The AI is stuck in an endless loop trying to fix an error (and how to solve it)

General
1

Hi all,

I’m new to Firebase Studio, and I think it is amazing!

Yet sometimes it seems like it enters into a loop of fixing and creating more bugs, unnecessarily. Usually it goes something like this:

  1. The AI completes a task I gave it, but then I receive a runtime error, well documented, it even catches it in the chat and says: “I’ve detected an error. Want me to help fix it?”
  2. When I click the “Fix Error” button, I receive an explanation, and Firebase Studio starts working on the solution, editing several files.
  3. Once it completes, it’s either a new error or the same error. After 3-4 iterations, going through the same process, both of us realize we are in a deadly loop (the AI keeps apologizing for not being able to fix it).
  4. At that point, I take a step back and simply leave Firebase Studio, heading to ChatGPT, Claude, or even Gemini, copying the content of all the files Firebase Studio just touched, and the detailed error, without a special prompt, just paste the raw files and data.
  5. All the other AIs immediately identify the problem and give me a step-by-step solution, with all the files I need to update and the detailed edits that need to be done.
  6. I then copy-paste it into Firebase Studio chat (sometimes without even reading), the AI thanks me and compliments me on my perfect solution, implements it, and the problem is solved!.

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Now, even though I always manage to solve these loops using the above method and move forward, it takes more time, and it breaks the natural flow using Firebase Studio.

And since sometimes I use Gemini (externally) to solve these endless debugging loops, it raises some questions:

  1. Is the model being used in Firebase Studio not the same Gemini model we all use outside?
  2. Is it not the latest? The strongest? Is it throttled down or limited?
  3. Are there any stronger versions or tiers of Firebase Studio?
  4. Can I choose a different model as my AI agent inside Firebase Studio?
  5. Can I pay or enlist for a stronger, faster, better version of Firebase Studio? :slight_smile:

Thank you for reading this.

1 Like
2

It’s a shame that the entire community posts questions waiting for Google’s developers to respond, but unfortunately, no one responds… It’s as if Google completely doesn’t give a damn about what we say. What a shame…

3

All the while it’s eating up CPU cycles with nothing to show for it. I raised this issue with support and asked them to reinstate the lost time but they wouldn’t do it.

I understand it’s beta but they should credit lost time in the billing system.

4

Hello! First off, thank you so much for this detailed and thoughtful feedback.

What you’re experiencing is a real (and very frustrating!) phenomenon. :sad_but_relieved_face: That “deadly loop” is something we’re very aware of, and your workaround is brilliant.

You’ve asked some excellent questions, and I want to answer them directly. Your core question is really, “Why does the external Gemini web app seem ‘smarter’ than the integrated one?”

Why This Loop Happens: The Challenge of Context

The issue isn’t that the model is “throttled” or a “weaker” version. It’s about the context provided to the model for a specific task.

  1. Firebase Studio’s “Fix Error” Button: When you click that button, the AI is given a very specific, narrow context: the error log and often just the one file that threw the error. It’s trying to be a surgical “fixer” for that single error. The problem, as you’ve seen, is that the fix often requires a wider understanding of 2-3 other files, which the agent doesn’t have in its narrow “fix-it” context.

  2. Your External Gemini Web App Method: When you manually go to the Gemini web app, you are acting as the “context provider.” You are giving it all the files Studio just touched, plus the error. The AI now has a much wider, more complete context to analyze, which is why it’s often more successful at finding solutions that span multiple files.

So, it’s not that the model is ‘weaker,’ but that the prompt you’re giving it externally is far more complete.

Your Specific Questions Answered

  • Is the model being used in Firebase Studio not the same Gemini model?

    It is! Firebase Studio is powered by the same family of state-of-the-art Gemini models.

  • Is it not the latest? The strongest? Is it throttled down or limited?

    It’s not throttled in a performance sense. The difference is in the prompting and context that the Studio “agent” uses for shortcut buttons like “Fix Error.”

  • Are there any stronger versions or tiers of Firebase Studio? Can I pay or enlist for a stronger, faster, better version?

    Not at this time. The entire Firebase Studio experience is currently available as-is.

  • Can I choose a different model as my AI agent inside Firebase Studio?

    Yes, you can. If you switch from the “Prototyper” view to the full “Code” editor view, the chat panel has a model selector. This allows you to bring your own API key and use other Gemini models. However, this won’t necessarily solve the “loop” problem, as the core challenge is the context being provided, not the model’s raw power.

:light_bulb: How to Solve This Inside Firebase Studio

Here are two pro-tips that avoid leaving the app.

1. Reset the AI’s Memory with /clear

My apologies, you are correct that the @ syntax is not a feature. The method you described—manually copy-pasting the file contents—is the right way to provide that context in the chat.

However, when you’re in that “deadly loop,” the AI is often fixated on its last few (failed) attempts. Its short-term memory is cluttered. The best way to break the loop is to start fresh.

  • In the chat box, just type /clear and hit Enter.

This wipes the chat history and resets the AI’s context. Now you can re-paste the error and your files as if it’s the first time, giving it a clean slate to find the right solution.

2. Use the Pre-installed Gemini CLI

Since you’re in an environment with a full terminal, you have another powerful option pre-installed: the Gemini CLI. This is often much faster than copy-pasting.

Instead of using the UI, you can ask the CLI to look at the files directly.

Example Command:

Bash

# You can pass multiple files with -f and the error in the prompt
gemini -f src/app/page.tsx -f src/lib/auth.ts \
  "Fix this error in these files: [paste full error log here]"

This gives the model the full context of all relevant files right from your terminal.

Thank you again for this feedback. It’s users like you who help us find these issues and make the product better!

1 Like
5

Thank you, @rody !

I really appreciate your detailed and transparent answers - and the time you took to respond so thoroughly. As a product manager myself, I can’t overstate the importance of this kind of direct engagement in building user trust and confidence in the product. Kudos to you and the team for being so open and responsive!

I’m also glad to hear that you’re aware of this “loop” phenomenon and have already diagnosed it as a context limitation issue rather than a model weakness. That clarity makes perfect sense.

If I may add a suggestion - from both a user and product-thinking perspective - I’d propose introducing a “Loop Termination Protocol” that the AI could trigger automatically once it detects it’s caught in one of these repetitive fix cycles.

As mentioned, the AI often knows it’s in a loop (it apologizes repeatedly for being unable to solve the problem). That means it’s already self-aware enough to recognize the pattern. At that point, instead of continuing to retry the same narrow fix, the protocol could instruct it to:

  1. Analyze its recent trace of file edits — for example:

    T(0): F1  
T(-1): F2, F3  
T(-2): F4, F1  
T(-3): F5, F2, F4  
T(-4): F3

  2. Expand the context window dynamically - including more of the previously touched files, stepping backward through that trace until the AI reaches enough context to break the cycle.

  3. Reclassify the task scope - shifting from a “local fix” mode to a “context recovery” mode, which could automatically combine multiple files or request the user’s approval to do so.

This approach wouldn’t require a new model or even new data - just a smarter meta-layer for context escalation when loops are detected.

I’ll definitely try the /clear and Gemini CLI approaches you suggested next time this happens. Both sound like great workarounds.

Thanks again for your transparency and thoughtful reply - it really shows that the Firebase Studio team cares deeply about user experience and continuous improvement.