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 aspasskiy · Codex Skill · ★ 298
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is GoGogot safe to install? View the security audit →
GoGogot — Lightweight OpenClaw Written in Go A lightweight, extensible, and secure open-source AI agent that lives on your server. It runs shell commands, edits files, browses the web, manages persistent memory, and schedules tasks — a self-hosted alternative to OpenClaw (Claude Code) in 9,000 lines of core Go. Single binary, 15 MB, 10 MB RAM — deploys with one command Your keys stay on your server — no cloud account, no telemetry, no phoning home You pick the model — Anthropic, OpenAI, or any OpenRouter model Extensible — clean Go interfaces (, , ) make it trivial to add providers, transports, or custom tools Quick Start Prerequisites Get a by creating a new bot via @BotFather on Telegram. Find your TELEGRAMOW
| Stars | 298 |
| Forks | 17 |
| Language | Go |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 68.2376895011188/100 |
| Open Issues | 4 |
| Last Updated | 2026-08-26 |
| Created | 2026-02-23 |
| Platforms | claude-code, docker, go |
| Est. Tokens | ~17k |
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GoGogot is Lightweight self-hosted AI agent. Open-source OpenClaw alternative in Go.. It is categorized as a Codex Skill with 298 GitHub stars.
GoGogot is primarily written in Go. It covers topics such as agentic, ai-agent, ai-assistant.
You can find installation instructions and usage details in the GoGogot GitHub repository at github.com/aspasskiy/GoGogot. The project has 298 stars and 17 forks, indicating an active community.
GoGogot is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to GoGogot on Agent Skills Hub include Overture, openclaw-command-center, openpaw. 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: