This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Most AI agents fail in production. Not because the model is bad. Because the infrastructure is missing. Clyro's Agent Kernel stops loops, bounds costs, and enforces business logic before failures happen. Clyro adds runtime governance to AI agents, preventing failures before they reach production. Wrap any agent with policy enforcement, execution guardrails, loop detection, cost controls, audit logs, and runtime visibility.
Hey Product Hunt 👋 I'm Arpan, co-founder of Clyro.
We kept seeing AI agents go off the rails in ways nobody caught in time. An agent gets stuck waiting on another agent, which is waiting on it right back, and it just runs like that for days because nothing technically threw an error.
Most tools we tried are observability tools. They tell you what happened after the fact. We wanted something that steps in while the agent is still running, before things spiral.
That's Clyro. A Prevention Stack that sits alongside your agent and enforces hard limits in real time.
Cost caps and loop detection catch a runaway agent early instead of letting it burn through your budget for days
Step limits and guardrails stop it from taking actions you never approved
An open-source SDK you can wire into an existing agent in a few minutes
It's free to try. Solo devs and full teams can both test it on their own agents right now, no commitment.
One thing I'd love to know: what scares you most about running agents in production? Runaway cost, wrong actions, or silent loops you can't see? Tell me and that's what we'll go after next.
The thing I can never give my leadership is a straight answer to "is the agent safe to expand?" It's always vibes. One reliability score I can point to, and watch move week to week, turns it into a number instead of a gut-feel argument, if it works well. That alone could change how we plan rollouts.
Every agent I've shipped has the same arc, it was flawless in the demo, fine in testing, then it does something baffling in front of a real user. I dont think the model was the problem. mostly, It's that nothing's watching what the agent sees, remembers, or does. I think it is the gap Clyro seems built to close...
The local-first bit is what I want to understand. There's a SQLite buffer, so do replay and the full Prevention Stack work completely offline, or do the loop/cost checks still need the cloud? Trying to figure out if I can develop against it with nothing leaving my machine until I explicitly opt in.
Samll question: how much latency does `clyro.wrap()` add per tool call? even a rough number helps. I'm wrapping an agent that's already on a tight response budget and want to know what I'm signing up for.
The part nobody warns you about: when an agent misbehaves, you lose hours just reconstructing what it was even thinking. There's no real trail. The Think > Act > Observe replay is the first thing I've seen that turns "I have no idea why it did that" into an actual answer.
Hi everyone! 👋
I'm excited to introduce Clyro, built for developers and teams shipping AI agents into production.
As AI agents become more capable, they also become harder to control. Debugging failures, enforcing policies, tracking costs, and understanding what happened during execution shouldn't require guesswork. That's why the team built Clyro to add runtime governance to AI agents without changing how you build them.
With Clyro, you can monitor executions, inspect traces, enforce policies, detect violations, control costs, and gain complete visibility across your AI agents. It works seamlessly with LangGraph, CrewAI, Claude Agent SDK, MCP tools, and other Python-based agent frameworks.
The team would genuinely love your feedback. What features would you find most valuable when running AI agents in production? Your thoughts and suggestions will help shape Clyro's roadmap.
Thanks for checking out the launch, we're looking forward to the discussion!
About Clyro on Product Hunt
“Control AI agents safely at runtime”
Clyro was submitted on Product Hunt and earned 29 upvotes and 16 comments, placing #21 on the daily leaderboard. Most AI agents fail in production. Not because the model is bad. Because the infrastructure is missing. Clyro's Agent Kernel stops loops, bounds costs, and enforces business logic before failures happen. Clyro adds runtime governance to AI agents, preventing failures before they reach production. Wrap any agent with policy enforcement, execution guardrails, loop detection, cost controls, audit logs, and runtime visibility.
Clyro was featured in Developer Tools (515.4k followers) and Tech (627.4k followers) on Product Hunt. Together, these topics include over 240.5k products, making this a competitive space to launch in.
Who hunted Clyro?
Clyro was hunted by ISTIAK AHMAD. 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.
Want to see how Clyro stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
ISTIAK AHMAD
Hey Product Hunt 👋 I'm Arpan, co-founder of Clyro.
We kept seeing AI agents go off the rails in ways nobody caught in time. An agent gets stuck waiting on another agent, which is waiting on it right back, and it just runs like that for days because nothing technically threw an error.
Most tools we tried are observability tools. They tell you what happened after the fact. We wanted something that steps in while the agent is still running, before things spiral.
That's Clyro. A Prevention Stack that sits alongside your agent and enforces hard limits in real time.
Cost caps and loop detection catch a runaway agent early instead of letting it burn through your budget for days
Step limits and guardrails stop it from taking actions you never approved
An open-source SDK you can wire into an existing agent in a few minutes
It's free to try. Solo devs and full teams can both test it on their own agents right now, no commitment.
One thing I'd love to know: what scares you most about running agents in production? Runaway cost, wrong actions, or silent loops you can't see? Tell me and that's what we'll go after next.
— Arpan