back to top
HomeSoftwareAI ToolsVelo: Fast & AI-Enhanced Desktop Email Client with Offline Support

Velo: Fast & AI-Enhanced Desktop Email Client with Offline Support

- Advertisement -

File Information

FileDetails
NameVelo
Versionv0.3.17
PlatformsWindows, macOS & Linux
LicenseApache-2.0 License (Open Source)
Size6MB (exe) • 18MB (dmg) • 10MB (deb) • 85MB (AppImage)
AI SupportAnthropic, OpenAI GPT, Google Gemini
GitHub RepositoryGithub/velo
Official Sitevelomail

Description

Velo is designed for speed, privacy, and efficiency. Your emails are stored locally in an SQLite database, so you stay in full control of your data without any external servers or hidden trackers.

Its keyboard-first interface allows fast inbox management, while built-in AI features help you summarize threads, compose smart replies, and search naturally.

If you want a fully offline client or prefer connecting to Gmail or IMAP/SMTP accounts, Velo adapts to your workflow.

Screenshots

Features of Velo Email Client

FeatureDescription
Split InboxEmails are auto-sorted into Primary, Updates, Promotions, Social, and Newsletters using rule-based categorization with AI fallback.
AI AssistantChoose Claude, GPT, or Gemini for thread summaries, smart replies, AI drafts, and natural-language inbox search.
Multi-Account SupportConnect Gmail (OAuth), Outlook, Yahoo, iCloud, Fastmail, or any IMAP server with auto-discovery.
Command Palette & SearchGmail-style operators (from:, has:attachment, before:) with fuzzy matching and instant results.
Quick Steps AutomationChain up to 18 actions (archive, label, reply, forward) into one-click workflows.
Snooze & ScheduleSnooze threads to resurface later and schedule emails to send at the perfect time.
Undo SendConfigurable delay window to cancel emails before they are delivered.
Newsletter BundlesGroup newsletters into scheduled bundles and read them on your terms.
Filters & RulesAuto-apply labels, archive, star, or mark as read with AND-logic criteria.
Calendar IntegrationBuilt-in Google Calendar to view events and create meetings without switching apps.
Rich ComposerTipTap editor with formatting, templates, signatures, attachments, and auto-save drafts.
Phishing Detection10 heuristic security checks including homograph attacks and brand impersonation detection.
Themes & CustomizationLight/dark mode, 8 accent colors, adjustable density levels, font scaling, and flexible reading pane.

System Requirements

OSMinimum Requirements
WindowsWindows 10+ (64-bit)
macOSmacOS 11+
LinuxModern 64-bit distro
RAM4GB+ recommended
Disk500MB+ free

How to Install Velo Email Client??

Windows (.exe)

  1. Download the .exe installer.
  2. Right-click → Run as administrator (if needed).
  3. Follow the setup wizard.
  4. Launch Velo from Start Menu.

macOS (.dmg)

  1. Download the .dmg file.
  2. Open and drag Velo to Applications folder.
  3. Open from Applications.

Linux (.AppImage / .deb / .rpm)

Option 1: AppImage (recommended)

  1. Download .AppImage.
  2. Right-click -> Properties -> Enable “Allow executing file as program”.
  3. Double-click to launch.

Option 2: Debian / Ubuntu (.deb)

  1. Download .deb.
  2. Double-click -> Open with Software Installer -> Click Install.

Option 3: RPM (Fedora, RHEL, CentOS)

  1. Download .rpm.
  2. Open with Software Installer.
  3. Click Install.

Recommended For You: Voicebox: Offline AI Voice Cloning & TTS Studio (Qwen3-TTS, Open Source)

Download Velo: Fast & AI-Enhanced Desktop Email Client For Windows , macOS & Linux

Privacy vs. AI: How Velo Handles Your Data

Velo handles your data in your own machine. While the emails are stored in a local SQLite database, using the AI features sends specific “thread snippets” to the AI providers (OpenAI/Anthropic/Gemini).

Conclusion

Velo combines speed, local privacy, AI enhancements, and native desktop performance in a single email client. Its lightweight design ensures fast startup, minimal memory use, and complete control over your emails. Perfect for professionals and power users who demand more from their email client.

Want more stories worth your time?

Add us to your Google favorites. We cover the tech stories, AI developments, and open-source projects that are easy to miss in the noise.

Add as a preferred source on Google

Don’t miss any Tech Story

Subscribe To Firethering NewsLetter

You Can Unsubscribe Anytime! Read more in our privacy policy

LEAVE A REPLY

Please enter your comment!
Please enter your name here

YOU MAY ALSO LIKE
LLM Wiki Desktop

LLM Wiki: An AI Knowledge Base That Builds Itself

0
Most AI tools can answer questions about your documents. But there's a problem with the usual approach. You add a bunch of files, ask a question and the AI searches through them to find an answer. Ask something else later and it does much of the same work again. LLM Wiki on the other hand, uses an LLM to build a persistent, structured wiki from them. Add a collection of documents and LLM Wiki analyzes them, creates pages for important entities and concepts, connects related information and keeps the knowledge base updated as you add more sources. That means your knowledge doesn't just sit inside a folder waiting to be searched. It gradually becomes an interconnected collection of information that you can browse, search and ask questions about.
hister search engine

Hister: Your Own Private Search Engine for Web Pages and Files

0
You know that page you read three months ago and somehow can’t find again? Hister is built for exactly that problem. It turns the web pages you visit and the files you keep into your own searchable index, so you can search the actual content instead of trying to remember a title, URL, or where you saved it.
Atomic Chat App

Atomic Chat: Run Open-Weight LLMs Locally on Windows, macOS & Linux

0
Want to run an AI model locally, but still use it with the tools you already have? Atomic Chat makes that possible. It lets you run open-weight LLMs from Hugging Face on your own computer, then exposes them through an OpenAI-compatible API so coding agents, CLIs, IDE plugins and other apps can use your local models too. You can run models such as Llama, Gemma, Qwen, Mistral and Phi, use Atomic Chat as a regular AI chat app, or connect it to tools such as OpenCode, Goose and Kilo Code. Your local conversations and API keys can stay on your machine, while cloud providers such as OpenAI, Anthropic, Mistral and Groq are available when you need them. Under the hood, it also supports multiple inference engines and performance features such as speculative decoding, Flash Attention and TurboQuant on supported models and hardware. So you're getting more than a local chatbot. Atomic Chat can act as the local AI layer behind the rest of your setup.