Glassbrain captures every step of your AI app as an interactive visual trace tree. Click any node, swap the input, replay instantly without redeploying. Snapshot mode stores deterministic replays. Live mode hits your actual stack. Auto-generated fix suggestions reference exact trace data with one-click copy. Diff view shows exactly what changed. Shareable replay links let your team debug together. Works with OpenAI and Anthropic. Two lines of code to integrate. Free tier: 1K traces/month.
I built Glassbrain because debugging AI apps with text logs is brutal. You're staring at walls of JSON trying to figure out which step broke and why.
Glassbrain gives you a visual trace tree of your entire AI pipeline. Click any node, change the input, and replay it right there without redeploying. If something's wrong, it auto-suggests fixes referencing the exact trace data, and you can copy the fix with location comments baked in.
No direct competitor does visual replay. LangSmith, Langfuse, Helicone all stop at text-based tracing.
Free tier is 1K traces/month. Would love your feedback.
The prompt drift thread is the interesting part. Knowing when outputs shift is often more useful than logging what each call returned. Are you planning regression-style monitoring where replay runs automatically on a schedule and flags drift before users notice?
Huge congrats on the launch! The ability to replay a failed run from a specific node without having to redeploy the whole codebase is a massive time-saver. Does the 1K free tier count every individual node click as a trace, or just the full pipeline run?
the deterministic replay mode is the part I'd actually use most. debugging AI agents by re-running against fixed inputs without live calls is a pain point that keeps coming up.
Two lines of code to integrate is the right move. Half the reason I avoid adding observability to my projects is the setup overhead. The visual trace tree vs. walls of JSON logs is a real upgrade. Quick question - does it handle multi-step chains where one node calls another model mid-pipeline, or is it mainly single-call tracing?
The replay without redeploying part is what got me. Does it work with any LLM framework or do you need to set up a specific SDK? Asking because I'm on a custom Claude API setup and always dread the debug process.
About Glassbrain on Product Hunt
“Visual trace replay for AI apps to fix bugs in one click”
Glassbrain launched on Product Hunt on April 6th, 2026 and earned 101 upvotes and 22 comments, placing #13 on the daily leaderboard. Glassbrain captures every step of your AI app as an interactive visual trace tree. Click any node, swap the input, replay instantly without redeploying. Snapshot mode stores deterministic replays. Live mode hits your actual stack. Auto-generated fix suggestions reference exact trace data with one-click copy. Diff view shows exactly what changed. Shareable replay links let your team debug together. Works with OpenAI and Anthropic. Two lines of code to integrate. Free tier: 1K traces/month.
Glassbrain was featured in SaaS (41.5k followers), Developer Tools (511k followers) and Artificial Intelligence (466.2k followers) on Product Hunt. Together, these topics include over 192.5k products, making this a competitive space to launch in.
Who hunted Glassbrain?
Glassbrain was hunted by Sai Ram Muthineni. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Sai Ram Muthineni