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 nlpodyssey · Agent Tool · ★ 270
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is openai-agents-go safe to install? View the security audit →
OpenAI Agents Go SDK The OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows. It is provider-agnostic, supporting the OpenAI Responses and Chat Completions APIs, as well as other LLMs. This is a Go port of OpenAI Agents Python SDK (see its license here). This project aims at being as close as possible to the original Python implementation, for both its behavior and the API. Core concepts: Agents: LLMs configured with instructions, tools, guardrails, and handoffs Handoffs: A specialized tool call used by the Agents SDK for transferring control between agents Guardrails: Configurable safety checks for input and output validation Explore the examples directory to see the SDK in action: Techniques
| Stars | 270 |
| Forks | 34 |
| Language | Go |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 75.0810324786291/100 |
| Open Issues | 9 |
| Last Updated | 2026-03-26 |
| Created | 2025-06-13 |
| Platforms | go |
| Est. Tokens | ~68k |
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openai-agents-go is A lightweight, powerful framework for multi-agent workflows in Go. It is categorized as a Agent Tool with 270 GitHub stars.
openai-agents-go is primarily written in Go. It covers topics such as agents, ai, artificial-intelligence.
You can find installation instructions and usage details in the openai-agents-go GitHub repository at github.com/nlpodyssey/openai-agents-go. The project has 270 stars and 34 forks, indicating an active community.
openai-agents-go is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to openai-agents-go on Agent Skills Hub include sre, awesome-ai-sdks, tools. 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: