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Conversationaly – Privacy-First AI Meeting Assistant

Conversationaly, a privacy-first AI meeting assistant for macOS, Windows, and Linux, records meetings, transcribes them live, and generates summaries entirely on-device by default, with no account, cloud round-trip, or telemetry. The free, open-source desktop app, a fork of Meetily rebuilt around transcribe.cpp and a bundled llama.cpp sidecar, supports ~87 transcription models across 16 families (default: nemotron-3.5-asr-streaming-0.6b-q8) and offers opt-in cloud STT and LLM providers. It runs a local Gemma 4 model for summaries and supports GPU acceleration via Metal, CUDA, Vulkan, and ROCm.

read3 min views1 publishedAug 19, 2026
Conversationaly – Privacy-First AI Meeting Assistant
Image: Michielbdejong (auto-discovered)

Records your meetings, transcribes them live, and writes the summary — on your machine, with no account and no cloud round-trip unless you deliberately configure one.

Table of Contents #

Conversationaly is a desktop app (macOS, Windows, Linux) that captures your microphone and system audio, transcribes the meeting as it happens, and generates a summary. Transcription models and the summary LLM both run locally by default — nothing is sent anywhere. Cloud providers are available if you want them, but they are opt-in, per-feature.

It is a fork of Meetily, rebuilt around transcribe.cpp and a bundled llama.cpp sidecar. It is fully free — there is no paid tier, no license key, no telemetry.

Local transcription— ~87 models across 16 families (Whisper, Parakeet, Nemotron, Canary, Voxtral, Qwen3-ASR, SenseVoice, Moonshine, GigaAM, …), downloaded on demand. Default:nemotron-3.5-asr-streaming-0.6b-q8

, multilingual across 39 locales.Live transcript— streaming-native models transcribe continuously as you speak; batch-only models are segmented by voice activity and still work live.Built-in AI, no Ollama required— a bundledllama-helper

sidecar runs Gemma 4 locally for summaries, and can also transcribe directly as an audio LLM.Bring your own LLM— summaries via Built-in AI, Ollama, Claude, Groq, OpenRouter, OpenAI, or any OpenAI-compatible endpoint.** Optional cloud STT**— Deepgram, ElevenLabs, Groq, or OpenAI, if you prefer a hosted transcriber.** Professional audio mixing**— microphone and system audio captured together with RMS-based ducking and clipping prevention.** Import & enhance**Beta

— transcribe existing audio files, or re-transcribe a past meeting with a different model or language.Summary templates— pick or write the structure your summaries follow, and set the summary language independently of the spoken one.** GPU acceleration**— Metal on Apple Silicon, CUDA (NVIDIA), Vulkan (AMD/Intel), ROCm (AMD on Linux).** Local storage**— meetings, transcripts, and models live in a SQLite database and a model directory on your disk.

Prebuilt installers (macOS .dmg

, Windows .exe

, Linux .deb

/.rpm

/.AppImage

) are published on the Releases page when a version is tagged.

Requires Rust, Node.js, pnpm, and cmake. See docs/BUILDING.md for per-platform prerequisites.

git clone https://github.com/bykof/conversationaly
cd conversationaly/frontend
pnpm install

./clean_build.sh

./build-gpu.sh

clean_build_windows.bat

Linux specifics: docs/building_in_linux.md.

macOS— microphone, plus screen recording for system audio (ScreenCaptureKit, macOS 13+).** Windows**— microphone; system audio uses WASAPI loopback.

On first launch, onboarding downloads one transcription model and one Gemma 4 tier. From then on:

  • Microphone and system audio are captured, mixed, and written to a recording.
  • The same mixed audio is resampled to 16 kHz and fed to the transcription engine, which emits transcript lines as the meeting runs.
  • When you ask for a summary, the transcript goes to whichever LLM provider you configured — the local sidecar by default.

Everything above is a local process. Cloud STT and cloud summary providers are the only paths that leave your machine, and only when you select one and supply a key.

A single Tauri application: a Rust core (audio capture, transcription, storage, summary orchestration) and a Next.js frontend, communicating over Tauri commands and events. There is no separate server to run.

Details: docs/architecture.md.

cd frontend
pnpm install

./clean_run.sh              # macOS: build and run (info logging)
./clean_run.sh debug        # verbose logging
clean_run_windows.bat       # Windows
./dev-gpu.sh                # Linux

pnpm run tauri:dev          # plain dev mode
pnpm run tauri:dev:metal    # force a specific GPU backend
pnpm run tauri:dev:cuda
pnpm run tauri:dev:vulkan
pnpm run tauri:dev:cpu

Architecture notes and conventions for contributors live in CLAUDE.md; GPU backend details in docs/GPU_ACCELERATION.md.

The

backend/

directory is an archived Python/FastAPI service from before the Tauri rewrite. It is unsupported and not needed to build or run the app.

Issues and pull requests are welcome. See CONTRIBUTING.md for project structure and guidelines.

MIT — see LICENSE.md.

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