cc-haha vs llama.cpp

Side-by-side comparison of two AI agent tools

Short answer

  • llama.cpp is growing faster: +4,848 GitHub stars in the last 30 days vs +540 for cc-haha.
  • Pick cc-haha for: cross-platform desktop workspace for Claude Code with multi-agent sessions, Git worktrees, and diff review. Pick llama.cpp for: lLM inference in C/C++.

From GitHub data refreshed daily.

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cc-hahaopen-source

Cross-platform desktop workspace for Claude Code with multi-agent sessions, Git worktrees, and diff review

llama.cppopen-source

LLM inference in C/C++

Metrics

cc-hahallama.cpp
Stars14.8k130.1k
Star velocity /mo5404.8k
Commits (90d)8781.5k
Releases (6m)1010
Overall score0.8388478266867060.9215106254372528

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 DetailsView llama.cpp Details

        FAQ

        Which is more popular, cc-haha or llama.cpp?
        llama.cpp has more GitHub stars (130,128 vs 14,829).
        Which is more actively developed, cc-haha or llama.cpp?
        llama.cpp had more commits in the last 90 days (1,491 vs 878).
        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.