Happy vs LiteLLM

Side-by-side comparison of two AI agent tools

Short answer

  • LiteLLM is growing faster: +2,999 GitHub stars in the last 30 days vs +1,205 for Happy.
  • Pick Happy for: mobile and Web client for Codex and Claude Code, with realtime voice, encryption and fully featured. Pick LiteLLM for: open-source Python SDK and AI gateway for calling 100+ LLMs through a unified OpenAI-compatible interface.

From GitHub data refreshed daily.

Happyopen-source

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

Open-source Python SDK and AI gateway for calling 100+ LLMs through a unified OpenAI-compatible interface

Metrics

HappyLiteLLM
Stars24.0k60.0k
Star velocity /mo1.2k3.0k
Commits (90d)30813.2k
Releases (6m)1010
Overall score0.83412093622437030.9397920782417792

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
  • +统一API接口设计，一套代码兼容100多个不同的LLM提供商，大幅简化多模型切换和对比测试
  • +内置企业级功能如成本追踪、负载均衡、安全防护栏，为生产环境提供完整的AI治理解决方案
  • +既提供Python SDK又提供独立的代理服务器部署模式，适合不同规模和架构的项目需求

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
  • -作为中间层抽象，可能无法完全利用某些模型提供商的独特功能和高级参数配置
  • -依赖网络连接和第三方API稳定性，增加了系统的复杂度和潜在故障点
  • -对于简单的单模型应用场景可能存在过度设计，增加不必要的依赖和学习成本

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应用开发中需要对比测试多个LLM模型性能，快速切换不同提供商而无需重写代码
  • •企业级AI服务需要统一的成本监控、访问控制和负载均衡管理多个模型调用
  • •构建AI代理或聊天机器人时需要根据用户需求和成本考虑动态选择最适合的模型
View Happy DetailsView LiteLLM Details

FAQ

Which is more popular, Happy or LiteLLM?
LiteLLM has more GitHub stars (59,982 vs 23,969).
Which is more actively developed, Happy or LiteLLM?
LiteLLM had more commits in the last 90 days (13,173 vs 308).
Should I use Happy or LiteLLM?
Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.