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 LiangNiang · Agent Tool · ★ 117
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is OpenMantis safe to install? View the security audit →
OpenMantis 中文 | English 基于 Bun + Vercel AI SDK 构建的个人 Agent 聊天框架——一次部署，自己用。 将多个 LLM 供应商连接到多个通讯平台，配合可组合的工具、定时任务、浏览器自动化、记忆系统、定时任务等能力 —— 一次部署，全部搞定。 [!NOTE] OpenMantis 是为个人单用户使用而设计的：假设只有一位用户（项目所有者本人）通过飞书/企业微信/QQ 等渠道与 Agent 对话。因此不内置多租户、用户隔离或权限管理，记忆、路由、调度等均不按用户区分。 特性 多 LLM 供应商 — OpenAI、Anthropic、字节跳动/豆包、DeepSeek、小米 MiMo，以及任意 OpenAI 兼容端点。支持按通道或按会话切换 LLM 供应商。 多平台 — 飞书/Lark、企业微信、QQ。每个平台均支持流式响应和附件处理，飞书额外支持交互式卡片 UI 和单渠道接入多个飞书应用。
| Stars | 117 |
| Forks | 9 |
| Language | TypeScript |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 59.458163958135/100 |
| Last Updated | 2026-05-16 |
| Created | 2026-04-11 |
| Platforms | browser, node |
| Est. Tokens | ~584k |
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OpenMantis is Lightweight multi-platform AI agent chat framework. Connect multiple LLMs to Feishu/WeCom/QQ with composable tools, long-term memory, scheduling & browser automation. | 轻量级多平台 AI Agent 聊天框架，连接多个 LLM . It is categorized as a Agent Tool with 117 GitHub stars.
OpenMantis is primarily written in TypeScript.
You can find installation instructions and usage details in the OpenMantis GitHub repository at github.com/LiangNiang/OpenMantis. The project has 117 stars and 9 forks, indicating an active community.
OpenMantis is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to OpenMantis on Agent Skills Hub include claude-plugins, tutor-skills, repren. 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: