{"id":46252251,"title":"Show HN: Browser4 – an open-source browser engine for agents and concurrency","points":6,"user":"galaxyeye","time":1765603554,"time_ago":"19 hours ago","type":"link","content":"

Hi HN,

I’d like to share an open-source project we’ve been working on for a while: Browser4.

The motivation came from a recurring frustration: most browser automation tools (Playwright, Selenium, Puppeteer) are excellent for human-written scripts, but start to show friction when used as a core execution layer for AI agents or at very high concurrency.

So instead of building “another wrapper around Playwright”, we experimented with a different direction:\ndesigning a browser engine where AI agents are first-class citizens.

### What Browser4 is

Browser4 is a browser automation engine built on native Chrome DevTools Protocol (CDP), with a focus on:

* Coroutine-safe concurrency (designed to run many browser sessions in parallel)

* Agent-oriented APIs (navigation, interaction, extraction as composable actions)

* Hybrid extraction: ML agent driven extraction + LLM extraction + structured selectors + an SQL-like DOM query language (X-SQL)

* Low-level control without Playwright-style abstraction overhead

It’s written in Kotlin/JVM, mainly because we needed predictable concurrency behavior and long-running stability under load.

The project is fully open-source (Apache 2.0).

### What it’s not

* It’s not a drop-in Playwright replacement.

* It’s not a no-code RPA tool.

* It’s not “LLM magic” — LLMs sit outside the browser engine.

Browser4 intentionally stays close to the browser execution layer and leaves planning/reasoning to external agent loops.

### Current use cases we’re testing

* Large-scale web data extraction

* Agentic workflows (search → navigate → extract → summarize)

* Price / content monitoring with frequent revisits

* High-concurrency crawling where browser startup and context switching are bottlenecks

On a single machine, we can sustain very high daily page visits, though we’re still validating benchmarks across different workloads.

### Open questions (where I’d love feedback)

* For agentic systems, does it make sense to bypass Playwright entirely and work closer to CDP?

* Where do you see the biggest pain points when combining LLMs with browser automation today?

* Is JVM a reasonable choice here, or is Python still the better tradeoff despite concurrency limits

* What abstractions would you want in a browser engine built for AI agents?

### Links

* GitHub: https://github.com/platonai/browser4

* Website (light overview): https://browser4.io

Happy to answer technical questions or hear criticism — especially from people running browser automation or agent systems in production.

Thanks for reading.","url":"https://github.com/platonai/Browser4","domain":"github.com","comments":[{"id":46252924,"level":0,"user":"SkyRocknRoll","time":1765612700,"time_ago":"16 hours ago","content":"

Very much excited about this. \n*Hybrid extraction: ML agent driven extraction

This is what I have been missing in the existing systems","comments":[]},{"id":46254130,"level":0,"user":"tomfox2","time":1765629395,"time_ago":"12 hours ago","content":"

Therefore, we should not be paying for tokens, but for more effective agents.","comments":[]}],"comments_count":2}