PandaProbe Cloud

Hey @sina_tayebati ,qq if we start on cloud but need to migrate back to self-hosted open source later because of data residency laws, is the data schema 100% compatible?

Just curios how is it different from langfuse/smith?

Bundling tracing, evals, and monitoring into a single managed layer is a sharp call. Teams building agent pipelines typically end up with fragile homegrown span logging that breaks the moment they chain subagents. The hardest part of multi-agent workflows isn't inference: it's reconstructing what happened across tool calls when something fails silently. How does trace collection handle async fan-out across spawned subagents?

Congrats on the cloud launch @sina_tayebati The open-source version saved me during a nasty multi-agent debug a few months back, so this is exciting. Does the eval scoring work for custom agent frameworks or is it tied to specific SDKs?

I like the focus on making agent debugging and monitoring easier. As agent workflows become more complex, having better visibility into what's happening behind the scenes is incredibly valuable.The fully managed approach is a big plus too.

"debugging becomes archaeology" lol yeah. once one action turns into 5 tool calls and a couple sub-agents the logs just tell you the order stuff happened, not why it went wronghow does the eval side deal with non-determinism though? same input gives slightly different traces every run, so how do you score regressions without drowning in false positives

This is so cool, congrats!

For MCP-heavy agents, I’m curious how session grouping works in Cloud. Do MCP tool calls get attached to one session timeline automatically, or is that only reliable if I pass the same session_id through each layer?

👋 Hey Product Hunt!I'm Sina, founder of PandaProbe.A while back we launched the open-source version here — the response was incredible. Today we're back with what many of you asked for: PandaProbe Cloud — full-stack tracing, evals, and monitoring for agents, with zero infrastructure to manage.Here's a pattern every agent builder knows: you ship, it looks fine in testing, then quietly misbehaves in production — nobody knows why. Once agents start chaining LLMs, tools, APIs, MCPs, and sub-agents, debugging becomes archaeology. Logs tell you something happened — not why, not whether quality regressed, not how the session held together. And solving that shouldn't mean building your own agent engineering stack.That's PandaProbe Cloud: ship better agents without the ops overhead.What you get🔎 Tracing — full agent executions captured as sessions, traces, and spans.📊 Evaluation — score traces and sessions using SOTA agent-specific metrics.⏱️ Monitoring — schedule recurring evals to track your agent's health in production.☁️ Fully managed — we handle the infra. You just connect, ship, and improve.Who it's for🧑‍💻 AI engineers debugging agent behavior across LLMs, tools, and workflows.🏗️ Platform teams monitoring quality and reliability without owning more infra.🔬 Builders experimenting with agents who want to iterate faster.🚀 Startups who want production-grade observability from day one.Quickstart:☁️ Cloud signup: https://app.pandaprobe.com/🤖 Run: npx skills add chirpz-ai/pandaprobe-skills --skill '*' --yes💥 Then ask your coding agent to "set up PandaProbe".Free to start — generous usage credits. Up and running in minutes.Quick links📖 Docs: https://docs.pandaprobe.com⭐ Open source: https://github.com/chirpz-ai/pandaprobeI'll be here all day — drop your questions and feedback below.Thanks for checking it out 🙏— Sina