Happy vs Unsloth

Side-by-side comparison of two AI agent tools

Short answer

  • Unsloth is growing faster: +2,972 GitHub stars in the last 30 days vs +1,201 for Happy.
  • Pick Happy for: mobile and Web client for Codex and Claude Code, with realtime voice, encryption and fully featured. Pick Unsloth for: unsloth Studio is a web UI for training and running open models like Qwen, DeepSeek, gpt-oss and Gemma locally.

From GitHub data refreshed daily.

Happyopen-source

Mobile and Web client for Codex and Claude Code, with realtime voice, encryption and fully featured

Unslothopen-source

Unsloth Studio is a web UI for training and running open models like Qwen, DeepSeek, gpt-oss and Gemma locally.

Metrics

HappyUnsloth
Stars24.0k77.1k
Star velocity /mo1.2k3.0k
Commits (90d)3173.8k
Releases (6m)1010
Overall score0.84452887180804880.9293743798138157

Pros

  • +提供完整的移动端访问能力，支持 iOS、Android 和 Web 平台
  • +端到端加密保护代码安全，开源架构支持代码审计
  • +无缝设备切换体验，一键在手机和桌面间转换控制权
  • +显著的性能优化：训练速度提升2倍，显存使用减少70%，显著降低硬件成本和训练时间
  • +广泛的模型支持：支持500+种模型训练，包括主流的开源模型如Qwen、DeepSeek、Llama等
  • +统一的操作界面：通过单一Web UI集成推理和训练功能，支持多模态模型和多种文件格式

Cons

  • -需要安装额外的 CLI 包装器，增加了系统复杂度
  • -依赖网络连接进行远程通信，可能受网络状况影响
  • -作为第三方工具，需要额外的配置和维护工作
  • -Beta版本稳定性：作为测试版本，可能存在功能不完善和稳定性问题
  • -本地资源依赖：需要较强的本地计算资源，特别是GPU内存，对硬件配置有一定要求
  • -仅限开源模型：主要针对开源模型优化，不支持GPT、Claude等专有模型API

Use Cases

  • •外出时通过手机监控长时间运行的 AI 编程任务
  • •在多设备间灵活切换，随时随地查看代码生成进度
  • •团队协作场景下的远程代码审查和实时监控
  • •AI研究和实验：研究人员进行模型微调、实验不同架构和超参数优化
  • •本地AI应用开发：开发者在本地环境中训练定制模型，构建多模态AI应用
  • •教育和学习：AI学习者通过实际训练过程理解模型工作原理和优化技术
View Happy DetailsView Unsloth Details

FAQ

Which is more popular, Happy or Unsloth?
Unsloth has more GitHub stars (77,139 vs 23,980).
Which is more actively developed, Happy or Unsloth?
Unsloth had more commits in the last 90 days (3,818 vs 317).
Should I use Happy or Unsloth?
Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.
Happy vs Unsloth (2026): GitHub Stats, Features & Which to Choose