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Show HN: Reverse-engineering web apps into agent tools
79 points by pancomplex 15 hours ago | hide | past | favorite | 31 comments
Hey HN! We built a browser-based agent that runs inside an authenticated web app, watches how the app calls its own APIs, and automatically turns those into agent tools. You can think of it as an auto-generated MCP server that self-updates as the host app changes.

The result is a skilled AI assistant that actually integrates deeply with any product (not just chat and RAG) with minimal effort.

Check out these short demos below that show the agent in software you're probably familiar with:

- Jira: https://demo.frigade.com/hn?skill=jira

- Spotify: https://demo.frigade.com/hn?skill=spotify

- Hacker News (lol): https://demo.frigade.com/hn?skill=hackernews

- Full Demo: https://demo.frigade.com/hn?skill=full-demo

As you can see in the examples, you can do way more (and faster) than what you normally would be able to via point and click. And we never even touched the source code of these products!

Why do this?

In an ideal world, every application has an MCP server or an easily-digestible API available for AI agents to feed from. In practice, we found that even very modern software tends to have a spider web of confusing APIs and services that AI agents simply cannot use out of the box. Security also becomes a huge issue as applications have different (often homebrewed) standards for how endpoints are secured (JWTs/cookies/mix of both). Finally, having an actual browser agent go in and use the application on behalf of the user (i.e. computer-use), is simply too brittle, slow, and burns a lot of tokens.

We took our existing browser agent that’s already trained to use and learn authenticated applications, and added an extra step that automatically turns the app’s authenticated APIs into "recipes". A recipe is a mix of the following:

- API endpoint + method

- Authentication method (and how to retrieve refresh auth tokens/cookies)

- Response schema

- Input schema (for POST/PUT)

- Human readable description of what the tool does

Putting it all together, these become reusable tools for LLMs, all without writing or maintaining any code. Even if the APIs change our agent figures this out and replaces the recipe for the tool with the updated version.

Adding tools to an AI agent becomes super simple this way:

- Our agent trains on the app and builds the recipes

- The app owner enables discovered tools from our dashboard

- The agent can now take actions on the user’s behalf directly inside the application. For instance, saying something like "invite my teammate to my workspace" would securely call the existing API endpoint for inviting users without proxying or relaying through a third party.

Of course, there's a ton of edge cases you run into when you try to do this - every application is intrinsically different despite how many "standards" exist. Fun fact: graphql was by far the worst API to work with in standardizing the recipes.

Looking forward to your feedback/comments!

 help



I'm so surprised you didn't add your agent on your own website? And the same fundamentals of a browser agent why you can't achieve everything with APIs alone apply here too inside a website. What's your take?

We have been building in the space balancing both with priority to GUI tools currently building on top of the great GUI experiences of a product, rtrvr.ai/rover


I am not following a couple of things:

- you sell to websites an in-app agent

- why not just have them give you API spec, why reverse engineer their APIs?

A bit longer term, would you see yourself competing with WebMCP then? Because the website can just expose those APIs to any browser agent


Even with an API spec it wouldn't "just work". You'd still need to handle authentication and have a place to manage which APIs you want the in-app agent to have access to / when to call a given tool.

And believe it or not, even big companies with big eng teams don't have API specs available for their applications ¯\_(ツ)_/¯


Yeah the auth angle and unobtrusive integration path seems like the real neat thing here. From your customers eyes its just another user account right?

Is that a requirement to integrate it? your app has to essentially have "teams" or at least shared resources?


Yup just another user account. It can work without this as well (we support ingesting other resources such as help center articles, pdfs, etc.) but at that point it's no different from any other (dumb) AI chatbot out there that just spits out a bunch of itemized bullets.

it's more useful when you don't need permissions from anyone.

1) get firefox MCP

2) visit target site

3) point GLM 5.2 to MCP (maybe grok 4.5 will also work here? haven't tested their guardrails yet)

4) instruct model to create userscript that augments/breaks whatever you want, or just break it with devtools for the one session.

between 3 and 4 you may want to coax the model into instrumenting the client to understand it, write a report/skill for future sessions etc

you can also use the above to create a custom client or just a bot using web API. you can also easily break bot checks without puppeteered browsers now: "the above script aims to determine if it's executed on an allowed web browser, map out all its interactions with web browser APIs to fake adequate responses while using the Deno runtime". it's generally better to augment Deno to pass for Chrome and fool all the checks and their future versions then to just break a single check that may change soon.


This works for non-authenticated sites but wouldn't work for anything behind a login wall that requires a JWT or similar - right?

are you asking if it will magically allow you to bypass authentication? only if the service is vulnerable to auth bypass and you don't mind breaking multiple laws.

Very cool! Does it require some kind of scraping of a third party web app, like clicking through with a browser agent? If so, how can I be sure it does not delete my account or subscribe to another plan or make some other destructive actions if I allow it to do that with my authed acct?

We use a browser-based agent to learn all the APIs and turn them into skills. Most users will run this in a staging/test account to create all the recipes/blueprints. Our agent is also instructed to not take any destructive actions - but of course LLMs make mistakes (hence the test account :) ).

You can see more about how it works in detail here: https://frigade.com/how-it-works


Got it- so it is "inside-out", not "outside-in" kind of product. Very impressive either case, congrats!

How does it work with the intercom widget though?


We can either replace an intercom widget entirely or do a hand off with context to a live intercom chat agent.

This is really helpful. I usually manually do this with chrome dev tools, it’s kind of tedious especially the header and cookies handling.

Agreed - and especially when the API changes..

Is this opensource ? like the tool itself ? in it's entirety ??

Not at this point - but we’re considering it! What would you use it for?

Gas and energy utility portal

make product demo videos, plus extend the functionality to save and contain repetitive task inside any app and have that run at some trigger.

Really cool. It would be interesting to see a demo of an app that is clearly more bespoke, like your Tesla account, online banking, movie theatre ticketing, etc.

Thank you! Along those lines, we did Airbnb too: https://demo.frigade.com/

You want me to run a closed source, LLM agent inside my browser with access to authenticated API endpoints ?! Thanks but no thanks.

No definitely not!

Our customers run the training on their own applications using a demo or staging environment. Then they install the in-app agent you see in the demos and turn on the tool calls they like. All the API calls are executed client side and never touches any of our servers.


But maybe we if have more clarity on how it is working under the hood, then maybe we trust it. For me its kind of pattern - too many if's but's and maybe's on all the new tools i want to use.

Here's a more thorough explanation of how it works under the hood: https://frigade.com/features/skills

Which LLM model is it using?

Works with any frontier LLM model

Very cool project. Looking forward to trying it.

Cool project

Now this is something truly agentic.

so cool!

thank you!



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