Hello all, I'm trying to investigate a few hypothesis I have regarding the AI Voice Agent market. My questions are mostly related to security, observability, billing and load management. In order to do so, I've built the following Google Form: https://forms.gle/oFeM9J9WV9DRX9267 If you could please answer it, I would highly appreciate it - also, once I have all the data compiled - I will publish a post with all my findings, so that people can learn from this study as well. Much Appreciated.

So, over the past few weeks - I've been neck deep into working with PIpecat, LiveKit and Vibe Coding. Mainly, I wanted to see what kind of milage I can get from Vibe Coding tools, and in order to test it - what's a better way than build a Pipecat/LiveKit implementation? So, I decided to examine 3 primary tools: - Claude Code - Using Sonnet 3.5 (using CLI) - OpenCode - Grok Code Fast 1 - Google Antigravity - Using Gemini 2.5 Below are my conclusions, split into several categories. 💵 Financials: Most expensive to use - Claude Code Least expensive to use - OpenCode 😡 Developer Experience: Best experience - Google Antigravity Worst experience - Claude Code 💪 Reliability: Most reliable - Claude Code Least reliable - OpenCode 🚅 Performance: Fastest planning and building - Google Antigravity Slowest planning and building - OpenCode So, overall - there is no "one tool to rule them all" here - and what I found out that each tool is really good at performing specific tasks. Here is what I've learned about how to "leverage" these tools in order to build something successful: - Planning can be performed with either OpenCode of Google antigravity. Google provides free developer credits for Antigravity, and their deep-thinking and reasoning engine, when applied to software architecture and design works very well. - Backend development with either ClaudeCode or Google Antigravity. When coupled with proper topic sub-agents, these are really powerful tools. For some odd reason, Claude Code is far more capable at handling complex architectures, while Google Antigravity leans towards the "hacker style" coding. - UI/UIX development - without any question, OpenCode did a better job. It was far more capable in spitting out hundreds of lines of working UI/UX code - even faster that Claude. However, if at some point it gets stuck on a specific UI component package, it may require Claude to show it the light - so pay attention to what it's doing. - Code Review, Security and Privacy - without any question, Claude is the winner here - with potentially the most extensive availability of sub-agent topic experts.

Hi All, For those of you who are not familiar with Dograh, it is an open source project aimed at providing a VAPI like functionality. Dograh is also available as a cloud offering, enabling its users to build their own AI Voice Agent experiences using their infrastructure. Dograh is based on Pipecat for its AI orchestration, coupled with your favorite AI Models and Dograh's models as well. I recommend taking a look at it - https://www.dograh.com/ Now, by default - Dograh supports Twilio, Vonage, and Vobiz. However, this week Cloudonix patches were introduced - enabling Cloudonix telephony services ontop of Dograh. So, you can use it to connect your own phone system or any SIP compatible phone provider. In about 2 weeks we'll be holding a special Dograh + Cloudonix dedicated session, where we will show how this integration works - and what features it brings. For more information, feel free to DM me and register to our upcoming office hours sessions to learn more about this integration. https://us02web.zoom.us/meeting/register/6D63tRaYSDihkJtUlpNp-A

Has anyone successfully integrated LiveKit with Asterisk?

𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗩𝗼𝗶𝗰𝗲 𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗥𝗲𝘃𝗶𝗲𝘄 Hi everyone, Running a production voice agent (~500-600 calls/day) with 𝗽𝗶𝗽𝗲𝗰𝗮𝘁-𝗳𝗹𝗼𝘄𝘀. Would appreciate feedback on my architecture. 𝗪𝗵𝘆 𝗦𝗲𝗹𝗳-𝗛𝗼𝘀𝘁𝗲𝗱: Tried Pipecat Cloud but Talkdesk is not supported. WebSocket is mandatory - cannot use WebRTC. 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Talkdesk ──WS──► Bridge Server (Azure App Service) ──WS──► Pipecat Agent (Azure VM + Docker) • Bridge converts μ-law 8kHz ↔ PCM 16kHz (resampling on every chunk) • 3 Docker containers behind Nginx load balancer • Each handles ~15 concurrent calls ──► Each container: 3GB RAM, 0.75 CPU limit • CI/CD: GitHub Actions → Docker Hub → Azure VM pull 𝗔𝗜 𝗦𝘁𝗮𝗰𝗸: • STT: Azure Speech (Italian) • LLM: OpenAI GPT-4.1 • TTS: ElevenLabs (eleven_multilingual_v2) • VAD: Silero 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝗲𝘁𝘂𝗽 (pipecat-flows): Router Node → detects intent → routes to: • Booking Agent (20+ step flow) • Info Agent (RAG/knowledge base) • [Future] Person specify the doctors name e.g "I want to book appointment with Dr. Jhon for heart checkup." Doctor Booking Agent Agents can transfer between each other during conversation. 𝗠𝘆 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀: 𝟭. 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 feels high. Is the two-hop WebSocket architecture (Talkdesk → Bridge → Pipecat) causing this? Should I merge the bridge into the Pipecat container? 𝟮. Is having a 𝘀𝗲𝗽𝗮𝗿𝗮𝘁𝗲 𝗯𝗿𝗶𝗱𝗴𝗲 for audio conversion a common pattern, or is there a better approach? 𝟯. 𝗥𝗼𝘂𝘁𝗶𝗻𝗴 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: I use a Router node to detect intent and route to agents. But I'm concerned this approach is too rigid. Example: Currently I route to "Booking Agent" when user says "book X-ray". But what if user says "book with Dr. Jhon" or "book with Dr. Jhon at 3pm tomorrow"? Should I create separate agents for each variation? That feels wrong - they're all booking, just with different pre-filled data. Or should the Router extract entities (doctor name, time, service) and pass them as parameters to a single flexible agent that skips steps dynamically? What's the best pattern in pipecat-flows for handling these variations without creating rigid, bounded flows for each request type?