test cloudflare readme
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README.zh.md
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README.zh.md
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<a style="display:inline-flex;" href="https://cookbook.openai.com/examples/voice_solutions/running_realtime_api_speech_on_esp32_arduino_edge_runtime_elatoai"><img src="assets/oai.png" height="42" style="width: auto;"></a>
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<a style="display:inline-flex;" href="https://www.elatoai.com/docs"><img src="assets/docs.png" height="42" style="width: auto;"></a>
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<a style="display:inline-flex;" href="https://discord.gg/KJWxDPBRUj"><img src="assets/discord.png" height="42" style="width: auto;"></a>
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<!-- <a style="display:inline-flex;" href="https://elatoai.com/home"><img src="assets/try.png" height="42" style="width: auto;"></a> -->
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<a style="display:inline-flex;" href="https://www.kickstarter.com/projects/elatoai/elato-make-toys-talk-with-ai-voices"><img src="assets/ks.png" height="42" style="width: auto;"></a>
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<!-- <a style="display:inline-flex;" href="https://www.elatoai.com/products/ai-devkit"><img src="assets/diy.png" height="42" style="width: auto;"></a> -->
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</div>
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<a href="https://www.kickstarter.com/projects/elatoai/elato-make-toys-talk-with-ai-voices" target="_blank">
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<img src="assets/cover.png" alt="Elato Logo" width="100%">
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</div>
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## 新闻
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- **2026-03-14：** Elato 刚刚发布了 Local AI Toys。🎉🎉🎉 而且今天还是 Pi Day！现在你的 ESP32 设备可以通过 MLX 支持本地 AI 模型和语音生成，配合 Qwen、Mistral 等前沿本地 LLM 和 TTS 模型。点击[这里](https://www.github.com/akdeb/local-ai-toys)查看。
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- **2026 年 4 月 17 日：** 现在可以用 Cloudflare Voice Agents 和 Durable Objects 构建全球化的设备/玩具语音网络。Cloudflare Workers AI 原生提供 Deepgram STT/TTS，因此你只需要提供一个 LLM API Key，就能搭建可扩展、低延迟的语音 AI 流水线。
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- **2026 年 4 月 15 日：** 现在你可以通过基于 Pipecat 的 FastAPI 服务器部署 100 多种 STT、LLM、TTS 语音流水线系统。
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- **2026 年 3 月 14 日：** Elato 在 Pi Day 发布了 Local AI Toys。你的 ESP32 设备现在可以通过 MLX 运行本地 AI 模型和语音生成，支持 Qwen、Mistral 等本地前沿 LLM 和 TTS 模型。点击[这里](https://www.github.com/akdeb/local-ai-toys)查看。
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# 👾 ElatoAI：在 Arduino ESP32 上运行实时语音 AI 模型
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基于先进语音模型的实时 AI 语音系统，可运行在 ESP32 上，并通过安全 WebSocket 与 Deno Edge Functions 实现全球范围内超过 15 分钟的不间断对话。当前支持 OpenAI Realtime API、Gemini Live API、xAI Grok Voice Agents API、Eleven Labs Conversational AI Agents 和 Hume AI EVI-4。
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ElatoAI 让你在 ESP32 上运行由 100 多种语音 AI 模型驱动的实时语音系统，并通过安全 WebSocket 与边缘函数实现全球范围内 20 分钟以上的不间断对话。
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- [🚀 快速开始](https://www.elatoai.com/docs/quickstart)
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- [使用 PlatformIO 构建](https://www.elatoai.com/docs/platformio)
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- [在 Arduino IDE 上构建](https://www.elatoai.com/docs/arduino)
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- [在 Arduino IDE 中构建](https://www.elatoai.com/docs/arduino)
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- [全球部署](https://www.elatoai.com/docs/blog/deploying-globally)
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- [🤖🤖🤖 部署多台设备](https://www.elatoai.com/docs/blog/multiple-devices)
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@ -46,6 +46,20 @@
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视频链接：[OpenAI 演示](https://youtu.be/o1eIAwVll5I) | [Gemini 演示](https://youtu.be/_zUBue3pfVI) | [Eleven Labs 演示](https://youtu.be/7LKTIuEW-hg) | [Hume AI EVI-4 演示](https://youtu.be/Gtann5pdV0I)
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## 🧠 模型
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### Deno Edge
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1. [OpenAI Realtime API](https://github.com/akdeb/ElatoAI/tree/main/server/deno/models/openai)
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2. [Gemini Live API](https://github.com/akdeb/ElatoAI/tree/main/server/deno/models/gemini)
