q	Quit

Configuration

On first run, whosat creates ~/.whosat/config.toml with default categories and settings.

theme = "matrix"
refresh_interval = 30

[[categories]]
name = "python"
icon = "snake"
section = "System"
match = ["python3", "python", "uvicorn", "gunicorn", "celery"]

# Add your own:
# [[port_names]]
# port = 8020
# name = "My App"
# icon = "robot"

Requirements

• Python 3.10+
• Linux, macOS, or WSL2
• Optional: Docker SDK for container monitoring
• Optional: NVIDIA GPU for GPU memory tracking in memory view

License

MIT