TL;DR:
We built this because we kept hitting the same frustrations. You've got only two choices today. One, you pay a platform fee to any of the 300+ voice AI companies for a comfy UI. Or you build directly on Dograh, Pipecat or LiveKit, where every prompt tweak means a code change and a redeployment. For anyone shipping for clients or any production use case, that's a constant bottleneck.
We wanted a platform where the code is yours, the data stays in your infrastructure, and debugging means reading a trace, not filing a ticket.
1. Dograh 👑
I've built voice agents before, but when it came to shipping them for production, I couldn't find a platform that worked quickly in 2 minutes - until we started building Dograh.
It's an open-source voice AI platform with a visual workflow builder, built-in telephony, and post-call analytics out of the box. Alternative to Vapi, Retell, and Bland, but self-hostable and BSD-2 licensed.
You get a canvas where you connect nodes instead of writing Python, so prompt tweaks don't mean a redeploy. Voicemail detection, call transfer, variable extraction, knowledge base, and CRM connectors all come standard. Same feature set whether you self-host or use the managed cloud.
It has native support for BYOK (bring your own key) across every layer. Deepgram or Whisper for STT, ElevenLabs or Kokoro for TTS, and any LLM for the brain. Want to run everything locally? Swap in self-hosted models through the UI, no code required.
Check it. https://docs.dograh.com/getting-started
Youtube link: https://www.youtube.com/watch?v=sxiSp4JXqws
Star the Dograh repo ⭐ → https://github.com/dograh-hq/dograh
2. Pipecat
Building a voice AI prototype is one thing, but owning the audio pipeline in production is a different ball game. Pipecat is the Python framework from the Daily.co team for engineers who want full control over how audio frames move through an agent.
The framework handles STT, voice activity detection, LLM, and TTS as composable stages. Integration coverage is wide, including Deepgram, ElevenLabs, Cartesia, Kokoro, Whisper, Gemini, and several dozen others. Pipecat Cloud is available if you want to skip the ops side. Of the three frameworks on this list, Pipecat is the one I'd bet on in the long term if you're comfortable with Python and want to own the pipeline.
The tradeoff is that Pipecat doesn't ship anything above the framework layer: no visual builder, no post-call analytics, no CRM connectors, no QA tooling. Every change to conversation logic means editing Python, committing, and redeploying. Fine if you have an engineering team with the bandwidth to build the platform layer on top. Rough if you want a working system on day one.
Check it out: https://docs.pipecat.ai/overview/introduction
Star the Pipecat repo ⭐ →https://github.com/pipecat-ai/pipecat
3. LiveKit Agents
Building voice AI without battle-tested real-time infrastructure is a disaster waiting to happen. Audio is unforgiving and the moment you have packet loss, multi-party rooms, or browser-to-browser calls, rolling your own transport layer becomes a nightmare.
LiveKit Agents, a WebRTC-native voice framework from LiveKit, is built on top of their widely used real-time media server.
It's organised as composable pieces, including the core media server, the Agents framework for voice AI logic, and LiveKit SIP for PSTN bridging.
In addition, they offer a managed cloud option if you don't want to run the media server yourself, handling scaling, geographic distribution, and SIP trunking for you.
The easiest way to get started is to use the SDK.
The tradeoff is the same as Pipecat. Code-first SDK, no visual interface, no built-in analytics or CRM tooling. Getting a call out the door means wiring up the media server, the agent worker, and the SIP bridge separately. LiveKit Agents is overkill unless you're already using LiveKit for something else, or you genuinely need WebRTC multi-party. For a standard inbound or outbound phone agent, Pipecat is simpler, and Dograh is faster to ship.
For more, refer to their documentation.https://docs.livekit.io/intro/overview/
Star the LiveKit Agents repository ⭐ → https://github.com/livekit
- Vocode
Building a voice AI prototype is one thing, but inheriting a dead codebase is another. What can be a bigger time sink than picking a framework that looks alive in search results but is actually abandoned?
Vocode was one of the earlier Python libraries in this space and introduced useful abstractions when it launched. Active development has largely stopped, with minimal commits for well over a year, unanswered issues, and an architecture that predates speech-to-speech models and sub-500ms pipelines.
Building a new production system on Vocode means inheriting technical debt without an active maintainer behind it. Don't. Start with Dograh, Pipecat, or LiveKit instead.
Check out here:https://docs.vocode.dev/welcome
Star Github repository: https://github.com/vocodedev
| Feature | Dograh | Pipecat | LiveKit Agents | Vocode |
|---|---|---|---|---|
| Pricing | Free OSS + optional cloud | Free OSS | Free OSS | Free OSS |
| Visual workflow builder | Yes | No | No | No |
| Self-hostable | Yes | Yes | Yes | Yes |
| BYOK for STT, TTS, LLM | Yes | Yes | Yes | Yes |
| Production features (tools, QA, telephony) | Yes | No | No | No |
Thanks for reading the post.
Let me know in the comments below if any other open-source voice AI tools or frameworks have helped you ship agents to production.
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