Launching today
Clyro
Control AI agents safely at runtime
43 followers
Control AI agents safely at runtime
43 followers
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.
42Signals
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
@arpan_jha Couldn't have said it better! 🚀 Building Clyro has been all about giving teams the confidence to run AI agents in production without constantly wondering what they're doing behind the scenes. Excited to hear everyone's thoughts and feedback today! 🙌
Lancepilot
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!
@istiakahmad Thank you so much for hunting us and for the wonderful introduction! 🚀 We're incredibly excited to finally share Clyro with the Product Hunt community. Looking forward to hearing how everyone is approaching AI agent governance, learning from your experiences, and gathering feedback to make Clyro even better. 🙌
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.
@sanjai_arvinth_a_m Local-only is the default: no API key means nothing leaves your machine. Traces go to a SQLite buffer at `~/.clyro/traces.db`, and the execution controls (loop detection, cost bounds, step limits) all run in-process, so they work fully offline. The only things that need the cloud are dashboard replay and the full ARI rollup, plus custom policy eval, which fails open if it can't reach the backend. Develop with everything local, then flip on an API key when you want the dashboard.
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.
@prashantgupt A few milliseconds per call. The execution controls (loop/cost/step) run in-process, and trace export is async and batched in the background (default every 5s), so it stays off your request path. The only inline network hop is custom policy enforcement, which is opt-in, off by default, and it fails open. On a tight response budget you'll barely feel it; wrap one agent and confirm on your own p95.
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...
@guhan_pranav The pattern is the same. Demos are never as unpredictable as production is. It is considerably simpler to comprehend and manage those situations when one can see what an agent saw, recalled, decided, and carried out.
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.
@kasharkk Indeed. Debugging is extremely costly due to the lack of explainability; the execution is only half the issue. Guesswork should not be used when reconstructing an agent's thinking after the event.
@arya012 Exactly. The failure is rarely just the bad action, it’s the forensic cost afterward.
If you can’t reconstruct the chain of reasoning, tool calls, and state transitions, every incident turns into manual archaeology. That’s what makes agent debugging so painful in production; not just that something went wrong, but that you have no reliable way to explain why it went wrong.
A proper Think > Act > Observe replay feels less like a nice-to-have and more like the minimum layer needed for trust.
@arya012 @kasharkk Bridging the AI governance gap between AI innovation and AI accountability helps the decision makers to launch highly resilient products with AI-powered experiences while the developers could ship their products fast without being worried about traceability and drift concerns.
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.
@shivprakash_santelkar That's exactly the kind of confidence we're hoping to give teams. Decisions around expanding agent deployments should be backed by evidence. Thanks for sharing this perspective!