HyperAgent: open-source Browser Automation with LLMs

Hacker News

Hey HN! Excited to show you HyperAgent, a wrapper around Playwright that lets you control pages with LLMs. With HyperAgent you can run functions like: await page.ai("search for noise-cancelling headphones under $100 and click the best option") or const data = await page.ai( “Give me the director, release year, and rating for 'The Matrix'", { outputSchema: z.object({ director: z.string().describe("The name of the movie director"), releaseYear: z.number().describe("The year the movie was released"), rating: z.string().describe("The IMDb rating of the movie"), }), } ); We built this because automation is still too brittle and manual. HTML keeps changing and selectors break constantly, Writing full automation scripts is overkill for quick one-offs. Also, and possibly most importantly, AI Agents need some way to interact with the web with natural language. Excited to see what you all think! We are rapidly adding new features so would love any ideas for how we can make this better :)

Visit product View on Hacker NewsApr 21, 2025

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

91%91% 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 · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

29%29% 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.

Incorrect prediction on native model

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