Launching today
Dograh
The open source VAPI alternative
72 followers
The open source VAPI alternative
72 followers
Closed voice platforms make you rent your own agents. Dograh is completely open source- nothing is gated. Visual flow builder, add your model key across 30+ integrations or use local models, telephony, human transfer, and advanced QA & monitoring - all free to self-host in one command. Also connect your claude code with MCP to build voice agents for a use case or call recordings.
Dograh
Hey Product Hunt, I'm Pritesh, one of the makers of Dograh.
Dograh is an open source platform for building voice AI agents - calling agents that answer calls, book appointments, qualify leads, or run payment reminders over the phone.
Here's the problem we kept hitting. AI is getting locked up. Closed platforms make you rent your own agents on their cloud, at their price. And right now big labs are lobbying to restrict open source AI itself. We run on one belief: no company should own voice AI.
So from day 0 we open sourced every line. BSD-2 license, one command to install, nothing held back, nothing gated. Everything runs on your own server, and it's yours. You build phone agents and also own the whole stack.
That is the difference. There is no platform to rent here. No per-minute fee, no gated tier. Bring your own keys, or run local models completely offline, even air-gapped. And it's MCP native - connect Claude Code and build a voice agent by chatting with it.
Inside you still get everything the closed platforms sell:
Visual flow builder - no glue code
Real time speech to speech - 70+ languages with mid-call switching
Telephony + warm handoff to a human
Call recordings with automatic QA
Free REST API
Why that matters:
The payoff is ownership - your only cost is your model bill
Your data never leaves your servers - compliance gets simple
No glue code - you ship in hours
It fits developers and agencies building agents for clients or their internal use case, and compliance-heavy teams in healthcare, finance, and collections. Common uses today include appointment booking, lead qualification, support lines, payment reminders.
We are a small team. A star on GitHub or honest feedback here does more for us than you would think.
Install it, break it, tell me what's missing. I'm here all day.
But first, try a Dograh voice agent now.
DeepGuard
It is exciting to see voice AI move toward open source. As these models scale, how are you approaching the challenge of real-time sentiment analysis and behavioral tracking for agents during live calls, and what metrics do you prioritize to measure conversational quality without compromising user privacy?
Dograh
@lucianb - Thanks, and good question. Sentiment scores are easy to compute but hard to act on mid-call unless we have super robust orchestrator spanning multiple LLMs of different sizes and capabilities (something we are working on).
The signals that actually predict a bad conversation are structural and post call: how often the caller repeats themselves, how often they interrupt, how long the silence runs before they answer (dead air), whether they ask for a human and refuse to talk to the agent. We handle this using a native QA node in workflow builder and also integrate with solutions like Tuner, Noveum. On quality we hold one metric above the others: did the caller get what they called for. Everything else is a proxy for it.
Privacy is where open source does the real work. Self-hosted, the audio never leaves your infrastructure, so there is no third party to trust with it. You keep the derived signals and drop the recording.
At floworks, we tried a few voice agent platforms before landing on Dograh, and the self hosted option made it a no brainer. Feels like it's built by people who actually build voice agents themselves. Excited to see where this goes.
Dograh
@ritesh2503 - Thanks for your appreciation. Yes, we as builders are super passionate about what we are building for the open source ecosystem and community. Our mission is to enable every company in the world to own and operate their voice AI agents securely and efficiently.
Dograh
@ritesh2503 Thanks for the support! Glad the self-hosted approach worked for your team at Floworks. Excited to keep building with feedback from teams using Dograh in production.
Dograh
Thanks Ritesh- we believe in self hosted voice ai and also open weight models. We will keep building for the dev community :)
SigNoz
What do you lose when you move to speech to speech?
Dograh
@ankitnayan - Thanks for your comment. Speech to speech are excellent models which offer language switching and lower latency. Many labs are working on Speech to Speech models, like @Google (Gemini Live), @OpenAI (OpenAI Live), @Grok (Grok AI) etc. @Dograh support these and many more.
The only challenge currently is that you cant use custom cloned voice with them, and the tool calling can sometimes be a bit unpredictable. However, we are seeing some very rapid progress in the space and these are bound to improve in short term.