Eldon
Local Gemma 4 agent runtime. 39 tools. 7GB VRAM. No cloud.
What it does
Local agent runtime for Gemma 4 on llama.cpp. Unsandboxed by design — code_run is real Python; shell runs against an allowlist. You are the watchdog. Ships a working jinja template for Gemma 4 (llama.cpp has no built-in one yet) and a GBNF grammar that enforces tool calls at the logit level. 39 tools — semantic memory, browser automation, cron scheduler, and hot-reload skills (the model writes its own). Runs on 7GB VRAM. MIT, plain Python, no framework dependency.
Does the same job
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Google Gemma 4 12BJun 2026 · blog.google · ▲310Run multimodal AI locally with an encoder-free architecture
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Hi HN, I built a specialized inference engine for running 4-bit Gemma 4 26B-A4B-IT on any M-series Mac using about 2 GB of RAM. It is called TurboFieldfare and is written in Swift and Metal. I have always adored on-device AI. It feels like magic that you can run a powerful NN on your Mac or iPhone. So I wanted to push the limits a bit and run a model whose weights don’t fit in memory. The model’s 4-bit quantized weights occupy roughly 14 GB, which makes running it with conventional inference tools almost impossible on an 8 GB or even 16 GB Mac once the OS, applications, and KV cache are…
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Gemma Gem is a Chrome extension that loads Google's Gemma 4 (2B) through WebGPU in an offscreen document and gives it tools to interact with any webpage: read content, take screenshots, click elements, type text, scroll, and run JavaScript. You get a small chat overlay on every page. Ask it about the page and it (usually) figures out which tools to call. It has a thinking mode that shows chain-of-thought reasoning as it works. It's a 2B model in a browser. It works for simple page questions and running JavaScript, but multi-step tool chains are unreliable and it sometimes ignores its tools…
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Dev tools · May 2026 · github.com