Moshi AI vs Serp AI

Moshi AI is a speech-native conversational model from Kyutai, a Paris-based open-science research lab. Instead of chaining speech recognition, text generation, and text-to-speech, Moshi processes audio directly and holds full-duplex voice conversations with minimal latency.

Its multi-stream design runs separate channels for the user, Moshi's spoken output, and an Inner Monologue text stream that improves coherence. That setup lets Moshi listen and talk at the same time, handle overlaps, interruptions, and backchanneling like a real conversation rather than rigid speaker turns.

Moshi is built on Helium, a 7B language model, and Mimi, Kyutai's neural audio codec. Weights and inference code ship for PyTorch, Rust, and MLX, and you can try it in the browser at moshi-chat.kyutai.org. Researchers, voice AI developers, and anyone building real-time spoken interfaces will find the most value here.

Serp AI is a voice and audio assistant you run on your own machine. Its open-source SERPy project listens for spoken commands and answers through a large language model, with a synthetic voice you choose yourself.

Compared with cloud assistants tied to one vendor stack, SERPy is a self-hosted GitHub project from the serp-ai organization with conversational memory that adapts to your habits over time. The original marketing page at serp.ai/tools/ai-voice-assistant now returns 404, so the repository is the live home for installs and documentation.

SERPy targets developers and hobbyists who want a Jarvis-style desktop assistant they can modify. The README points to a free download link and demo videos, and the repo tags cover voice activation, scheduling, email, and writing assistant use cases.