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3. [xAI Grok Voice Agent API](https://github.com/akdeb/ElatoAI/tree/main/server/deno/models/grok)
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4. [Eleven Labs Conversational AI Agents](https://github.com/akdeb/ElatoAI/tree/main/server/deno/models/elevenlabs)
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5. [Hume AI EVI-4](https://github.com/akdeb/ElatoAI/tree/main/server/deno/models/hume)
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### Cloudflare Workers
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1. LLM - [80 多种模型](https://developers.cloudflare.com/workers-ai/models/?tasks=Text+Generation)，包括 OpenAI、Gemini、xAI 等。
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2. TTS - [10 多种模型](https://developers.cloudflare.com/workers-ai/models/?tasks=Text-to-Speech)，包括 Deepgram、MeloTTS 等。
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3. STT - [5 种模型](https://developers.cloudflare.com/workers-ai/models/?tasks=Automatic+Speech+Recognition)，包括 Whisper、Deepgram 等。
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## 👷♀️ DIY 硬件设计
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<img src="assets/pcb-design.png" alt="Hardware Setup" width="100%">
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@ -56,50 +70,48 @@
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<img src="assets/mockups.png" alt="App Screenshots" width="100%">
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## ⭐️ 语音 AI 关键特性
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<img src="assets/features.png" alt="App Screenshots" width="100%">
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## 🌟 完整功能列表
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1. **实时语音转语音**：基于 OpenAI Realtime API、Gemini Live API、xAI Grok Voice Agent API、Eleven Labs Conversational AI Agents 和 Hume AI EVI4 的即时语音转换。
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2. **创建自定义 AI 智能体**：创建拥有不同个性和声音的自定义智能体。
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3. **可定制语音**：从多种声音和人格中进行选择。
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1. **实时语音转语音**：由 OpenAI Realtime API、Gemini Live API、xAI Grok Voice Agent API、Eleven Labs Conversational AI Agents 和 Hume AI EVI4 驱动的即时语音转换。
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2. **创建自定义 AI 智能体**：创建具有不同人格和声音的 AI 智能体。
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3. **可自定义语音**：从多种声音和人格配置中进行选择。
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4. **安全 WebSocket**：可靠且加密的 WebSocket 通信。
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5. **服务端 VAD 轮次检测**：智能处理对话轮次，让交互更流畅。
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5. **服务端 VAD 轮次检测**：智能对话轮次处理，让交互更自然。
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6. **Opus 音频压缩**：以极低带宽实现高质量音频流传输。
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7. **全球边缘性能**：低延迟的 Deno Edge Functions 确保全球范围内的流畅对话。
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8. **ESP32 Arduino 框架**：经过优化且易于使用的硬件集成方案。
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9. **对话历史**：查看你的历史对话记录。
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7. **全球边缘性能**：低延迟的 Deno Edge Functions 确保全球范围内的顺畅对话。
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8. **ESP32 Arduino 框架**：经过优化且易于集成的硬件方案。
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9. **对话历史**：查看历史对话记录。
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10. **设备管理与认证**：注册并管理你的设备。
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11. **用户认证**：安全的用户认证与授权。
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12. **通过 WebRTC 和 WebSocket 对话**：你可以在 NextJS Web 应用中通过 WebRTC 与 AI 对话，也可以在 ESP32 上通过 WebSocket 对话。
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13. **音量控制**：在 NextJS Web 应用中控制 ESP32 扬声器的音量。
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14. **实时转录**：你的对话实时转录内容会存储在 Supabase 数据库中。
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11. **用户认证**：安全的用户身份认证与授权。
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12. **通过 WebRTC 和 WebSocket 对话**：在 NextJS Web 应用中用 WebRTC 与 AI 对话，在 ESP32 上通过 WebSocket 对话。
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13. **音量控制**：通过 NextJS Web 应用控制 ESP32 扬声器音量。
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14. **实时转录**：对话实时转录结果存储在 Supabase 数据库中。
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15. **OTA 更新**：支持 ESP32 固件空中更新。
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16. **通过 Captive Portal 管理 Wi-Fi**：在 ESP32 设备上连接到你的 Wi-Fi 网络或热点。
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16. **通过 captive portal 管理 Wi-Fi**：直接在 ESP32 设备上连接 Wi-Fi 或热点。
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17. **恢复出厂设置**：通过 NextJS Web 应用对 ESP32 设备执行恢复出厂设置。
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18. **按钮与触摸支持**：可使用按钮或触摸传感器来控制 ESP32 设备。
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19. **无需 PSRAM**：ESP32 设备无需 PSRAM 即可运行语音转语音 AI。
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20. **Web 客户端 OAuth**：为你的用户提供 OAuth，以管理他们的 AI 角色和设备。
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21. **音高因子**：在 NextJS Web 应用中调节 AI 语音的音高，以创建更卡通化的声音。
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22. **工具调用**：从 ESP32 设备调用 Deno Edge Functions 上的工具和函数，构建完整的语音 AI 智能体。
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23. **轻触唤醒**：轻触触摸板即可从休眠中唤醒设备。
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18. **按钮和触摸支持**：可以通过按钮或触摸传感器控制 ESP32 设备。
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19. **无需 PSRAM**：设备无需 PSRAM 即可运行语音转语音 AI。
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20. **Web 客户端 OAuth**：让用户管理自己的 AI 角色和设备。
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21. **音高控制**：在 NextJS Web 应用中调整 AI 声音音高，做出更卡通化的声音。
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22. **工具调用**：从 ESP32 设备调用边缘函数中的工具和函数，构建完整的语音 AI 智能体。
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23. **轻触唤醒**：轻触触摸板即可从休眠中唤醒。
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24. **部署到 Cloudflare**：借助 Cloudflare Voice Agents 和 Durable Objects 连接任意 LLM、TTS、STT 服务。
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## 项目架构
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ElatoAI 由三个主要组件构成：
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ElatoAI 由三个主要组件组成：
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1. **前端客户端**（部署在 Vercel 上的 `Next.js`）- 用于创建并与 AI 智能体对话，并将其“发送”到你的 ESP32 设备
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2. **边缘服务函数**（运行在 Deno/Supabase Edge 上的 `Deno`）- 用于处理来自 ESP32 设备的 WebSocket 连接以及对 LLM 提供商 API 的调用
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3. **ESP32 IoT 客户端**（`PlatformIO/Arduino`）- 用于接收来自边缘服务函数的 WebSocket 连接，并通过 Deno 边缘服务把音频发送给 LLM 提供商
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2. **边缘服务函数**（`Deno Edge` 或 `Cloudflare Workers`）- 用于处理来自 ESP32 设备的 WebSocket 连接以及对模型提供商 API 的调用
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3. **ESP32 IoT 客户端**（`PlatformIO/Arduino`）- 接收来自边缘服务函数的 WebSocket 连接，并通过 Deno 边缘服务器或 Cloudflare Durable Objects 把音频发送给模型提供商
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## 🛠 技术栈
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| 组件 | 使用的技术 |
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| 组件 | 使用技术 |
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|-----------------|------------------------------------------|
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| 前端 | Next.js, Vercel |
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| 后端 | Supabase DB |
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| 边缘函数 | 运行于 Deno/Supabase 的 Deno Edge Functions |
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| 边缘函数 | Deno Edge 或 Cloudflare Workers |
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| IoT 客户端 | PlatformIO, Arduino Framework, ESP32-S3 |
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| 音频编解码 | Opus |
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| 通信 | 安全 WebSockets |
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@ -115,7 +127,7 @@ flowchart TD
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end
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UserInput --> ESP32
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ESP32[ESP32 设备] -->|WebSocket| Edge[Deno Edge Function]
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ESP32[ESP32 设备] -->|WebSocket| Edge[Deno Edge / Cloudflare Workers]
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Edge -->|OpenAI API| OpenAI[OpenAI Realtime API]
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Edge -->|Gemini API| Gemini[Gemini Live API]
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Edge -->|xAI API| xAI[xAI Grok Voice Agent API]
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@ -135,14 +147,14 @@ flowchart TD
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```mermaid
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graph TD
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repo[ElatoAI]
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repo --> frontend[前端 Vercel NextJS]
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repo --> deno[Deno Edge Function]
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repo --> esp32[ESP32 Arduino 客户端]
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deno --> supabase[Supabase DB]
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repo --> frontend[Frontend Vercel NextJS]
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repo --> server[Deno Edge Function / Cloudflare Workers]
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repo --> esp32[ESP32 Arduino Client]
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server --> supabase[Supabase DB]
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frontend --> supabase
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esp32 --> websockets[安全 WebSockets]
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esp32 --> opus[Opus 编解码器]
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esp32 --> websockets[Secure WebSockets]
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esp32 --> opus[Opus Codec]
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esp32 --> audio_tools[arduino-audio-tools]
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esp32 --> libopus[arduino-libopus]
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esp32 --> ESPAsyncWebServer[ESPAsyncWebServer]
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@ -151,38 +163,39 @@ graph TD
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## 📊 关键指标
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- **延迟**：全球往返延迟小于 2 秒
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- **音频质量**：使用 12kbps Opus 编码（高清晰度），24kHz 采样率
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- **不间断对话**：全球范围内支持最长 15 分钟连续对话
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- **全球可用性**：通过边缘计算进行优化
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- **音频质量**：12kbps Opus 编码（高清晰度）+ 24kHz 采样率
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- **不间断对话**：全球范围内最长可达 20 分钟连续对话
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- **全球可用性**：通过边缘计算优化
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## 🛡 安全性
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- 使用安全 WebSocket（WSS）进行加密数据传输
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- 可选：使用 256 位 AES 对 API Key 进行加密
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- 可选：使用 256 位 AES 加密 API Key
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- 使用 Supabase DB 进行安全认证
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- 所有数据表均采用 Postgres RLS
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- 所有表均采用 Postgres RLS
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## 🚫 限制
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- 连接边缘服务器时有 3-4 秒冷启动时间
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- 已测试最长连续对话时间约为 17 分钟
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- 当超过墙钟时间限制时，边缘服务器会停止运行
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- ESP32 上暂无语音打断检测
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- 已测试最长连续对话约为 17 分钟
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- 超过 wall clock time 后边缘服务器会停止
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- ESP32 上尚未支持语音打断检测
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## 🙌 贡献
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我们非常欢迎你的贡献！这里有一些可以参与的方向：
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欢迎贡献。你可以从这些方向开始：
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1. ESP32 上的语音打断（已支持 OpenAI）
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2. ~~添加 Arduino IDE 支持~~
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3. ~~添加用于情绪检测的 Hume API 客户端~~
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3. ~~添加 Hume API 客户端用于情绪检测~~
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4. 在 Deno Edge 上添加 MCP 支持
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5. ~~接入 Eleven Labs API 进行语音生成~~
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6. 添加 Azure OpenAI 支持（容易上手）- 审核中
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7. 添加 Cartesia 支持
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8. 添加 Amazon Nova 支持
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9. 添加 Deepgram 支持
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10. ~~添加 Cloudflare Workers 支持~~
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## 许可证
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本项目基于 MIT License 发布，详情请参见 [LICENSE](LICENSE) 文件。
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本项目基于 MIT License 发布，详情请查看 [LICENSE](LICENSE)。
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**欢迎查看我们的硬件产品：[ElatoAI Products](https://www.elatoai.com/)。如果你觉得这个项目有趣或有帮助，欢迎在 GitHub 上给这个项目点个 Star。⭐**
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**欢迎查看我们的硬件产品：[ElatoAI Products](https://www.elatoai.com/)。如果你觉得这个项目有趣或有帮助，欢迎在 GitHub 上给它点个 Star。⭐**
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# server-cloudflare
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# server/cloudflare
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Cloudflare Workers + Durable Objects voice backend for Elato.
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Cloudflare Workers + Durable Objects backend for Elato's ESP32 realtime voice flow.
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This starts with one ESP32-compatible websocket path:
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This server keeps the existing Elato device protocol and routes audio through Cloudflare-hosted services:
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- `/ws/esp32`
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The route is backed by a Durable Object that preserves the Elato device control protocol.
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## Current stack
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- STT: `@cf/openai/whisper`
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- STT: Cloudflare Workers AI via `@cloudflare/voice`
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- LLM: OpenAI Chat Completions
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- TTS: `@cf/deepgram/aura-1`
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- TTS: Cloudflare Workers AI Deepgram Aura
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- Transport: WebSocket + Opus packetization for ESP32
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## Local setup
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If you are new to the overall project, start with the root README first:
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1. Install dependencies
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- `/Users/akashdeepdeb/Desktop/Projects/ElatoAI/README.md`
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- `/Users/akashdeepdeb/Desktop/Projects/ElatoAI/server/README.md`
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## What This Server Does
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This backend is meant to be an alternative to the Deno edge server, not a separate firmware protocol.
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The ESP32 still talks to the same Elato-style control surface:
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- `auth`
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- `AUDIO.COMMITTED`
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- `RESPONSE.CREATED`
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- binary audio frames
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- `RESPONSE.COMPLETE`
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- `SESSION.END`
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Public route:
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```text
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/ws/esp32
|
||||
```
|
||||
|
||||
Health check:
|
||||
|
||||
```text
|
||||
/healthz
|
||||
```
|
||||
|
||||
## Current Layout
|
||||
|
||||
```text
|
||||
server/cloudflare/
|
||||
├── models/
|
||||
│ ├── llm.ts
|
||||
│ ├── session.ts
|
||||
│ ├── stt.ts
|
||||
│ └── tts.ts
|
||||
├── src/
|
||||
│ ├── index.ts
|
||||
│ ├── opus.ts
|
||||
│ ├── prompt.ts
|
||||
│ └── types.ts
|
||||
├── package.json
|
||||
└── wrangler.toml
|
||||
```
|
||||
|
||||
## How It Works
|
||||
|
||||
1. The ESP32 opens a secure websocket to `/ws/esp32`.
|
||||
2. The Worker creates a fresh Durable Object session for that websocket.
|
||||
3. The server sends the Elato `auth` payload.
|
||||
4. The server triggers the first assistant turn.
|
||||
5. LLM output is synthesized to audio.
|
||||
6. Audio is packetized into Opus frames and streamed back to the ESP32.
|
||||
7. After playback, the ESP32 goes back to listening.
|
||||
8. Incoming mic audio is fed to the STT session for the next turn.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
You need:
|
||||
|
||||
- Node.js 22+
|
||||
- npm
|
||||
- a Cloudflare account with Workers enabled
|
||||
- a Workers AI binding
|
||||
- an OpenAI API key for the LLM path
|
||||
|
||||
## Local Development
|
||||
|
||||
### 1. Install dependencies
|
||||
|
||||
```bash
|
||||
cd /Users/akashdeepdeb/Desktop/Projects/ElatoAI/server/cloudflare
|
||||
npm install
|
||||
```
|
||||
|
||||
2. Copy `.dev.vars.example` to `.dev.vars` and fill in your keys.
|
||||
### 2. Create local env vars
|
||||
|
||||
3. Run locally
|
||||
Copy the example file:
|
||||
|
||||
```bash
|
||||
cp .dev.vars.example .dev.vars
|
||||
```
|
||||
|
||||
Then fill in the values you actually need.
|
||||
|
||||
Typical local file:
|
||||
|
||||
```env
|
||||
OPENAI_API_KEY=...
|
||||
ELATO_OPENAI_MODEL=gpt-4.1-mini
|
||||
ELATO_OPENAI_SYSTEM_PROMPT=You are a friendly toy character.
|
||||
ELATO_OPENAI_FIRST_MESSAGE=Say hello first in one short sentence.
|
||||
```
|
||||
|
||||
Notes:
|
||||
|
||||
- `JWT_SECRET_KEY` is not currently required for the stripped-down iteration unless you wire auth back in.
|
||||
- Do not commit real secrets.
|
||||
|
||||
### 3. Run locally
|
||||
|
||||
```bash
|
||||
npm run dev
|
||||
```
|
||||
|
||||
## Notes
|
||||
This uses:
|
||||
|
||||
- ESP32 clients should connect to:
|
||||
|
||||
```text
|
||||
wss://<worker-domain>/ws/esp32
|
||||
```bash
|
||||
wrangler dev --ip 0.0.0.0 --port 8787
|
||||
```
|
||||
|
||||
- Auth is intentionally left out of this iteration. Add your own auth check in the Worker route before using this in production.
|
||||
- This backend now targets the current Elato ESP32 control protocol first:
|
||||
`auth`, `AUDIO.COMMITTED`, `RESPONSE.CREATED`, binary audio frames, `RESPONSE.COMPLETE`, and `SESSION.END`.
|
||||
- It does not currently use `@cloudflare/voice`; the Durable Object owns the websocket session directly so the firmware protocol stays explicit.
|
||||
- The ESP32 route now packetizes Cloudflare TTS output into Opus frames before sending binary websocket packets, matching the same 24kHz mono / 120ms framing shape used by `server/deno`.
|
||||
- The remaining gap is operational, not transport-level: this prototype still has placeholder auth / DB comments and has not been load-tested against long-running device sessions yet.
|
||||
So local access is typically:
|
||||
|
||||
```text
|
||||
http://<your-lan-ip>:8787/healthz
|
||||
ws://<your-lan-ip>:8787/ws/esp32
|
||||
```
|
||||
|
||||
For local firmware testing:
|
||||
|
||||
- point the ESP32 at your machine's LAN IP, not `0.0.0.0`
|
||||
- local plain `ws://` is fine for quick testing if your firmware build allows it
|
||||
- production firmware should use `wss://`
|
||||
|
||||
## Deploying to Cloudflare
|
||||
|
||||
### 1. Set Worker secrets
|
||||
|
||||
Set the runtime secrets in Cloudflare:
|
||||
|
||||
- `OPENAI_API_KEY`
|
||||
- optionally `ELATO_OPENAI_MODEL`
|
||||
- optionally `ELATO_OPENAI_SYSTEM_PROMPT`
|
||||
- optionally `ELATO_OPENAI_FIRST_MESSAGE`
|
||||
|
||||
### 2. Deploy
|
||||
|
||||
```bash
|
||||
cd /Users/akashdeepdeb/Desktop/Projects/ElatoAI/server/cloudflare
|
||||
npm run deploy
|
||||
```
|
||||
|
||||
### 3. Point the ESP32 at the Worker
|
||||
|
||||
Example production route:
|
||||
|
||||
```text
|
||||
wss://<your-worker>.workers.dev/ws/esp32
|
||||
```
|
||||
|
||||
## Durable Object Model
|
||||
|
||||
The current setup uses one fresh Durable Object per websocket voice session.
|
||||
|
||||
That is the sensible default for realtime voice apps because:
|
||||
|
||||
- each call/session gets isolated state
|
||||
- reconnects do not inherit stale memory
|
||||
- turn state is easier to reason about
|
||||
- cleanup is straightforward
|
||||
|
||||
This is what the Worker does in `/Users/akashdeepdeb/Desktop/Projects/ElatoAI/server/cloudflare/src/index.ts`.
