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vox CLI + bridge server that connects Twilio Media Streams (PSTN phone calls) ↔ OpenAI Realtime (speech-to-speech) and lets the voice model call back into your local agent via tools. This repo is intentionally “thin glue”: WebSocket proxying, barge-in/interrupt handling, logging, and a minimal tool adapter. What you get : HTTP endpoint that returns TwiML and a WebSocket endpoint Twilio streams audio to. : outbound calling via Twilio REST to connect a call to your running . OpenAI Realtime session configured for G.711 μ-law passthrough () so there’s no resampling/DSP required. tool that can call: an HTTP endpoint (), or a local subprocess (, JSONL request/response). Requirements Node.js = 22 A public HTTPS URL to your laptop (Twilio needs to reach and ). works fine. OpenAI API key Twilio account + a phone number (for PSTN calling) Setup Fill in at least: (the public HTTPS base URL that maps to your local server, e.g. your ngrok URL) Run Start the bridge: Smoke check: Inbound calls (recommended dev loop) Point your Twilio Phone Number’s “A call comes in” webhook to: When you
| Stars | 94 |
| Forks | 18 |
| Language | TypeScript |
| Category | AI Tool |
| License | MIT |
| Quality Score | 69.5642921456516/100 |
| Last Updated | 2026-09-04 |
| Created | 2025-12-13 |
| Platforms | cli, node |
| Est. Tokens | ~14k |
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vox is Let's your agent run phone calls. It is categorized as a AI Tool with 94 GitHub stars.
vox is primarily written in TypeScript. It covers topics such as call, cli, typescript.
You can find installation instructions and usage details in the vox GitHub repository at github.com/steipete/vox. The project has 94 stars and 18 forks, indicating an active community.
vox is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to vox on Agent Skills Hub include sudocode, mcp-server-atlassian-bitbucket, claude-code-open. 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.
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