An open source framework for voice assistants
I've been obsessed for the past ~year with the possibilities of talking to LLMs. I built a bunch of one-off prototypes, shared code on X, started a Meetup group in SF, and co-hosted a big hackathon. It turns out that there are a few low-level problems that everybody building conversational/real-time AI needs to solve on the way to building/shipping something that works well: low-latency media transport, echo cancellation, voice activity detection, phrase endpointing, pipelining data between models/services, handling voice interruptions, swapping out different…
An open source framework for voice assistants launched on May 13, 2024 with 346 votes, #120 of 4,539 launches that month and more than 99% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
Pipecat is an open source Python framework for building voice and multimodal AI assistants. It addresses core technical challenges in conversational AI including low-latency media handling, echo cancellation, voice activity detection, and model pipelining. The framework is designed for developers creating real-time voice applications like personal coaches and meeting assistants, providing pre-built solutions for problems that previously required custom implementation in each project.
written from the facts on this page · September 2026
From the sources
Open Source framework for voice agents, multimodal apps, and realtime AI. Maintained by Daily and the community. - pipecat-ai/pipecat
In the maker’s words, at launch
I've been obsessed for the past ~year with the possibilities of talking to LLMs. I built a bunch of one-off prototypes, shared code on X, started a Meetup group in SF, and co-hosted a big hackathon. It turns out that there are a few low-level problems that everybody building conversational/real-time AI needs to…
Does a similar job
all alternatives →
Open-source, native audio turn detection model2025 · github.com · ▲126Our goal with this project is to build a completely open source, state of the art turn detection model that can be used in any voice AI application. I've been experimenting with LLM voice conversations since GPT-4 was first released. (There's a previous front page Show HN about Pipecat, the open source voice AI orchestration framework I work on. [1]) It's been almost two years, and for most of that time, I've been expecting that someone would "solve" turn detection. We all built initial, pretty good 80/20 versions of turn detection on top of VAD (voice activity detection) models. And…
Conversational Replicas by Tavus2024 · tavus.io · ▲694Hey there, I’m Brian. I've been shipping conversational models over here at Tavus for the past two years. I want to tell you about our new audio-understanding model: Sparrow-2! It’s a new category of model and a unique new approach to conversational audio. Earlier this year we launched Sparrow-1, (at the time) our SoTA turn taking model. Since our Sparrow-1 launch, I’ve spent a lot of time listening to humans talking and trying to really understand how people know when to talk, when to listen, and when to wait. I’ve also been hunting down failure modes of the current SoTA models. And while…
I built a sub-500ms latency voice agent from scratchMar 2026 · ntik.me · ▲570I built a voice agent from scratch that averages ~400ms end-to-end latency (phone stop → first syllable). That’s with full STT → LLM → TTS in the loop, clean barge-ins, and no precomputed responses. What moved the needle: Voice is a turn-taking problem, not a transcription problem. VAD alone fails; you need semantic end-of-turn detection. The system reduces to one loop: speaking vs listening. The two transitions - cancel instantly on barge-in, respond instantly on end-of-turn - define the experience. STT → LLM → TTS must stream. Sequential pipelines are dead on arrival for natural…
Inworld TTS2025 · inworld.ai · ▲318Hey HN, this is Igor, one of the engineers behind Inworld Runtime, which we're releasing today in public preview. We built it to solve the common problem we and our customers had: engineers spend more time on AI ops and plumbing than on actual feature development. This was often due to the challenge of using Python for I/O-bound, high-concurrency workloads and complexity maintaining pipelines with streams that use always-changing ML models. Our solution is a high-performance runtime written in C++ with the core idea of defining AI logic as graphs. For instance, a basic voice-to-voice…
More ai this month
the category →
Paintings by AI models: programs run through a simulation of oil paint.
AI · 20d ago · stillwet.art
PixVerse R2▲381A real-time world model you can explore and change
AI · 29d ago · world.pixverse.video
Source: Show HN post ↗ · launch text quoted in part
