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 openai · Agent Tool · ★ 3.8k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is openai-agents-js safe to install? View the security audit →
OpenAI Agents SDK (JavaScript/TypeScript) The OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows in JavaScript/TypeScript. It is provider-agnostic, supporting OpenAI APIs and more. [!NOTE] Looking for the Python version? Check out OpenAI Agents SDK Python. Core concepts Agents: LLMs configured with instructions, tools, guardrails, and handoffs Agents as tools / Handoffs: Delegating to other agents for specific tasks Tools: Various Tools let agents take actions (functions, MCP, hosted tools) Guardrails: Configurable safety checks for input and output validation [Human in the loop](https://openai.github.io/openai-agents-js/guides/h
| Stars | 3,844 |
| Forks | 981 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 68.7063525758279/100 |
| Open Issues | 29 |
| Last Updated | 2026-09-22 |
| Created | 2025-05-31 |
| Platforms | node |
| Est. Tokens | ~14k |
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openai-agents-js is A lightweight, powerful framework for multi-agent workflows and voice agents. It is categorized as a Agent Tool with 3.8k GitHub stars.
openai-agents-js is primarily written in TypeScript. It covers topics such as agents, openai, openai-api.
You can find installation instructions and usage details in the openai-agents-js GitHub repository at github.com/openai/openai-agents-js. The project has 3.8k stars and 981 forks, indicating an active community.
openai-agents-js is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to openai-agents-js on Agent Skills Hub include LightAgent, Patter, AI-Gateway. 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: