Which is more popular, agents or TextGen?

TextGen has more GitHub stars (47,721 vs 14,447).

Which is more actively developed, agents or TextGen?

agents had more commits in the last 90 days (528 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

agents is growing faster: +1,358 GitHub stars in the last 30 days vs +215 for TextGen.
Pick agents for: a framework for building realtime voice AI agents. Pick TextGen for: the original local LLM interface.

From GitHub data refreshed daily.

agentsopen-source

A framework for building realtime voice AI agents 🤖🎙️📹

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

+Comprehensive multi-modal capabilities with flexible integrations for STT, LLM, TTS, and Realtime APIs in a single framework
+Built-in telephony integration allows agents to make and receive phone calls through LiveKit's telephony stack
+Advanced semantic turn detection using transformer models helps reduce interruptions and improve conversation flow

+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 server infrastructure and technical expertise to deploy and maintain realtime voice agents
-Complex setup with multiple integration points may have a steep learning curve for newcomers
-Real-time voice processing demands significant computational resources and low-latency networking

-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

•Customer service automation with voice-enabled agents that can handle phone calls and web-based interactions
•Virtual assistants for healthcare or education that need to see, hear, and respond in real-time conversations
•Interactive voice response (IVR) systems that integrate with existing telephony infrastructure for business applications

•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, agents or TextGen?: TextGen has more GitHub stars (47,721 vs 14,447).
Which is more actively developed, agents or TextGen?: agents had more commits in the last 90 days (528 vs 1).
Should I use agents or TextGen?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.