by langtail · Agent Tool · ★ 83
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npm install ai-orchestra
yarn add ai-orchestra
Taken from the project's README on GitHub.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 83 |
| Forks | 6 |
| Language | TypeScript |
| Category | Agent Tool |
| Quality Score | 67/100 |
| Last Updated | 2025-02-12 |
| Created | 2025-02-02 |
| Platforms | node |
| Est. Tokens | ~5k |
AI Orchestra 🎭 Simple orchestration for AI Agents built around Vercel's . AI Orchestra provides a lightweight way to handle agent handoffs and state transitions, similar to OpenAI's Swarm but with more developer control and less magic. Think of it as a simpler alternative to LangGraph, focused on streaming and agent coordination.
Looking for a ai-orchestra alternative? If you're comparing ai-orchestra with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
The pipe layer of an AI nervous system — one interface connecting provider neurons to your application, across
Torque is a Declarative, typesafe DSL for building synthetic LLM datasets — compose conversations like React c
AI-powered offensive security testing using autonomous agents, directly in your terminal.
Context-aware AI reviewer for Pull Requests. Instant summary, line-by-line comments, title generation and more
An introduction to the world of AI Agents
Web app for teams of 20+ members. In-built connections to major LLMs via API. Share chats, prompts, and agents
Explore other popular agent tool tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: