Flagged: opens a tunnel service. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by bolna-ai · Agent Tool · ★ 769
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End-to-end open-source voice agents platform: Quickly build voice firsts conversational assistants through a json. Discord | Hosted Docs | Website [!NOTE] We are actively looking for maintainers. Introduction Bolna is the end-to-end open source production ready framework for quickly building LLM based voice driven conversational applications. Demo https://github.com/bolna-ai/bolna/assets/1313096/2237f64f-1c5b-4723-b7e7-d11466e9b226 What is this repository? This repository contains the entire orchestration platform to build voice AI applications. It technically orchestrates voice conversations using combination of different ASR+LLM+TTS providers and m
| Stars | 769 |
| Forks | 360 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 63.7462561797929/100 |
| Open Issues | 136 |
| Last Updated | 2026-09-22 |
| Created | 2024-08-24 |
| Platforms | python |
| Est. Tokens | ~16k |
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bolna is Conversational voice AI agents. It is categorized as a Agent Tool with 769 GitHub stars.
bolna is primarily written in Python. It covers topics such as agentic-ai, agents, ai-agents.
You can find installation instructions and usage details in the bolna GitHub repository at github.com/bolna-ai/bolna. The project has 769 stars and 360 forks, indicating an active community.
bolna is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to bolna on Agent Skills Hub include DemoGPT, tools, samples. 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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