Open-source alternatives to Granola

screenpipe leads with 88, ahead of meetily (77) and openwhispr (72). Updated from public GitHub data.

The highest-scoring open-source projects in Voice. They are picks, not exact replacements, so read the weaknesses before you switch. The same picks fit if you are leaving Granola AI, Fireflies.ai or Fireflies.

Most stars
meetily 32,358 stars
Permissive license
meetily MIT
Try before you install
meetily live demo
Open-source alternatives to Granola
RankProjectScore
1screenpipeRecords screen and audio locally, makes it searchable for you and your agentsDesktop AI apps22.2k starscustom license88 out of 100
2meetilyDesktop app that records, transcribes and summarizes meetings locally Live demo ↗ (opens in a new tab)Voice32.4k starsMIT77 out of 100
3openwhisprCross-platform voice dictation and meeting transcription app with local or cloud modelsVoice10.5k starsMIT72 out of 100
#4SpeakrSelf-hosted transcription with speaker labels, summaries and chat over recordingsVoice4.1k starsAGPL-3.060 out of 100

Reviews

188 out of 100

screenpipe

Records screen and audio locally, makes it searchable for you and your agents

22.2k starscustom licenselast commit Oct 2026

Its own README calls it an alternative to Granola.

Screenpipe captures screen content (accessibility tree, with OCR as fallback), system audio and microphone with local Whisper transcription, and stores it in a local SQLite database. It exposes a localhost REST API on port 3030, an MCP server, and pipes, which are scheduled agents defined in markdown that query this history. Newer versions also target teams, with central config and per-pipe data permissions.

Strengths

  • Event-driven capture uses the accessibility tree first, OCR only as fallback
  • Local REST API on port 3030, raw SQLite access and an MCP server
  • Pipes are markdown files with per-pipe app, window and endpoint permissions
  • Runs on macOS, Windows and Linux; works with local models such as Ollama

Weaknesses

  • Source-available license: commercial use requires a paid commercial license
  • Continuous capture of everything on screen raises privacy and compliance concerns
  • Capture alone takes about 5-20% CPU and 0.5-3 GB RAM; storage use is unspecified
  • Some features depend on paid plans; Linux capture varies by desktop environment
  • RAM ≥ 8 GB
  • GPU optional
  • Needs Node.js (npx CLI), Ollama (optional local LLM), Deepgram (optional cloud transcription)
  • Models: Whisper Large-V3-Turbo, Deepgram, Ollama, OpenAI-compatible servers
  • port 3030
  • README: alternative to Otter.ai, Granola
277 out of 100

meetily

Desktop app that records, transcribes and summarizes meetings locally

Live demo ↗ (opens in a new tab)32.4k starsMITlast commit Sep 2026

Meetily is a Tauri desktop app with a Rust backend and a Next.js frontend. It captures microphone and system audio, transcribes on-device with Whisper or Parakeet models, and generates summaries through Ollama, Claude, Groq, OpenRouter, or any OpenAI-compatible endpoint. Installers exist for Windows and macOS; Linux requires a source build.

Strengths

  • Transcription runs on-device with Whisper or Parakeet; no cloud needed
  • Summary provider is selectable, including local Ollama or a custom OpenAI-compatible endpoint
  • Records microphone and system audio together with mixing
  • Imports existing audio files and re-transcribes with another model or language (beta)

Weaknesses

  • No prebuilt Linux package; must build from source
  • Speaker diarization is not in the community edition; planned for PRO
  • Custom summary templates, advanced exports and auto-join are PRO-only
  • Standard Windows installer uses Vulkan; CUDA needs a source build
  • GPU optional
  • Needs Ollama (optional, for local summaries)
  • Models: Whisper, Parakeet (ONNX), Ollama, Claude, Groq
372 out of 100

openwhispr

Cross-platform voice dictation and meeting transcription app with local or cloud models

10.5k starsMITlast commit Oct 2026

Its own README calls it an alternative to Granola.

OpenWhispr is an Electron desktop app for macOS, Windows and Linux. A global hotkey records speech and pastes the transcript at the cursor, using local models (Whisper via whisper.cpp, NVIDIA Parakeet via sherpa-onnx) or cloud providers. It also covers meeting transcription with speaker diarization, notes with semantic search, and a voice assistant that talks to GPT, Claude, Gemini or local models.

Strengths

  • Offline transcription with Whisper and Parakeet; audio stays on the device
  • GPU-accelerated local Whisper on Metal, CUDA and Vulkan
  • Local speaker diarization and voice fingerprinting across meetings
  • Public API and MCP server for notes and transcriptions

Weaknesses

  • Intel Macs lack live speaker identification and semantic notes search
  • Cloud sync, sharing and team spaces depend on OpenWhispr Cloud
  • Source setup requires Node.js 24+; no Docker or server deployment
  • Enterprise SSO and SCIM controls are listed, with no edition details in the README
  • GPU optional
  • Needs Node.js 24+
  • Models: Whisper, NVIDIA Parakeet, Cohere Transcribe, llama.cpp local LLMs, OpenAI
  • README: alternative to Granola, Wispr Flow
#460 out of 100

Speakr

Self-hosted transcription with speaker labels, summaries and chat over recordings

4.1k starsAGPL-3.0last commit Oct 2026

Speakr is a Flask and Vue web app that records or imports audio, transcribes it through a configurable ASR backend (WhisperX, OpenAI, Mistral Voxtral, AssemblyAI and others), and generates summaries, action items and events with an LLM. It adds per-recording chat, semantic search across the library, multi-user OIDC login, sharing, retention policies and a REST API with webhooks. Storage is SQLite by default or PostgreSQL.

Strengths

  • Many ASR connectors, auto-detected from config, including self-hosted WhisperX and VibeVoice
  • Speaker diarization, plus cross-recording voice profiles with the WhisperX backend
  • Multi-user with OIDC SSO, groups, sharing, per-user quotas and retention policies
  • REST API with Swagger, signed webhooks, watched import folder and templated auto-export

Weaknesses

  • Voice profiles and top-quality diarization need a separate WhisperX container with a GPU
  • Summaries and chat need an external LLM endpoint, cloud or local
  • Lite image replaces Inquire semantic search with basic text search
  • Versioned 0.10.x alpha; MOSI and other cloud connectors send audio to third parties
  • GPU optional
  • Docker
  • Needs OpenAI-compatible LLM endpoint (OpenRouter, OpenAI or local), ASR backend (WhisperX service or cloud API), SQLite or PostgreSQL
  • Models: WhisperX, OpenAI Whisper and gpt-4o-transcribe-diarize, Mistral Voxtral, AssemblyAI, VibeVoice ASR
  • port 8899