https://github.com/user-attachments/assets/89999dd8-a1be-49ad-9419-60c2b38f6374

Why is the "hi" demo slow? That clip was recorded on a CPU-only Android device with no usable GPU or NPU path. Running Gemma 4 E2B on pure CPU takes about 45 seconds to warm up. On stronger phones it is much faster:

• Google Tensor G3/G4 (Pixel 8, Pixel 9)
• Snapdragon 8 Gen 2/3 (Galaxy S24, OnePlus 12)
• Dimensity 9200/9300 (recent MediaTek flagships)
• Snapdragon 7+ Gen 2+ (mid-range with GPU)

On these devices, warmup drops to seconds. Same model, better hardware.

The Story

I'm building this solo. When Gemma 4 landed with native tool calling on LiteRT-LM, I wanted to know whether a phone could become a real on-device agent instead of just another chatbot. PokeClaw is the result.

The interesting part is not just chatting with a local model. The interesting part is getting a local model to read the screen, choose tools, operate apps, keep task state, and finish real phone workflows. That is exactly what this project is built for.

PokeClaw already supports fully on-device automation with Gemma 4 and optional cloud models for stronger task execution. The current focus is broader device support, more generic skills, more local model options, and a cleaner public release path.

If you hit something interesting, open an issue. Real device reports are how this gets better fast.

Product Direction

PokeClaw is not just a chat app with a few phone-control tricks glued on top.

At its core, it is becoming a mobile agent harness:

• a generic tool layer for phone control
• a task/runtime loop that lets a model choose and chain those tools
• playbooks, rules, and guards that can be iterated against real device QA
• a product shell on top so the same harness is usable by normal people, not just developers

That distinction matters. The long-term goal is not to hardcode one-off app flows forever. The goal is to build the strongest practical harness for AI phone agents on Android, then ship product experiences on top of that foundation.

That is also why the project invests so heavily in:

• generic tools before narrow workflows
• repeated real-device QA instead of one-off demos
• Cloud vs Local model comparisons on the same task families
• playbooks and rules only when the model proves it needs extra structure

That direction affects how bugs are prioritized. PokeClaw should fix deterministic harness, runtime, hardware, storage, accessibility, foreground-service, signing, and QA-runner problems before drilling into one flaky model task.

Examples of harness problems worth fixing immediately:

• the model cannot observe the relevant screen text
• a tool reports success when it actually failed
• task state leaks into the next run
• Accessibility, Notification Access, foreground service, or install/update state is wrong
• model download, storage, or LiteRT startup fails before the model can run
• GPU fallback is mislabeled, crashes, or hides the real fallback reason

Examples that should usually be treated as model-performance or exploratory-agent limits unless logs prove otherwise:

• one Cloud model fails one multi-step app flow once
• one Local model cannot reason through a complex UI path
• a workflow sometimes succeeds and sometimes fails because the model chooses a weak plan
• a prompt needs repeated attempts to pick the right cross-app path

Prompts, tools, skills, and playbooks should stay generic. Add structure when it improves a reusable class of tasks; avoid one-off prompt hacks or coordinate scripts just to make a single demo pass.

Deployment Positioning

There are already strong mobile-agent frameworks for developers, benchmarks, and cloud/desktop-controlled devices. DroidRun/Mobilerun, minitap/mobile-use, Mobile-Agent, and AppAgent-style systems are useful references, especially for planning, UI observation, benchmark design, and failure recovery. They are not the same deployment category as PokeClaw:

• DroidRun/Mobilerun local/open-source runs the agent loop in a Python CLI/container on a host machine and talks to a Portal APK through ADB/port forwarding. The Portal APK uses Android Accessibility for tree/gesture/screenshot capture, but it is not where the local LLM agent loop runs. Its phone-initiated path is cloud-backed.
• minitap/mobile-use, Mobile-Agent, AppAgent, and AppAgentX are external SDK/research harnesses that normally require Python plus ADB, UIAutomator, screenshots, Docker, cloud phones, or host-side model/API services.

PokeClaw should not try to become another PC/SDK/ADB-driven mobile automation framework. Its product lane is different:

• runs as an Android app on the user's own phone
• does not require a computer to be connected while tasks run
• uses Accessibility, Notification Access, foreground service, storage, and Android lifecycle handling inside the phone app
• runs the task loop inside the installed APK, with local LiteRT-LM or optional cloud LLMs behind the same harness
• exposes integration points for power users without making Tasker, MacroDroid, ADB, or a cloud phone fleet mandatory

External automation tools are still useful. Tasker, MacroDroid, Locale, and similar apps are good deterministic trigger engines. PokeClaw should let them trigger an AI task, but PokeClaw should remain the phone-resident execution harness.

See the UI

👉 Try the interactive demo on our landing page — click through every screen without installing anything.

What it does

The model picks the right tool, fills in the parameters, and executes. You don't configure anything per-app. It just reads the screen and acts.

Proven Quick Tasks

These are tasks we have already run end-to-end during on-device QA.

Local mode

• Summarize notifications
• Explain clipboard contents
• Analyze storage / apps and suggest cleanup targets
• Check whether the battery needs charging
• Report installed apps
• Report phone temperature
• Report Bluetooth state
• Report battery, storage, and Android version
• Run quick-task cards directly from the UI and return the result in chat
• Route contact-specific send / call tasks correctly and fail cleanly when the contact does not exist on the device

Cloud mode

• Send a WhatsApp message and auto-return to the same PokeClaw conversation
• Search inside YouTube in the real app
• Check what is trending on Twitter / X and summarize it
• Install or open Telegram from Play Store
• Open Reddit and search for pokeclaw
• Copy the latest email subject and Google it
• Draft an email saying you will be late
• Preserve task state and session history across cross-app execution and return

Benchmark & Real-Device QA

Every number below comes from repeated trials on a physical Pixel 8 Pro running release builds. No cherry-picked runs, no emulators. The full verified task list and tier breakdown is in thoughts/verified-task-capabilities.md.

Cloud (GPT-4.1) — 18/20 pass, real tasks on real phone

| Task | Result | Rounds | What h