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 →
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Voice Agents SDK Sample App This repository contains a sample app to highlight how to build voice agents using the Agents SDK and Python. The backend is written using FastAPI and exposes a websocket endpoint. The front-end is written using Next.js and connects to the websocket server. Features: Multi-turn conversation handling Push-to-talk audio mode Function calling Streaming responses & tool calls This app is meant to be used as a starting point to build a conversational assistant that you can customize to your needs. Requirements OpenAI API key If you're new to the OpenAI API, sign up for an account. Follow the Quickstart to retrieve your API key. Node.js and npm installed on your system How to use Set the OpenAI API key: 2 options: Set the environment variable globally in your system Set the environment variable in the project: Create a
| Stars | 256 |
| Forks | 77 |
| Language | TypeScript |
| Category | AI Tool |
| License | MIT |
| Quality Score | 67.5972542370805/100 |
| Open Issues | 1 |
| Last Updated | 2025-05-13 |
| Created | 2025-04-08 |
| Platforms | node |
| Est. Tokens | ~10k |
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openai-voice-agent-sdk-sample is Sample application to add voice capabilities to the Agents SDK. It is categorized as a AI Tool with 256 GitHub stars.
openai-voice-agent-sdk-sample is primarily written in TypeScript.
You can find installation instructions and usage details in the openai-voice-agent-sdk-sample GitHub repository at github.com/openai/openai-voice-agent-sdk-sample. The project has 256 stars and 77 forks, indicating an active community.
openai-voice-agent-sdk-sample is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to openai-voice-agent-sdk-sample on Agent Skills Hub include pivotnacci, MedgeClaw, SciToolAgent. 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: