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Show HN: Workflow Use – Deterministic, self-healing browser automation (RPA 2.0)

(github.com)
67 points gregpr07 | 1 comments | 16 May 25 16:05 UTC | HN request time: 0.207s | source

Hey HN – Gregor & Magnus here again.

A few months ago, we launched Browser Use (https://news.ycombinator.com/item?id=43173378), which let LLMs perform tasks in the browser using natural language prompts. It was great for one-off tasks like booking flights or finding products—but we soon realized enterprises have somewhat different needs:

They typically have one workflow with dynamic variables (e.g., filling out a form and downloading a PDF) that they want to reliably run a million times without breaking. Pure LLM agents were slow, expensive, and unpredictable for these high-frequency tasks.

So we just started working on Workflow Use:

- You show the browser what to do (by manually recording steps; show don’t tell).

- An LLM converts these recordings into deterministic scripts with variables (scripts include AI steps as well, where it’s 100% agentic)

- Scripts run reliably, 10x faster, and ~90% cheaper than Browser Use.

- If a step breaks, workflow will fallback to Browser Use and agentically run the step. (This self-healing functionality is still very early.)

This project just kicked off, so lots of things will break, it’s definitely not production-ready yet, and plenty of stuff is still missing (like a solid editor and proper self-healing). But we wanted to share early, get feedback, and figure out what workflows you’d want to automate this way.

Try it out and let us know what you think!

1. Richardson143 ◴[17 May 25 01:57 UTC] No.44011471[source]▶
This is a really interesting direction, Gregor & Magnus! You're spot on about enterprises needing more robust and self-healing solutions for their high-frequency automation.

It's true that many are looking into self-healing for existing automation scripts; from what I've seen, tools like Healenium are gaining some traction in this space. However, I agree that a Browser Use-like approach also holds a lot of promise here.

My thinking on how this could be achieved with AI agents like Browser Use is to run the existing automation scripts as usual. If a script breaks due to an "element not found" exception or similar issues, the AI agent could then be triggered to analyze the page, identify the correct new locator for the problematic element, and dynamically update or "heal" the script. I've actually put together a small proof-of-concept demonstrating this idea using Browser Use: https://www.loom.com/share/1af87d78d6814512b17a8f949c28ef13?...

I had explored a similar concept previously with Lavague setup here: https://www.loom.com/share/9b0c7cf0bdd6492f885a2c974ca8a4be?...

Another avenue, particularly relevant for existing test suites, is how many QA teams manage their locators. Often, these are centralized in files like POM.xml (for Java/Maven projects) or external spreadsheets/CSVs. An AI agent could potentially be used to proactively scan the application and update these locator repositories.

For instance,

I've experimented with a workflow where Browser Use updates a CSV file of locators weekly based on changes detected on the website: https://www.loom.com/share/821f80fcb0694be4bd4d979e94900990?...

Excited to see how Workflow Use evolves, especially the self-healing aspects!