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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
turn old phones into ai agents - give it a goal in plain english. it reads the screen, thinks about what to do, taps and types via adb, and repeats until the job is done.
| Date | Stars |
|---|---|
| 2026-07-31 | 1551 |
| 2026-08-02 | 1555 |
| 2026-08-06 | 1555 |
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# droidclaw
> an ai agent that controls your android phone. give it a goal in plain english — it figures out what to tap, type, and swipe.
**[Download Android APK (v0.5.3)](https://github.com/unitedbyai/droidclaw/releases/download/v0.5.3/app-debug.apk)** | **[Dashboard](https://app.droidclaw.ai)** | **[Discord](https://discord.gg/SaCs3cPQdY)**
i wanted to turn my old android devices into ai agents. after a few hours reverse engineering accessibility trees and playing with tailscale.. it worked.
think of it this way — a few years back, we could automate android with predefined flows. now imagine that automation layer has an llm brain. it can read any screen, understand what's happening, decide what to do, and execute. you don't need api's. you don't need to build integrations. just install your favourite apps and tell the agent what you want done.
one of the coolest things it can do right now is delegate incoming requests to chatgpt, gemini, or google search on the device... and bring the result back. no api keys for those services needed — it just uses the apps like a human would.
```
$ bun run src/kernel.ts
enter your goal: open youtube and search for "lofi hip hop"
--- step 1/30 ---
think: i'm on the home screen. launching youtube.
action: launch (842ms)
--- step 2/30 ---
think: youtube is open. tapping search icon.
action: tap (623ms)
--- step 3/30 ---
think: search field focused.
action: type "lofi hip hop" (501ms)
--- step 4/30 ---
action: enter (389ms)
--- step 5/30 ---
think: search results showing. done.
action: done (412ms)
```
---
## how it works
the core idea is dead simple — a **perception → reasoning → action** loop that repeats until the goal is done (or it runs out of steps).
```
┌─────────────────────────────────────────┐
│ your goal │
│ "send good morning to mom on whatsapp"│
└────────────────┬────────────────────────┘
│
▼
┌─────────────────────────────────────────────────┐
│ │
│ ┌──────────────┐ │
│ │ 1. perceive │ │
│ └──────┬───────┘ │
│ │ │
│ dump accessibility tree via adb │
│ parse xml → interactive ui elements │
│ diff with previous screen (detect changes) │
│ optionally capture screenshot │
│ │ │
│ ▼ │
│ ┌──────────────┐ │
│ │ 2. reason │ │
│ └──────┬───────┘ │
│ │ │
│ send screen state + goal + history to llm │
│ llm returns { think, plan, action } │
│ "i see the search icon at (890, 156). │
│ i should tap it." │
│ │ │
│ ▼ │
│ ┌──────────────┐ │
│ │ 3. act │ │
│ └──────┬───────┘ │
│ │ │
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:bc509b949f414b58, desc:ai agents