Dograh AI - Open Source Voice AI Platform- Low Code Workflow Builder for voice Agents
If you’ve ever dealt with a robotic voice assistant, slow responses, scripted replies and expensive pricing you know the frustration. Most platforms also limit trials, require heavy coding, and lock you into proprietary ecosystems.
Dograh AI changes that narrative with an open-source voice platform and a low-code workflow builder. It’s built for developers, tech teams, and indie hackers across industries looking to integrate powerful, customizable voice automation into their workflows.
What is Dograh AI ?
Dograh AI is an open source alternative to (Vapi, Retell and Bland AI) built by and for the community of voice automation techies. Offers drag-and-drop workflow builder leisure to create conversational agents in <2 minutes.
Allow users to build workflows in plain English, connect any STT, LLM, or TTS, and deploy either on your own servers or use our cloud. Dograh already powers AI voice calling (inbound and outbound), with Twilio(and other telephony) integration and a flexible workflow engine that supports custom multi-agent logic to reduce hallucinations and improve control.
It comes with a prebuilt agent builder - imagine vibe coding your agent.
Mission : Big Tech has monopolized AI. We’re reclaiming it. Just as Wikipedia opened access to information, Dograh aims to make voice technology free and open to all.
We ensure no single company controls the world’s voice. Everything we build is open. Always.
Why it matters : Voice will soon power every interface. No single company should own it. With Dograh, your models, data and GPUs stay in your country - your voice stays yours.
Key Features
Dograh AI designed for startups to fortune 500 companies, enabled ready to go template, <2 minutes deployment, and built in testing AI-to-AI bot calling (LoopTalk). Some prime features are :
1. Voice Capabilities
- 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
2. Developer Experience
- 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
3. Testing and Quality
- 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
Dograh AI is built to empower seamless voice automation, ensuring businesses never miss a single lead.
Join Slack Community and discuss issue with Dograh experts :
For Developers, Startups and Indie Hackers
Build, launch, and scale voice AI without heavy coding and drag-and-drop workflows, Dograh AI highlights :
- 2 Min Launch : In the Create Workflow Dashboard, you can launch a voice agent in just 2 minutes by selecting inbound or outbound calls and writing “For the use cases of…” to describe your agent (e.g., qualify real estate leads, screen candidates, or handle customer support). This description becomes the LLM prompt, instantly generating your customized workflow.
- Pre built Template : Pre-Built Templates let you launch an AI voice agent in just 2 minutes without writing any code or prompts, with easy customization through a global node for user-specific preferences.
- License : Dograh AI is released under the BSD 2-Clause License, a true FOSS license with full flexibility, compatibility and the freedom to use, modify, and distribute.
How to Build AI Voice Agent - Step by Step with Dograh
If you’re not tech-savvy, try the Dograh AI hosted version
1. Quick Start Instructions
Getting Started with Dograh AI OSS Voice AI Platform
Step by step written guide to building and deploying your first voice AI Agent
- Open Dashboard: Launch http://localhost:3000 on your browser.
- Choose Call Type: Select Inbound or Outbound calling.
- Name Your Bot: Use a short two-word name (e.g., Lead Qualification).
- Describe Use Case: In 5–10 words (e.g., Screen insurance form submissions for purchase intent).
- Launch: Your bot is ready! Open the bot and click Web Call to talk to it.
2. Simple 2 Step Agent Builder
Describe use case
Auto-generated templates - test your bot and customize quickly
3. Configuring Your Assistant
Service Configuration - LLM Configuration, TTS Configuration, STT Configuration
- LLM Configuration : Users can customize their LLM by selecting providers like Dograh, Azure, Groq, Google or OpenAI. Just plug in your API key and choose a model Default, Fast, or Accurate.
- TTS Configuration : Users can customize their TTS service by selecting providers like Dograh, Deepgram, ElevenLabs or OpenAI. Just plug in your API key and choose model and voice Default, Joey or Rachel.
- STT Configuration : Users can customize their STT service by selecting providers like Dograh, Deepgram, Cartesia or OpenAI. Just plug the API key and choose a model.
4. Functions
Dograh AI Functions for more customization
- Custom Tools : Dograh’s Custom Tools include Active Workflows, Campaign management, Automation options, LoopTalk (AI-to-AI Testing - Beta), plus a complete Usage dashboard with billing insights, workflow run filters, usage history, daily reports, and access to the Developer Portal.
- Integrations : Dograh offers seamless integrations with Gmail, Google Sheets, and Slack for smooth workflow automation.
A Detailed Comparison with other Platform
Dograh AI detailed comparison with alternative LiveKit/Pipecat and Retell/Bland/Retell highlights :
Core Differentiators
- 100% open source and self-hostable
- AI Calling, integrations, Analytics
- Bring-your-own-keys or use Dograh’s fine tuned models for voice ai
- Multi-agent AI workflow builder
- AI-to-AI testing suite (in beta)
- Built by exited founders, ex-CTOs, and YC alumni
Challenges and Considerations
Potential Challenges in Implementing AI Voice Assistants
- Latency : Maintaining ultra-low latency during heavy concurrent usage is challenging, especially when multiple AI models or external APIs are involved.
- Voice and Language Quality : Natural, accurate speech across accents and noisy environments is tough, and many platforms still struggle with real-time adaptation.
- Reliability and Maintenance : Long or multi-party calls increase complexity risking context loss, hallucinations and tougher logic testing as dialogs scale.
Practices for Overcoming These Challenges
- Optimize for Latency : Choose platforms with sub-800ms latency, edge/streaming support, real-time analytics and stress test under peak load before scaling.
- Automated Testing : Use automated agent testing (like Dograh AI’s AI-to-AI LoopTalk), detailed analytics, and alerting to keep logic reliable and reduce failures.
- Proactive Monitoring : Choose tools with detailed usage tracking, cost forecasting, and flexible pricing, like Dograh AI’s usage dashboard and campaign controls.
Dograh AI Reddit Reviews
The Bottom Line: Is Dograh AI Worth it ?
If you want an open-source, developer-friendly, low-code voice automation platform with ultra-low latency, flexible integrations, strong customization and detailed call reports and dashboards and you’re comfortable managing infrastructure then yes, Dograh AI delivers excellent value and scalability.
How to Build AI Voice Agent - Step by Step with Dograh