# Conversationaly – Privacy-First AI Meeting Assistant

> Source: <https://github.com/bykof/conversationaly/>
> Published: 2026-08-19 14:26:42+00:00

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](https://github.com/Zackriya-Solutions/meeting-minutes), rebuilt around [transcribe.cpp](https://github.com/handy-computer/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 bundled`llama-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](https://github.com/bykof/conversationaly/releases) when a version is tagged.

Requires Rust, Node.js, pnpm, and cmake. See [docs/BUILDING.md](/bykof/conversationaly/blob/main/docs/BUILDING.md) for per-platform prerequisites.

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

# macOS
./clean_build.sh

# Linux (auto-detects GPU backend)
./build-gpu.sh

# Windows
clean_build_windows.bat
```

Linux specifics: [docs/building_in_linux.md](/bykof/conversationaly/blob/main/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](/bykof/conversationaly/blob/main/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](/bykof/conversationaly/blob/main/CLAUDE.md); GPU backend details in [docs/GPU_ACCELERATION.md](/bykof/conversationaly/blob/main/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](/bykof/conversationaly/blob/main/CONTRIBUTING.md) for project structure and guidelines.

MIT — see [LICENSE.md](/bykof/conversationaly/blob/main/LICENSE.md).

- Conversationaly is a fork of
[Meetily](https://github.com/Zackriya-Solutions/meeting-minutes)by Zackriya Solutions, which it builds on under the MIT license. - Transcription runs on
[transcribe.cpp](https://github.com/handy-computer/transcribe.cpp), built on[ggml](https://github.com/ggerganov/ggml)/[whisper.cpp](https://github.com/ggerganov/whisper.cpp). - Local LLM inference uses
[llama.cpp](https://github.com/ggerganov/llama.cpp)via[llama-cpp-2](https://crates.io/crates/llama-cpp-2). - We borrowed some code from
[Screenpipe](https://github.com/mediar-ai/screenpipe)and[transcribe-rs](https://crates.io/crates/transcribe-rs). - Import & Enhance was contributed by
[Jeremi Joslin](https://github.com/jeremi), improved by[Vishnu P S](https://github.com/p-s-vishnu)and[Mohammed Safvan](https://github.com/mohammedsafvan). - Thanks to
**NVIDIA** for the**Parakeet** and**Nemotron** speech models, and to the teams behind the other model families in the catalog.