Which is more popular, Happy or TextGen?

TextGen has more GitHub stars (47,721 vs 23,980).

Which is more actively developed, Happy or TextGen?

Happy had more commits in the last 90 days (317 vs 1).

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

Happy is growing faster: +1,201 GitHub stars in the last 30 days vs +215 for TextGen.
Pick Happy for: mobile and Web client for Codex and Claude Code, with realtime voice, encryption and fully featured. Pick TextGen for: the original local LLM interface.

From GitHub data refreshed daily.

Happyopen-source

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

The original local LLM interface. Text, vision, tool-calling, training, and more. 100% offline.

+Complete offline operation with zero telemetry ensures maximum privacy and data security
+Multiple backend support (llama.cpp, Transformers, ExLlamaV3, TensorRT-LLM) with hot-swapping capabilities
+Comprehensive feature set including vision, tool-calling, training, and image generation in one interface

-Requires significant local hardware resources (GPU/CPU) for optimal performance
-Full feature set installation may be complex compared to portable GGUF-only builds
-No cloud-based fallback options when local hardware is insufficient

•Privacy-sensitive organizations needing local AI without data leaving premises
•Researchers and developers fine-tuning custom models with LoRA training
•Content creators requiring offline multimodal AI for text, vision, and image generation

Which is more popular, Happy or TextGen?: TextGen has more GitHub stars (47,721 vs 23,980).
Which is more actively developed, Happy or TextGen?: Happy had more commits in the last 90 days (317 vs 1).
Should I use Happy or TextGen?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.