A high-performance, local-first intelligent agent for Android. All inference runs entirely on-device — no cloud APIs, no telemetry, no data leakage. Built on Google's Gemma-4 model family via LiteRT.

The app operates on a Brain–Memory–Action triad using a three-tier Resident Agent Architecture:

• The Brain: Gemma-4 E-4B/E-2B runs resident on GPU via LiteRT — always loaded, always ready. A lightweight QuickIntentRouter (pure Kotlin regex, zero memory) handles instant device actions (<5ms). Complex queries go straight to Gemma-4 for full reasoning with native tool calling.
• The Memory: A local RAG (Retrieval-Augmented Generation) system using sqlite-vec and EmbeddingGemma-300M. The assistant remembers personal facts, preferences, and conversation history across sessions with zero data leaving the device. Episodic distillation consolidates each conversation into long-term memories.
• The Action: A modular skill framework. Tier 2 native Kotlin actions execute instantly (torch, timer, DND, bluetooth). Tier 3 complex skills (weather, calendar, email) are handled by the resident Gemma-4 model via LiteRT-LM's native @Tool annotations with SDK constrained decoding. Community-extensible WebAssembly skills run sandboxed via Chicory for safe extensibility.

• 🧠 On-device reasoning — Gemma-4 E-4B running on GPU via LiteRT, no internet required
• 💾 Persistent memory — RAG-powered recall across conversations using sqlite-vec semantic search
• 🔒 100% private — No cloud APIs, no telemetry, all data stays on device
• 💬 Full markdown rendering — headings, bold, italic, inline code, code blocks, tables, links, lists
• 🎯 Smart chat titles — auto-generated from conversation content
• 🗂️ Multi-conversation — create, delete, rename, and search conversations by title
• 🧠 Core memories — add and manage permanent facts the assistant always recalls
• 📂 Memory Management screen — view, add and delete core memories and episodic memory browser
• ⚙️ Model selection — switch between E-2B and E-4B in Settings
• 🎬 Fun loading screens — 13 themed animated narratives
• 🖼️ Context window management — structured prompt assembly with KV cache management and recursive summarisation
• 📊 Runtime info — shows active model, backend (GPU/NPU/CPU), and device tier in chat
• ⚡ Quick Actions tab — instant device commands (torch, timer, DND, bluetooth) via zero-overhead Kotlin pattern matcher
• 💭 Episodic memory distillation — Gemma-4 summarises each conversation into long-term memories
• 🔧 Native skills — alarms, timers, SMS, email, torch, calendar events, weather (GPS + city), navigation, media playback, Wikipedia
• 🔍 search_memory — semantic search across core + episodic memories on demand
• 🔎 Tool call debugging — expand any tool call chip to see request/result, tap to copy
• 🧭 Nav drawer — Lists and Alarms accessible from Chat, Actions, and all main screens via hamburger menu
• 📋 Lists — create and manage named lists via chat ("add milk to shopping list") or the Lists UI; full CRUD with active/completed sections
• 🗓️ Scheduled Alarms — date-specific alarms scheduled via Jandal appear in the Alarms screen for review and cancellation
• 📟 Side panel — slide-out drawer accessible from Chat and Settings shows active alarms and timers with live countdown; cancel any from the panel
• 🎵 Media controls — pause, stop, skip, and previous track via Jandal ("skip song", "pause music")
• 🎙️ Podcast playback — open a podcast app and start/resume playback ("play my podcasts", "resume podcast")
• ⏱️ Timer management — list active timers and cancel individual ones ("cancel my 10 minute timer")
• 🗑️ Alarm multiselect delete — select multiple alarms in the Alarms screen and delete them at once
• 📝 Bulk list add — add multiple items to a list in one request ("save all ingredients to shopping list")

• 🗣️ Voice + text input — tap-to-talk with auto-stop (Phase 3)
• 💬 Multi-turn dialog — slot filling, confirmation, and context-switching across intents (Phase 3G, #522)
• 🌦️ Smarter intent routing — colloquial phrases ("is it gonna rain?") routed natively (Phase 3C, #608)
• 📅 Native date arithmetic — date_diff tool for reliable date calculations (Phase 3C, #619)
• 🗒️ Lists — stretch goals — cards, swipe-to-complete, rich content, attachments, sorting (Phase 3, #472)
• ⏰ Alarms CRUD UI — create, edit, and toggle alarms directly from the Alarms screen (Phase 3, #479)
• 📱 Homescreen widget — quick actions and voice from the launcher (Phase 3F, #617)
• 🌙 Dreaming Engine — overnight WorkManager consolidation (Light Sleep → REM → Deep Sleep) (Phase 4)
• ⚡ Semantic cache — instant responses for repeated knowledge queries (Phase 4)
• 🪪 Self-healing identity — structured user profile, LLM-managed via Dreaming cycle (Phase 4)
• 🧩 Wasm skill store — community-extensible plugins with sandboxed execution (Phase 5)
• 🏠 Home Assistant / Google Home — smart home control (Phase 5)
• 📱 8GB device optimisation — dynamic weight loading/unloading, E2B fallback (Phase 6)
• 🎙️ "Hey Jandal" wake word — always-on local detection → instant action routing (Phase 3F)

Backend note: On Qualcomm devices the app uses the Hexagon NPU delegate for fastest inference. On Google Pixel 10 (Tensor G5) the app uses Backend.GPU (ARM Immortalis-G925 via LiteRT). Performance and output are equivalent — the hardware tier detection automatically selects the right delegate. See models/README.md for per-device model setup.

• Android Studio Ladybug (2024.2.1) or newer
• Android NDK (for sqlite-vec)
• JDK 17
• Physical Android device with 8–12GB RAM (emulator cannot test GPU/NPU)

git clone https://github.com/NickMonrad/kernel-ai-assistant.git
cd kernel-ai-assistant
./scripts/download-models.sh       # Download AI models from HuggingFace (~3GB)

Open in Android Studio, connect your device via USB, and run.

This repo can use the official android CLI as a low-noise accelerator for agentic Android work:

./scripts/setup-android-cli.sh
android describe --project_dir=.
android docs search 'edge-to-edge Compose guidance'

Useful commands in this repo:

• android describe --project_dir=. — discover project metadata and build outputs
• android docs search ... / android docs fetch ... — fetch official Android guidance
• android layout --pretty — inspect the active app UI from a connected device/emulator
• android run --apks=app/build/outputs/apk/debug/app-debug.apk — deploy a known APK

./scripts/setup-android-cli.sh installs the official Google CLI into ~/.local/bin on supported platforms, runs android init, and wires the android-cli skill into detected agents such as OpenCode and Copilot.

If the CLI is unavailable on your platform, keep using the normal Gradle + adb workflow.

• Inference: Google AI Edge (LiteRT) with INT4 quantization, GPU + NPU delegates
• Extensibility: GitHub-indexed Skill Store (Rust → Wasm) + Native Kotlin skills
• Context: Recursive context window management with semantic summarization
• Privacy: 100% offline-capable; no telemetry or external LLM API dependencies

Apache License 2.0 — see LICENSE for details.

This project adapts code from Google AI Edge Gallery and uses the LiteRT-LM library. See NOTICE for attribution.