Droidrun – LLM Agent for Android

Hacker News

Hi HN, I'm Nikolai, software engineer and co-founder at DroidRun. We built DroidRun, an LLM-based agent that leverages the Android Accessibility Tree for precise control and understanding of UI elements. It works on real phones and emulators, and it's open source. How it started: Our co-founder Niels Schmidt (you’ll see him in the demos) coded a prototype and shared a quick video. It went viral, about 50k views on X in under 2 hours. That moment pushed us to go all-in on DroidRun and soon after, we open-sourced it. How it works: Most agents rely on screenshots alone for context. We do that plus feed the Accessibility Tree into the LLM. That gives structural, hierarchical, and spatial metadata about UI elements. Here’s an example: Screenshot of a real UI: https://imgur.com/a/ePRLpyv And a matching accessibility JSON snippet: { "index": 3, "resourceId": "com.android.settings:id\\/search_action_bar", "className": "LinearLayout", "text": "search_action_bar", "bounds": "42, 149, 1038, 338", "children": [ { "index": 4, "resourceId": "com.android.settings:id\\/search_bar_title", "className": "TextView", "text": "In Einstellungen suchen", "bounds": "189, 205, 768, 282", "children": [] } ] } We also annotate UI regions in screenshots with numbers, then match them in the tree. This structure gives the agent a deep understanding of what’s on screen, even across different device types like tablets. This allows for better generalization across devices and screen sizes. Agents can act with greater confidence and fewer hallucinations. Current Status: - Ranked #1 on AndroidWorld until recently (it became highly competitive) - Supports real devices + Emulators - Strong performance on simple and complex UI tasks - Gemini 2.5 Pro works best so far, but we’re iterating fast What's next: We’re working on a cloud platform where you can run prompts on Android devices without setup. Think of LLM controlling a phone in the cloud, ready to test your automations. Looking for: - Feedback from HN - Collaborators who love Android, LLMs, agents - OSS contributors

Visit product View on Hacker NewsOct 28, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, context · Missing: mac, macos, cursor

94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: supports, started, gemini · Missing: reddit linkedin, podcasting, created

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: plus, platform, soon · Missing: intuitive, reviews, host

59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released

34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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