Which is more popular, cc-haha or llama.cpp?

llama.cpp has more GitHub stars (129,982 vs 14,793).

Which is more actively developed, cc-haha or llama.cpp?

llama.cpp had more commits in the last 90 days (1,449 vs 886).

Should I use cc-haha or llama.cpp?

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.

cc-haha vs llama.cpp

Side-by-side comparison of two AI agent tools

cc-hahaopen-source

Local-first cross-platform desktop workspace for Claude Code / agents: multi-agent, Git worktrees, code diffs, skill marketplace, multi-model, Computer Use, tas

llama.cppopen-source

LLM inference in C/C++

Metrics

	cc-haha	llama.cpp
Stars	14.8k	130.0k
Star velocity /mo	1.2k	4.9k
Commits (90d)	886	1.4k
Releases (6m)	10	10
Overall score	0.8098229207614953	0.916755908707962

Pros

+High-performance C/C++ implementation optimized for local inference with minimal resource overhead
+Extensive model format support including GGUF quantization and native integration with Hugging Face ecosystem
+Multiple deployment options including CLI tools, REST API server, Docker containers, and IDE extensions

Cons

-Requires technical knowledge for compilation and model conversion processes
-Limited to inference only - no training capabilities
-Frequent API changes may require code updates for downstream applications

Use Cases

•Local AI inference for privacy-sensitive applications without cloud dependencies
•Code completion and development assistance through VS Code and Vim extensions
•Building AI-powered applications with REST API integration via llama-server

View cc-haha Details View llama.cpp Details

FAQ

Which is more popular, cc-haha or llama.cpp?: llama.cpp has more GitHub stars (129,982 vs 14,793).
Which is more actively developed, cc-haha or llama.cpp?: llama.cpp had more commits in the last 90 days (1,449 vs 886).
Should I use cc-haha or llama.cpp?: 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.