u/nicolotognoni

I'm Nico, building Patter (open-source voice SDK, alpha). I'm at the point where talking to production users beats writing more code.

Looking for 10 conversations specifically with devs who run voice agents in production right now. Pipecat, LiveKit, Vapi with custom LLM, self-hosted, anything that's live.

10 min on a call. You share what's actually painful in production (latency, cost, debugging, compliance, anything).

DM or comment your stack.

Discovery question for folks who shipped voice agents using LangChain or LangGraph in production.

I'm Nico, building an open-source voice SDK (Patter, alpha). Before writing more code I want to talk to 10 production users to understand what actually broke and what worked.

If you're running voice + LangChain in production (regardless of telephony provider), would 10 min on a call work?

Not selling anything. DM or comment your stack.

I'm Nico, building Patter (open-source voice SDK, alpha). Before writing more code I want to talk to 10 people actually running voice agents in production.

Specifically anyone on:

1. Pipecat in production
2. LiveKit Agents in production
3. Vapi with custom LLM endpoint in production

10 min on a call. No pitch, not selling anything.

DM me or comment your stack.

I'm Nico, building Patter (open-source voice AI SDK, alpha). Before I write another line of code I want to talk to 10 people actually running voice agents in production right now.

Specifically looking for:

1. Pipecat in production
2. LiveKit Agents in production
3. Vapi with custom LLM endpoint in production

10 min on a call. No pitch, not trying to sell anything.

DM me or drop a comment with your stack.

Quick context. Spent the last 3 weeks building Patter because we kept hitting the same wall with Vapi and Retell: per-minute markup on top of STT/LLM/TTS that's never explained, zero visibility into which segment of the call is actually expensive.

The wedge we went with: open source the whole SDK (MIT), let people run it in their own process, give them a cost dashboard that breaks down spend per call across STT, LLM, TTS, and telephony. Around 30 providers wired in. TypeScript and Python parity from day one.

never hits our servers, which made compliance conversations easier when we were validating with potential users

Alpha just went live. Very early. We're not selling anything yet, just want devs trying it and telling us what's broken.

github.com/PatterAI/Patter

Happy to answer anything about the build, the positioning against Vapi, or the path to monetization.

Audio

Built Patter over the last 3 weeks: open-source SDK (MIT, alpha) that connects any AI agent to a phone number in 4 lines of code.

Origin: kept hitting the same wall with Vapi/Retell. Opaque pricing, audio routed through their infra, no way to swap providers without rewriting. Decided to build something we'd actually want to use.

Two modes:

1. Tool-call mode: registers with Claude Code or any orchestrator as a tool. Your agent decides "i need to call this number" and Patter handles the voice loop, returns transcript + outcome.
2. Embedded mode: drop it into your own pipeline as a custom voice agent.

Things we wanted that didn't exist:
- Provider swappability (around 30 STT/LLM/TTS, change with one config line)
- Per-segment cost breakdown so we'd know if a call cost was driven by TTS or LLM
- Audio never flowing through someone else's infra
- Real TypeScript and Python parity, not Python-first with a weak JS port

Repo: github.com/PatterAI/Patter

just shipped. Expecting rough edges. Feedback and PRs welcome.

Alpha

Curious what stacks people are actually using right now, and where you're hitting walls.

Some things I've been observing while testing combos:

- Deepgram Nova-3 still the best STT for English, Cartesia is closing the gap on streaming

- ElevenLabs Flash and Cartesia Sonic basically tied for TTS latency

- OpenAI Realtime fastest end-to-end but you give up provider control. Claude/Anthropic adds 200-300ms but conversation quality is noticeably better

- Groq + Llama 3 70B for low-latency reasoning is underrated

Open questions I haven't cracked:

1. For non-English (Hindi, Arabic, Spanish), what's your STT? Nova-3 multilingual works but Sarvam/Gladia might be better for Indic

2. Anyone using Smallest AI Lightning TTS in production? curious about real-world latency

3. For tool-call use cases (orchestrator agents placing calls mid-workflow), how are you handling state across the call boundary?

(Reason I care about this: I open-sourced Patter today, an SDK that lets you swap providers per call without rewriting. github.com/PatterAI/Patter, MIT, alpha, very rough. Built it because I wanted to A/B providers in production.)

Would love to hear what you're running.