Which is more popular, Happy or Pipecat?

Happy has more GitHub stars (23,993 vs 16,152).

Which is more actively developed, Happy or Pipecat?

Pipecat had more commits in the last 90 days (2,870 vs 322).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

Pick Happy for: mobile and Web client for Codex and Claude Code, with realtime voice, encryption and fully featured. Pick Pipecat for: open Source framework for voice and multimodal conversational AI.

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 framework for voice and multimodal conversational AI

+Voice-first architecture with built-in speech recognition and text-to-speech integration for natural conversational experiences
+Comprehensive ecosystem with client SDKs for multiple platforms and additional tools for structured conversations and UI components
+Modular, composable pipeline system that supports integration with various AI services and transport protocols for flexible development

-Python-only framework which may limit developers working primarily in other languages
-Real-time voice processing complexity may require significant learning curve for developers new to audio/video handling

•Building voice assistants and AI companions for customer support, coaching, or meeting assistance applications
•Creating multimodal interfaces that combine voice, video, and images for interactive storytelling or creative content generation
•Developing business automation agents for customer intake, support workflows, or guided user interactions with structured dialog systems

Which is more popular, Happy or Pipecat?: Happy has more GitHub stars (23,993 vs 16,152).
Which is more actively developed, Happy or Pipecat?: Pipecat had more commits in the last 90 days (2,870 vs 322).
Should I use Happy or Pipecat?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.