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 CommandCodeAI · Agent Tool · ★ 1.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is BaseAI safe to install? View the security audit →
BaseAI is now archived in the favor of Langbase AI Primitives. The more we built BaseAI the more we realized frameworks are a bad idea in AI engineering. This space is moving fast and frameworks become blockers. Instead you should be using AI primitives, like memory, pipes, agents that work with any language as APIs and with TypeScript and Python SDKs. You can use any coding agent, like CommandCode to make your own AI framework with these AI primitives. BaseAI is a great example how. BaseAI Getting Started BaseAI is the AI framework for building serverless and composable AI agents with memory and tools. It allows you to develop AI agent p
| Stars | 1,275 |
| Forks | 112 |
| Language | TypeScript |
| Category | Agent Tool |
| Quality Score | 68.430783887529/100 |
| Open Issues | 12 |
| Last Updated | 2026-05-16 |
| Created | 2024-09-29 |
| Platforms | browser, gemini, node |
| Est. Tokens | ~206k |
These tools work well together with BaseAI for enhanced workflows:
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BaseAI is BaseAI — The Web AI Framework. The easiest way to build serverless autonomous AI agents with memory. Start building local-first, agentic pipes, tools, and memory. Deploy serverless with one command.. It is categorized as a Agent Tool with 1.3k GitHub stars.
BaseAI is primarily written in TypeScript. It covers topics such as ai, anthropic, artificial-intelligence.
You can find installation instructions and usage details in the BaseAI GitHub repository at github.com/CommandCodeAI/BaseAI. The project has 1.3k stars and 112 forks, indicating an active community.
The top alternatives to BaseAI on Agent Skills Hub include ax, py-gpt, chat-js. 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: