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dograh

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Self-hosted voice AI platform — Vapi/Retell alternative with MCP and visual workflow builder

التثبيت

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

تشغيل في Manus

المصدر

RajeshKalidandi

RajeshKalidandi/awesome-niche-tools

فتح مستودع GitHub عرض مستودعات المنشئ

تنزيل

تشغيل في Manus

المهن ذات الصلةSOC

استنادا إلى تصنيف SOC المهني

مطوّرو البرمجياتمهن الحاسوب والرياضيات·SOC 15-1252

SKILL.md

readonly

name	dograh
description	Self-hosted voice AI platform — Vapi/Retell alternative with MCP and visual workflow builder
tags	["voice-ai","telephony","selfhosted","mcp","stt","tts"]
related_skills	["hermes-agent"]

Dograh — Self-Hosted Voice AI Platform

Build, deploy, and manage voice AI applications on your own infrastructure. MCP native.

Prerequisites

Python 3.10+
API keys for STT/TTS providers (Deepgram, ElevenLabs, OpenAI, etc.)
Telephony provider (Twilio, Vonage) for inbound/outbound calls

Installation

git clone https://github.com/dograh-hq/dograh.git
cd dograh
pip install -e .

Configuration

# Set provider keys
export OPENAI_API_KEY=*** DEEPGRAM_API_KEY=*** ELEVENLABS_API_KEY=***
# Or use .env file
cp .env.example .env

Usage

Start the server

dograh serve --port 8080

Create a voice workflow

curl -X POST http://localhost:8080/api/workflows \
  -H "Content-Type: application/json" \
  -d '{
    "name": "appointment-scheduler",
    "voice": "alloy",
    "llm": "gpt-4",
    "prompt": "You are a friendly appointment scheduler. Help callers book appointments.",
    "max_duration_seconds": 300
  }'

MCP Server (for AI agent integration)

dograh mcp --port 8090
# Add to agent config:
# "mcpServers": {"dograh": {"command": "dograh", "args": ["mcp", "--port", "8090"]}}

Common Pitfalls

Latency: First response may be slow (LLM cold start). Use streaming for real-time feel.
Telephony costs: Self-hosting eliminates platform fees, but you still pay Twilio/Vonage per minute.
Voice quality: Test with your target TTS provider — some voices sound robotic at speed.
Concurrent calls: Default config handles ~10 concurrent calls. Scale workers for more.
MCP auth: When exposing MCP server, add authentication for production use.

Verification

# Start server
dograh serve --port 8080 &

# Health check
curl http://localhost:8080/health

# Create test workflow
curl -X POST http://localhost:8080/api/workflows \
  -H "Content-Type: application/json" \
  -d '{"name": "test", "voice": "alloy", "llm": "gpt-4", "prompt": "Say hello"}'

# List workflows
curl http://localhost:8080/api/workflows

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Dograh — Self-Hosted Voice AI Platform

Build, deploy, and manage voice AI applications on your own infrastructure. MCP native.

Prerequisites

Python 3.10+

API keys for STT/TTS providers (Deepgram, ElevenLabs, OpenAI, etc.)

Telephony provider (Twilio, Vonage) for inbound/outbound calls

Installation

git clone https://github.com/dograh-hq/dograh.git cd dograh pip install -e .

Configuration

# Set provider keys export OPENAI_API_KEY=*** DEEPGRAM_API_KEY=*** ELEVENLABS_API_KEY=*** # Or use .env file cp .env.example .env

Usage

Start the server

dograh serve --port 8080

Create a voice workflow

curl -X POST http://localhost:8080/api/workflows \ -H "Content-Type: application/json" \ -d '{ "name": "appointment-scheduler", "voice": "alloy", "llm": "gpt-4", "prompt": "You are a friendly appointment scheduler. Help callers book appointments.", "max_duration_seconds": 300 }'

MCP Server (for AI agent integration)

dograh mcp --port 8090 # Add to agent config: # "mcpServers": {"dograh": {"command": "dograh", "args": ["mcp", "--port", "8090"]}}

Common Pitfalls

Latency: First response may be slow (LLM cold start). Use streaming for real-time feel.

Telephony costs: Self-hosting eliminates platform fees, but you still pay Twilio/Vonage per minute.

Voice quality: Test with your target TTS provider — some voices sound robotic at speed.

Concurrent calls: Default config handles ~10 concurrent calls. Scale workers for more.

MCP auth: When exposing MCP server, add authentication for production use.

Verification

# Start server dograh serve --port 8080 & # Health check curl http://localhost:8080/health # Create test workflow curl -X POST http://localhost:8080/api/workflows \ -H "Content-Type: application/json" \ -d '{"name": "test", "voice": "alloy", "llm": "gpt-4", "prompt": "Say hello"}' # List workflows curl http://localhost:8080/api/workflows