Is Glassbrain free to use?

There's a free plan, and paid tiers run up to about $20 per month. Exact limits change, so check the site before you commit.

Do I need to write code to use it?

Yes. You connect your app through an SDK or integration and send your traces in. It won't debug an app it can't see, so if you never wire up the connection there's simply nothing for the platform to analyze or replay for you.

Can it debug AI apps in any language or framework?

It works through its integrations, so support depends on whether your stack is covered. If yours isn't listed yet, you'd be wiring it up yourself.

Does Glassbrain have an API?

Yes, an API is available, which is how runs and traces get into the platform.

How is this different from normal logging?

Logs give you lines. Glassbrain gives you the whole run as a tree you can replay, which is exactly what you need when the failure is behavioral rather than a crash and there's no stack trace to point you at the culprit step.

Developers and small teams running AI features in production, especially those without a full observability setup. If you already treat an AI debugging tool as part of your stack, Glassbrain slots in next to it. If not, it can be the first one you add. Glassbrain fits best with teams already tracing their AI calls and willing to do the setup. If you're debugging AI features by hand right now, it's worth a look, start with the free plan and see how the trace trees read against your own bugs.

Glassbrain

Glassbrain · Coding

Glassbrain is an AI debugging platform built for developers who ship AI features and need to know why a model failed. Think of it as an AI bug fixer that works on behavior, not crashes. It captures each run as a visual trace tree, lets you replay the exact moment things broke, and suggests fixes based on the failing step. Teams use it to cut the time between "the output is wrong" and "here's the line that caused it."

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About Glassbrain

What Is Glassbrain

Glassbrain is a debugging workspace for AI applications. Instead of reading raw logs, you get a tree view of every step a request took: which prompt fired, which model answered, which tool call ran, and where the chain went sideways.

The problem it solves is simple. AI apps fail in ways normal debuggers handle badly. A wrong answer can come from a bad prompt, a stale context window, or a tool that returned junk, and logs alone rarely show which one of those three actually caused the failure you're staring at. Glassbrain keeps the whole run so you can walk back through it.

The catch is setup. This isn't a drop-in tool that works with zero code. You have to send your traces into it, which means wiring up an SDK or an integration first. If your stack doesn't match what it supports yet, expect some plumbing before you see anything useful, and budget an afternoon for that plumbing rather than ten minutes.

Getting Started

Create an account on the Glassbrain site and open a new project for your app.
Install the SDK or connect your existing tracing source with the project key.
Run your app and trigger the AI feature you want to inspect.
Open the trace tree for that run and click into the step that failed.
Replay the run, review the suggested fix, then apply it in your code.

Product Information

A quick look at Glassbrain's pricing, supported platforms, and performance.

Free PlanYes

Paid Plans$0 - $20/mo

PlatformWeb

DeveloperGlassbrain

CategoryCoding

Release DateJan 2025

Latest UpdatedSep 2025

Website VisitsN/A

Website Global RankN/A

API AvailabilityYes

Best for

The users, tasks, and scenarios where this tool fits best.

Users

Backend and ML engineers
Solo developers shipping LLM apps
Support and QA engineers

Tasks

Tracing an AI request end to end
Reproducing an intermittent AI failure
Fixing a prompt or context bug
Onboarding a new teammate to an AI codebase

Scenarios

Debugging a chatbot that gives wrong answers
Chasing a flaky agent that works sometimes
Reviewing an AI feature before launch

Key features

Visual Trace Trees

Glassbrain lays out each AI run as a tree of steps. You see the full path a request took. Prompts, model calls, and tool results sit in order, and branches show where the logic split. For anyone who's ever scrolled through a wall of logs, this is the part that saves time.

Time-Travel Replay

You can step back to any point in a run and replay it from there. Why does that matter? Flaky AI bugs are the hardest kind. The ones that show up once out of twenty tries. Instead of trying to recreate the conditions, you reopen the run and watch what happened.

AI-Powered Fix Suggestions

Once a run fails, Glassbrain points at the likely cause and suggests a fix. The suggestion is a starting point, not a patch you apply blind. Treat it as a second opinion on the failing step. Then decide whether it matches your code. It won't always be right.

Run Capture and Storage

Every run is kept so you can return to it later. That helps when a bug is reported days after it happened, or when you need to compare a healthy run against a broken one. You don't have to have been watching live to debug it. That alone kills a lot of guesswork.

Web-Based Workspace

The whole thing runs in the browser. Nothing to install on your machine. You and a teammate can look at the same trace tree and point at the same step, which beats screen-sharing a terminal.

Pros and cons

Pros

Trace trees make AI failures readable without digging through raw logs.
Replay handles the flaky bugs that are hardest to reproduce by hand, the ones that vanish the moment you try to recreate them yourself.
Fix suggestions give you a concrete starting point on the failing step.
Runs are stored, so you can debug a report that arrived late.

Cons

Requires SDK or integration setup before it does anything, so there's upfront work.
Only useful if your app sends traces in; unsupported stacks see nothing.
Fix suggestions still need a developer to judge them, since they don't know your full codebase.

Frequently asked questions

It's an AI debugging platform. It records each run of your AI feature as a trace tree, lets you replay it, and suggests fixes for the step that failed.

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