On-device, real-time multimodal AI. Have natural voice and vision conversations with an AI that runs entirely on your machine. Powered by Gemma 4 E2B and Kokoro.
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Updated
Jul 11, 2026 - HTML
On-device, real-time multimodal AI. Have natural voice and vision conversations with an AI that runs entirely on your machine. Powered by Gemma 4 E2B and Kokoro.
The most advanced, fully offline client-side AI suite on Android today.
Run local LLMs like Gemma, Qwen, and LLaMA on Android for offline, private, real-time chat and question answering with LiteRT and ONNX Runtime.
Turn your Android phone into an OpenAI-compatible LLM inference server — Fully local, private and Open Source
Run ml models with both tflite & litert runtimes on edge devices
Swift package for running LiteRT-LM models on iOS. Wraps Google's C API in a clean, async/await Swift interface.
Edge Agent Lab is an Android testing platform for evaluating small language model (SLM) agents directly on mobile devices.
Run LLM inference in an Android app with llama.cpp, ExecuTorch, LiteRT, ONNX, and more.
Rust bindings for LiteRT, successor to TensorFlow Lite. is Google's On-device framework for high-performance ML & GenAI deployment on edge platforms, via efficient conversion, runtime, and optimization
LiteRT-LM model inference support for Unity Android and Meta Quest apps.
Unofficial Swift SDK for Google's LiteRT-LM — run Gemma 4 on-device with text, vision, audio, and tool calling. CPU + GPU (Metal). iOS 17+ / macOS 14+.
On-device LLM client for Android. Fork of Google AI Edge Gallery
Experimental local-first Android AI agent built with Kotlin and Jetpack Compose, featuring on-device Gemma models, policy-controlled tool calling, and safe Android automation.
Brain tracker that won't blow smoke up your ass. Gemma 4, Android, fully local.
Agent Skills for on-device ML: FunctionGemma fine-tuning, LiteRT-LM export, and more. Compatible with Claude Code, Codex, and Gemini CLI.
Ash — offline survival assistant for iOS. Gemma 4 E2B/E4B fully on-device (text · image · voice) with RAG-grounded answers over 56 emergency-response packs. Built for the Kaggle Gemma 4 Good Hackathon.
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