OpenTelemetry-native observability for agents and LLMs
Your agent cost $40 and took 34 seconds. But why? OpenObserve traces every agent session across models, tools, services, datastores, and user sessions so you can see exactly where time, money, and quality went. Detect loops, run online evals, and follow failures from the LLM call through your backend and database, alongside the logs, traces, and metrics from the rest of your production stack.
Hi Product Hunt, Ashish here, Head of Engineering at OpenObserve.
If your AI agent got stuck in a tool-call loop right now, would you know? Would you spot it before your customer did?
We didn't.
During a live demo, our own AI SRE Agent silently stalled. No errors. No timeouts. Nothing useful in the logs. We had to dump raw spans and debugged through them just to find the loop.
Reading raw spans isn't a debugging strategy—it’s an apology waiting to happen.
So we built AI Observability directly into OpenObserve. We wanted to see inside the black box.
Here is what it actually gives you:
Sessions map out every single turn. You see every LLM request, tool call, token count, cost, and exactly what prompt caching saved you.
Agent Graph plots your agents, tools, and models onto the exact same service map as the rest of your backend infrastructure.
Agent Behavior automatically flags the sessions that loop or fail—long before a user complains.
Online Evals let you score live sessions using any judge model (bring your own provider and key).
Annotation Queues let you turn those ugly, failed sessions into clean datasets so you can regression-test your fixes.
The best part? It's OpenTelemetry-native.
It normalizes OTel GenAI, OpenInference, OpenLLMetry, and Vercel AI SDK and many more out of the box. Nothing you’ve already wired up goes to waste, and you don’t have to ship a second copy of your data to another platform.
Our SRE Agent runs on this daily now, and it's still our harshest critic.
Point it at your own agent traces. I'd love to hear what you find.
About AI Observability by OpenObserve on Product Hunt
“OpenTelemetry-native observability for agents and LLMs”
AI Observability by OpenObserve launched on Product Hunt on September 10th, 2026 and earned 280 upvotes and 105 comments, earning #2 Product of the Day. Your agent cost $40 and took 34 seconds. But why? OpenObserve traces every agent session across models, tools, services, datastores, and user sessions so you can see exactly where time, money, and quality went. Detect loops, run online evals, and follow failures from the LLM call through your backend and database, alongside the logs, traces, and metrics from the rest of your production stack.
On the analytics side, AI Observability by OpenObserve competes within Developer Tools, Artificial Intelligence and Tech — topics that collectively have 1.6M followers on Product Hunt. The dashboard above tracks how AI Observability by OpenObserve performed against the three products that launched closest to it on the same day.
Who hunted AI Observability by OpenObserve?
AI Observability by OpenObserve was hunted by fmerian. 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.
Reviews
AI Observability by OpenObserve has received 6 reviews on Product Hunt with an average rating of 5.00/5. Read all reviews on Product Hunt.
For a complete overview of AI Observability by OpenObserve including community comment highlights and product details, visit the product overview.
fmerian
Hi Product Hunt, Ashish here, Head of Engineering at OpenObserve.
If your AI agent got stuck in a tool-call loop right now, would you know? Would you spot it before your customer did?
We didn't.
During a live demo, our own AI SRE Agent silently stalled. No errors. No timeouts. Nothing useful in the logs.
We had to dump raw spans and debugged through them just to find the loop.
Reading raw spans isn't a debugging strategy—it’s an apology waiting to happen.
So we built AI Observability directly into OpenObserve. We wanted to see inside the black box.
Here is what it actually gives you:
Sessions map out every single turn. You see every LLM request, tool call, token count, cost, and exactly what prompt caching saved you.
Agent Graph plots your agents, tools, and models onto the exact same service map as the rest of your backend infrastructure.
Agent Behavior automatically flags the sessions that loop or fail—long before a user complains.
Online Evals let you score live sessions using any judge model (bring your own provider and key).
Annotation Queues let you turn those ugly, failed sessions into clean datasets so you can regression-test your fixes.
The best part? It's OpenTelemetry-native.
It normalizes OTel GenAI, OpenInference, OpenLLMetry, and Vercel AI SDK and many more out of the box. Nothing you’ve already wired up goes to waste, and you don’t have to ship a second copy of your data to another platform.
Our SRE Agent runs on this daily now, and it's still our harshest critic.
Point it at your own agent traces. I'd love to hear what you find.