展示HN:Dograh – 一个开源的Vapi替代品,用于快速构建和测试语音代理。
Show HN: Dograh – an OSS Vapi alternative to quickly build and test voice agents

原始链接: https://github.com/dograh-hq/dograh

## Dograh:开源语音AI平台 Dograh是一个快速的开源平台,作为Vapi等平台的替代品,它允许用户通过简单的拖放界面构建定制的语音代理。你可以在不到2分钟的时间内从零开始创建一个可用的机器人! Dograh完全可以自托管,避免了供应商锁定,并以其开放的代码库提供完全的控制和透明度。它具有内置的AI测试角色(LoopTalk),并支持与各种LLM、TTS和STT提供商的灵活集成——或者使用其自身的默认设置。 通过Docker进行设置非常简单 (`curl -o docker-compose.yaml...`),可以通过 `http://localhost:3010` 访问。Dograh提供Twilio集成、可定制模型、实时处理和模块化架构等功能。 Dograh由YC校友开发和维护,致力于保持语音AI的开放和可访问性。欢迎通过GitHub贡献,并通过他们的Slack社区获得支持。你也可以在[https://www.dograh.com](https://www.dograh.com) 探索托管云版本。

## Dograh:一个开源语音代理框架 Dograh是一个全新的、完全开源的框架,旨在简化语音代理的构建和测试,目标是解决现有VAPI风格平台(如Vapi和Retell)相关的复杂性和成本问题。 由YC校友创建,他们对重复的基础设施工作感到沮丧,Dograh建立在Pipecat和LiveKit之上,并添加了许多开源解决方案中经常缺失的关键功能——自定义事件模型、并发性修复、可视化拖放式代理构建器和内置变量提取。它还提供电话集成(Twilio、Vonage等)和多语言支持。 一个关键特性是AI到AI的测试,用于自动压力测试。开发者开源Dograh是为了避免闭源SaaS解决方案在数据控制、隐私和自托管方面的限制。他们强调,平台费用通常在现有解决方案的成本中占据主导地位,而实际的LLM/STT/TTS使用量只是费用的一小部分。 该项目正在积极寻求关于管理长期对话中上下文的意见。仓库地址:[https://github.com/dograh-hq/dograh](https://github.com/dograh-hq/dograh)。
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原文

If you find value in this project, PLEASE STAR the Github repository to help others discover our FOSS platform!

Docs: https://docs.dograh.com License: BSD 2-Clause Slack Community Docker Ready

The open-source alternative to Vapi - Dograh helps you build your own voice agents with an easy drag-and-drop workflow builder. It's the fastest way to build voice AI agents - from zero to working bot in under 2 minutes (our hard SLA standards).

  • 100% open source, self-hostable platform - no vendor lock-in, unlike proprietary solutions like Vapi
  • Full control & transparency - every line of code is open, with built-in AI testing personas and flexible LLM/TTS/STT integration
  • Maintained by YC alumni and exit founders, ensuring the future of voice AI stays open, not monopolized

The only command you need to run:

Download and setup Dograh on your local machine

Note We collect anonymous usage data to improve the product. You can opt out by setting the ENABLE_TELEMETRY to false in the below command.

curl -o docker-compose.yaml https://raw.githubusercontent.com/dograh-hq/dograh/main/docker-compose.yaml && REGISTRY=ghcr.io/dograh-hq ENABLE_TELEMETRY=true docker compose up --pull always

Note First startup may take 2-3 minutes to download all images. Once running, open http://localhost:3010 to create your first AI voice assistant! For common issues and solutions, see 🔧 Troubleshooting.

🎙️ Your First Voice Bot

  1. Open Dashboard: Launch http://localhost:3010 on your browser
  2. Choose Call Type: Select Inbound or Outbound calling.
  3. Name Your Bot: Use a short two-word name (e.g., Lead Qualification).
  4. Describe Use Case: In 5–10 words (e.g., Screen insurance form submissions for purchase intent).
  5. Launch: Your bot is ready! Open the bot and click Web Call to talk to it.
  6. No API Keys Needed: We auto-generate Dograh API keys so you can start immediately. You can switch to your own keys anytime.
  7. Default Access: Includes Dograh’s own LLMs, STT, and TTS stack by default.
  8. Bring Your Own Keys: Optionally connect your own API keys for LLMs, STT, TTS, or telephony providers like Twilio.

Open-source alternative to Vapi - 2-minute setup with hard SLA standards

  • 🔧 No vendor lock-in: Self-hostable platform vs proprietary SaaS solutions
  • 🤖 AI Testing Personas: Test your bots with LoopTalk AI that mimics real customer interactions
  • 🔓 100% Open Source: Every line of code is open - no hidden logic, no black boxes (unlike Vapi)
  • 🔄 Flexible Integration: Bring your own LLM, TTS, or STT - or use Dograh's APIs
  • ☁️ Deploy anywhere: Self-host or use our hosted version at app.dograh.com
  • Telephony: Built-in Twilio integration (easily add others)
  • Languages: English support (expandable to other languages)
  • Custom Models: Bring your own TTS/STT models
  • Real-time Processing: Low-latency voice interactions
  • Zero Config Start: Auto-generated API keys for instant testing
  • Python-Based: Built on Python for easy customization
  • Docker-First: Containerized for consistent deployments
  • Modular Architecture: Swap components as needed
  • LoopTalk (Beta): Create AI personas to test your voice agents
  • Workflow Testing: Test specific workflow IDs with automated calls
  • Real-world Simulation: AI personas that mimic actual customer behavior

Architecture diagram (coming soon)

Refer prerequisites and first steps

For detailed deployment instructions including remote server setup with HTTPS, see our Docker Deployment Guide.

Production guide coming soon. Drop in a message for assistance.

Visit https://www.dograh.com for our managed cloud offering.

You can go to https://docs.dograh.com for our documentation.

  • GitHub Issues: Report bugs or request features
  • Slack: Our Slack community is not just for support — it’s the cornerstone of Dograh AI contributions. Here, you can:
    • Connect with maintainers and other contributors
    • Discuss issues and features before coding
    • Get help with setup and debugging
    • Stay up to date with contribution sprints

👉 Join us → Dograh Community Slack

We love contributions! Dograh AI is 100% open source and we intend to keep it that way.

  • Fork the repository
  • Create your feature branch (git checkout -b feature/AmazingFeature)
  • Commit your changes (git commit -m 'Add some AmazingFeature')
  • Push to the branch (git push origin feature/AmazingFeature)
  • Open a Pull Request

Dograh AI is licensed under the BSD 2-Clause License- the same license as projects that were used in building Dograh AI, ensuring compatibility and freedom to use, modify, and distribute.

Built with ❤️ by Dograh (Zansat Technologies Private Limited) Founded by YC alumni and exit founders committed to keeping voice AI open and accessible to everyone.

⭐ Star us on GitHub | ☁️ Try Cloud Version | 💬 Join Slack

联系我们 contact @ memedata.com