No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by vastsa · MCP Server · ★ 4.6k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is PI-Desktop safe to install? View the security audit →
PI-Desktop A local-first desktop app for AI coding agents. Bring your own models. Keep your code, your keys, and your conversations on your machine. Download · Getting started · Highlights · How it works · Development · 简体中文 What is PI-Desktop? PI-Desktop puts an AI coding agent in a native desktop app. Point it at a project, describe what you want — explore and understand code, build a feature, review changes, fix a failing test — and watch it work, with every file edit and shell command surfaced for your approval. There is no account, no subscription, and no cloud in the middle: you connect the model provider you already use, and everything else — sessions, settings, API keys — stays local. Highlights Any model, your keys. Anthropic, OpenAI,
| Stars | 4,644 |
| Forks | 393 |
| Language | TypeScript |
| Category | MCP Server |
| License | LGPL-3.0 |
| Quality Score | 71.5407501860854/100 |
| Open Issues | 116 |
| Last Updated | 2026-09-20 |
| Created | 2023-03-22 |
| Platforms | mcp, node |
| Est. Tokens | ~15k |
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PI-Desktop is Local-first AI coding agent desktop: Electron + Rust host core + pi Agent Harness + user-installable plugins. It is categorized as a MCP Server with 4.6k GitHub stars.
PI-Desktop is primarily written in TypeScript. It covers topics such as ai-agent, coding-agent, desktop-app.
You can find installation instructions and usage details in the PI-Desktop GitHub repository at github.com/vastsa/PI-Desktop. The project has 4.6k stars and 393 forks, indicating an active community.
PI-Desktop is released under the LGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to PI-Desktop on Agent Skills Hub include open-cowork, tabularis, skills-hub. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: