AI Frameworks · Zackriya-Solutions

meetily

Meetily is a self-hosted, open-source AI meeting assistant built in Rust that runs entirely on your local machine. It captures, transcribes, and summarizes meetings in real-time using local speech-to-text models (Whisper/Parakeet) and LLMs (Ollama or cloud providers), with no data sent to the cloud.

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Source: GitHub — github.com/Zackriya-Solutions/meetily

20.8k

GitHub stars

2.1k

Forks

Rust

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	Zackriya-Solutions/meetily
Owner	Zackriya-Solutions
Primary language	Rust
License	MIT — OSI-approved
Stars	20.8k
Forks	2.1k
Open issues	261
Latest release	v0.4.0 (2026-06-05)
Last updated	2026-06-05
Source	https://github.com/Zackriya-Solutions/meetily

What meetily is

Tauri-based desktop application (macOS/Windows/Linux) with Rust backend and Next.js frontend, integrating local transcription engines (Whisper, Parakeet via whisper-cpp), optional Ollama for local LLM summarization, and GPU acceleration (Metal on Apple Silicon, CUDA/Vulkan on Windows/Linux). All processing occurs on-device with optional custom OpenAI-compatible endpoint support.

Quickstart

Get the meetily source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/Zackriya-Solutions/meetily.git2cd meetily3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Enterprise/Legal/Healthcare Compliance

Organizations handling sensitive conversations (contracts, medical records, M&A discussions) requiring GDPR, HIPAA, or SOC 2 compliance with zero cloud data exposure and full audit trails.

Defense & Government Contractors

Teams discussing classified or restricted information where local-first processing eliminates vendor lock-in and third-party data access risks.

Privacy-Conscious Teams & Consultants

Distributed teams, freelancers, and agencies that value data sovereignty and want to avoid recurring API costs while maintaining offline-first capability.

Implementation considerations

Ensure target systems meet GPU/CPU requirements for acceptable transcription latency; test with expected meeting lengths and quality expectations.
Plan for local storage: audio files, transcripts, and large LLM models (multi-GB) consume significant disk space; implement retention policies.
Ollama must be separately installed and configured for local LLM summaries; alternatively configure cloud provider credentials if using Claude/Groq/OpenAI.
Verify audio device driver support (macOS/Windows) and microphone permissions; test with actual meeting platforms (Zoom, Teams, etc.) before rollout.
Establish update strategy for Whisper/Parakeet models and Ollama; newer models improve accuracy but require re-download and local re-deployment.

When to avoid it — and what to weigh

No Local GPU or Weak CPU — Real-time transcription and summarization require meaningful compute resources. Older machines or those without GPU acceleration will struggle with latency or long processing delays.
Require Multi-Language or Niche Domain Models at Scale — While Whisper supports multiple languages, accuracy for specialized domains (medical terminology, legal jargon) may require fine-tuning not included in base distribution.
Need Cross-Platform Team Synchronization Out-of-the-Box — Meetily is a local-first desktop tool. Sharing, versioning, and collaborative editing of notes requires custom integration or external tools; not a built-in team platform.
Expect Hands-Off Deployment — Self-hosted Ollama setup, GPU driver configuration, and model downloads require technical effort. Not suitable for non-technical users or plug-and-play scenarios.

License & commercial use

MIT License – permissive open-source license allowing commercial use, modification, and distribution with minimal restrictions (attribution and license notice required).

MIT is an OSI-approved permissive license explicitly permitting commercial use. However, no commercial support SLA, liability indemnity, or warranty is stated in the license or project docs. Any commercial deployment should conduct due diligence on third-party dependencies (Tauri, whisper-cpp, Ollama) and their respective licenses. Professional support availability is unclear; the project references a PRO tier and commercial contact but terms are not defined in this data.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	High

Security considerations

Local-first processing eliminates cloud data exposure, reducing compliance risk for regulated industries. However, no documented threat model, encryption-at-rest spec for local files, or audit log capability is mentioned. Audio file permissions, transcript file security, and supply-chain security of dependencies (Tauri, Ollama, Whisper) are not addressed. Pre-release status and 261 open issues may include unpatched vulnerabilities. Security audit status is unknown. Organizations should perform threat modeling, dependency analysis, and testing before sensitive use.

Alternatives to consider

Otter.ai / Rev.com

Cloud-based, easier onboarding, better multi-language support, but requires data transmission and recurring API costs; unsuitable for privacy-critical use.

Riverside / Descript

Professional video/audio editing with built-in transcription and summaries, but cloud-dependent, expensive, and less privacy-focused; better for content production workflows.

Local Whisper + Custom Python Script

Pure open-source DIY approach (no GUI, full control), but requires software engineering skill; suitable only for technically advanced teams with custom requirements.

Software development agency

Build on meetily with DEV.co software developers

Download v0.4.0 for macOS or Windows, test with your actual meeting workflows, verify GPU acceleration on your hardware, and assess Ollama/LLM integration needs. Consult security and legal teams before production use with sensitive data.

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meetily FAQ

Does Meetily send any data to the cloud?

No. Meetily is designed to process audio, transcription, and summarization entirely on your local machine. Cloud LLM providers (Claude, Groq, OpenAI) are optional; Ollama local LLM is recommended for zero-cloud operation.

Can I run Meetily without a GPU?

Yes, but expect slower transcription and summarization. CPU-only inference is supported but may not meet real-time latency expectations. GPU acceleration (Metal on macOS, CUDA/Vulkan on Windows/Linux) is strongly recommended for production use.

Is speaker diarization available now?

Not in v0.4.0 (current release). README notes speaker diarization is planned for Meetily PRO in mid-June. Standard edition currently transcribes without speaker labels; identify speakers from manual notes.

Can my team collaborate on meeting notes in real-time?

Meetily is a local desktop app without built-in team collaboration. Notes can be exported (text, JSON) and shared manually, but shared editing requires integration with external tools (Git, Google Docs, Notion, etc.).

Custom software development services

Need help beyond evaluating meetily? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and ai frameworks integrations — and maintain them long-term.

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