dograh-hq / dograh

2026-05-17 · Source: Github Trending: All languages · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Intermediate, long

Summary

Dograh AI is an open-source, self-hostable platform designed for building production-ready voice agents with a drag-and-drop workflow builder. Positioned as an alternative to proprietary solutions like Vapi and Retell, Dograh allows users to create a functional bot in under two minutes. It offers full control and transparency with its BSD 2-Clause license, enabling flexible integration of various LLM, TTS, and STT providers. The platform includes features such as built-in telephony integration, real-time processing, and a comprehensive developer experience with zero-config startup, Python-based architecture, and Docker-first deployment. Dograh also provides robust testing tools, including a test mode, in-dashboard web calls, and a QA node for prompt quality analysis, ensuring developers can inspect and control every aspect of their voice AI system.

Key takeaway

For CTOs or VPs of Engineering evaluating voice AI platforms, Dograh AI presents a compelling option to avoid vendor lock-in and maintain full control over your infrastructure and data. Its open-source nature and self-hostable deployment mean you can customize every aspect, integrate preferred LLM/TTS/STT providers, and ensure data residency compliance. Consider Dograh if your team prioritizes transparency, cost control, and the ability to deeply inspect and modify your voice AI stack.

Key insights

Dograh AI offers an open-source, self-hostable platform for building and controlling voice agents with visual workflows.

Principles

• Open source prevents vendor lock-in.
• Visual builders simplify complex logic.
• Full control requires inspectable code.

Method

Users define voice agent logic via a drag-and-drop workflow builder, integrating custom LLM/TTS/STT, then deploy via Docker for self-hosting or use the managed cloud version.

In practice

• Self-host voice agents with one Docker command.
• Visually map call flows instead of hard-coding.
• Integrate custom LLM/TTS/STT providers.

Topics

• Dograh AI
• Open-source Voice AI
• Self-hostable Platform
• Voice Agent Workflow
• LLM/TTS/STT Integration

Code references

• dograh-hq/dograh
• orgs/dograh-hq

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Engineer, Software Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Github Trending: All languages.