[kwindla]

One easy way to build voice agents and connect them to Twilio is the Pipecat open source framework. Pipecat supports a wide variety of network transports, including the Twilio MediaStream WebSocket protocol so you don't have to bounce through a SIP server. Here's a getting started doc.[1]

(If you do need SIP, this Asterisk project looks really great.)

Pipecat has 90 or so integrations with all the models/services people use for voice AI these days. NVIDIA, AWS, all the foundation labs, all the voice AI labs, most of the video AI labs, and lots of other people use/contribute to Pipecat. And there's lots of interesting stuff in the ecosystem, like the open source, open data, open training code Smart Turn audio turn detection model [2], and the Pipecat Flows state machine library [3].

[1] - https://docs.pipecat.ai/guides/telephony/twilio-websockets [2] - https://github.com/pipecat-ai/pipecat-flows/ [3] - https://github.com/pipecat-ai/smart-turn

Disclaimer: I spend a lot of my time working on Pipecat. Also writing about both voice AI in general and Pipecat in particular. For example: https://voiceaiandvoiceagents.com/

[ldenoue]

The problem with PipeCat and LiveKit (the 2 major stacks for building voice ai) is the deployment at scale.

That’s why I created a stack entirely in Cloudflare workers and durable objects in JavaScript.

Providers like AssemblyAI and Deepgram now integrate VAD in their realtime API so our voice AI only need networking (no CPU anymore).

[nextworddev]

let me get this straight, you are storing convo threads / context in DOs?

e.g. Deepgram (STT) via websocket -> DO -> LLM API -> TTS?

[nextworddev]

This is good stuff.

In your opinion, how close is Pipecat + OSS to replacing proprietary infra from Vapi, Retell, Sierra, etc?

[kwindla]

It depends on what you mean by replacing.

The integrated developer experience is much better on Vapi, etc.

The goal of the Pipecat project is to provide state of the art building blocks if you want to control every part of the multimodal, realtime agent processing flow and tech stack. There are thousands of companies with Pipecat voice agents deployed at scale in production, including some of the world's largest e-commerce, financial services, and healthtech companies. The Smart Turn model benchmarks better than any of the proprietary turn detection models. Companies like Modal have great info about how to build agents with sub-second voice-to-voice latency.[1] Most of the next-generation video avatar companies are building on Pipecat.[2] NVIDIA built the ACE Controller robot operating system on Pipecat.[3]

[1] https://modal.com/blog/low-latency-voice-bot - [2] https://lemonslice.com/ = [3] https://github.com/NVIDIA/ace-controller/