|
||||
|
||||
## Migrations
|
||||
|
||||
This backend already has a Durable Object rename migration in `/Users/akashdeepdeb/Desktop/Projects/ElatoAI/server/cloudflare/wrangler.toml`:
|
||||
|
||||
- `ElatoOpenAiVoiceAgent` -> `ElatoVoiceSession`
|
||||
|
||||
If you rename the DO again later, add another migration instead of just changing the class name.
|
||||
|
||||
## Common Commands
|
||||
|
||||
Typecheck:
|
||||
|
||||
```bash
|
||||
npm run typecheck
|
||||
```
|
||||
|
||||
Local dev:
|
||||
|
||||
```bash
|
||||
npm run dev
|
||||
```
|
||||
|
||||
Deploy:
|
||||
|
||||
```bash
|
||||
npm run deploy
|
||||
```
|
||||
|
||||
## Operational Notes
|
||||
|
||||
A few things matter in practice:
|
||||
|
||||
- Rapid reconnect testing can trigger Workers AI rate limits, especially on TTS.
|
||||
- If you redeploy while a websocket session is active, Cloudflare may log:
|
||||
`This script has been upgraded. Please send a new request to connect to the new version.`
|
||||
That is expected during deploy churn.
|
||||
- If the ESP32 flips into speaking briefly and then falls back, check whether TTS actually produced audio or hit a `429`.
|
||||
- If STT does not advance turns, inspect the STT provider logs first before debugging firmware state.
|
||||
|
||||
## Current Limitations
|
||||
|
||||
This Cloudflare backend is still a pragmatic project backend, not a polished platform product.
|
||||
|
||||
Current caveats:
|
||||
|
||||
- auth is still intentionally stubbed out with comments
|
||||
- DB writes are still placeholders
|
||||
- Workers AI rate limiting can affect repeated testing
|
||||
- the stack is still operationally rough compared with the more mature Deno path
|
||||
|
||||
## Recommended Files To Read
|
||||
|
||||
If you are modifying this backend, read these first:
|
||||
|
||||
- `/Users/akashdeepdeb/Desktop/Projects/ElatoAI/server/cloudflare/src/index.ts`
|
||||
- `/Users/akashdeepdeb/Desktop/Projects/ElatoAI/server/cloudflare/models/session.ts`
|
||||
- `/Users/akashdeepdeb/Desktop/Projects/ElatoAI/server/cloudflare/models/stt.ts`
|
||||
- `/Users/akashdeepdeb/Desktop/Projects/ElatoAI/server/cloudflare/models/llm.ts`
|
||||
- `/Users/akashdeepdeb/Desktop/Projects/ElatoAI/server/cloudflare/models/tts.ts`
|
||||
- `/Users/akashdeepdeb/Desktop/Projects/ElatoAI/firmware-arduino/src/Audio.cpp`
|
||||
- `/Users/akashdeepdeb/Desktop/Projects/ElatoAI/firmware-arduino/src/Config.cpp`
|
||||
|
||||
## Relationship To Other Servers
|
||||
|
||||
Elato currently includes multiple backend paths:
|
||||
|
||||
- `/Users/akashdeepdeb/Desktop/Projects/ElatoAI/server/deno`
|
||||
- `/Users/akashdeepdeb/Desktop/Projects/ElatoAI/server/cloudflare`
|
||||
- `/Users/akashdeepdeb/Desktop/Projects/ElatoAI/server/fastapi`
|
||||
|
||||
Use Cloudflare when you want:
|
||||
|
||||
- Workers + Durable Objects
|
||||
- Cloudflare-hosted STT/TTS
|
||||
- a stateful edge session model
|
||||
|
||||
Use Deno when you want:
|
||||
|
||||
- the most battle-tested Elato path right now
|
||||
- direct provider integrations already working in production
|
||||
|
|
|
|||
Loading…
Reference in a new issue