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🎉 GGUF Loader v2.2.0 Release Notes

Separate GPU and CPU builds for Windows, a much faster folder search, and launchers that just work

What's New in v2.2.0

GGUF Loader v2.2.0 (August 11, 2026) upgrades Agent Mode to a proper LangGraph state machine with resumable sessions, adds a one-click GPU installer right in the app, ships separate GPU and CPU builds for Windows, and makes Find Paragraph dramatically faster on big folders.

🖥️ GPU & CPU Builds for Windows

Windows now ships two standalone executables, so you only download what your machine needs:

  • GGUFLoader_v2.2.0_CPU.exe (~69 MB) — runs on any PC, no GPU required. Great for laptops and office machines.
  • GGUFLoader_v2.2.0_GPU.exe (~854 MB) — bundles the NVIDIA CUDA runtime for GPU-accelerated inference. Pick this if you have a CUDA-capable NVIDIA card.

The GPU build was previously the only option — the CPU-only build is 12× smaller and installs in seconds.

⚡ Much Faster Find Paragraph Search

Searching a folder with Find Paragraph is now up to ~40× faster on large trees. The search walk now:

  • skips dependency and build directories (.venv, node_modules, .git, …) during the walk instead of scanning every file, and
  • avoids repeated regex work when normalizing paragraph text for deduplication.

On a typical project folder the search went from ~0.8 s to ~0.02 s with identical results.

🚀 Hardened Launchers for Linux & macOS

launch.sh (and launch.bat on Windows) now check everything up front and tell you exactly what to do:

  • Python 3.10+ — clear error if missing or too old
  • Broken venv detection — catches the Ubuntu/Debian python3-venv stub and falls back to virtualenv
  • No compiler needed — Linux installs automatically pull the prebuilt CPU wheel from the llama.cpp project's index, so you no longer need gcc/cmake to run from source
  • Network preflight — checks PyPI is reachable before installing, and saves a full log to /tmp/gguf_pip_install.log if something goes wrong

🤖 LangGraph-Powered Agent Mode

Agent Mode is now built on LangGraph — a proper state machine (START → agent → tools → agent → END) instead of an ad-hoc loop. That brings real engineering benefits:

  • Resumable conversations: every workspace's thread is checkpointed to SQLite, so closing the app and reopening it resumes the exact same conversation — even across restarts
  • Approval before sensitive actions: shell commands and git writes suspend the graph and ask you to approve before anything executes
  • Cleaner runs: step budgets, malformed-JSON repair, corrective retries on failed tool calls, and cooperative cancel — stop a run at any time and it stops cleanly
  • Streaming answers: the final summary streams token-by-token into the chat

🖱️ One-Click GPU Install

No more downloading wheels or running batch scripts by hand. The sidebar now has an ⬇ Install GPU Support button that:

  • detects whether CUDA support is already installed
  • downloads and installs the CUDA-enabled llama.cpp build in the background with a live status line
  • shows ✅ GPU Support Installed when done — just restart the app and pick GPU mode

If you try to load a model with GPU acceleration before installing, the app tells you exactly where to click.

📚 Documentation

  • New AGENTS.md — how AI agents should work on this codebase (build, test, addons, release)
  • Rewritten QUICK_REFERENCE.md and a new addon development guide (ggufloader/addons/README.md) with a working example
  • The full docs/ archive (user guide, FAQ, addon API, build instructions) was restored from git history and linked from the README
  • Build scripts now auto-name artifacts GGUFLoader_v<version>_<GPU|CPU>.exe and GGUFLoader_v<version>_linux_x86_64_CPU from the single version source of truth

Download v2.2.0

💡 Which Windows build? Not sure if you have CUDA? Use the CPU build — it runs on any machine. Choose the GPU build only if you have an NVIDIA graphics card and want maximum speed.

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