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PandaProbe
open source agent engineering platform
PandaProbe is an open-source agent engineering platform that gives you deep observability into AI agent applications. Use it to trace, evaluate, monitor and debug your AI agents in development and production.
I’m Sina, founder of PandaProbe. Building AI agents is getting easier, but understanding and trusting them in production is still hard.
Once agents call LLMs, tools, APIs, MCPs, and sub-agents, logs are not enough. You need to know what happened, why it failed, whether quality regressed, and if the agent is reliable across a full session.
PandaProbe is my attempt to solve that: an open-source agent engineering platform for tracing, evaluation, monitoring, and debugging AI agent applications.
What PandaProbe provides
🔎 Agent Tracing — capture LLM calls, tool calls, sub-agents, and custom logic as traces and spans.
🧵 Sessions — group related traces to understand the full agent lifecycle.
📊 Evaluations — score traces and sessions with built-in, research-driven, and agent-focused metrics.
I’ll be here all day answering questions and collecting feedback.
If you’re building agents, what’s the hardest part to debug or evaluate?
Thanks for checking it out 🙏
— Sina
About PandaProbe on Product Hunt
“open source agent engineering platform”
PandaProbe was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #20 on the daily leaderboard. PandaProbe is an open-source agent engineering platform that gives you deep observability into AI agent applications. Use it to trace, evaluate, monitor and debug your AI agents in development and production.
On the analytics side, PandaProbe competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how PandaProbe performed against the three products that launched closest to it on the same day.
Who hunted PandaProbe?
PandaProbe was hunted by Sina Tayebati. 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.
For a complete overview of PandaProbe including community comment highlights and product details, visit the product overview.
Sina Tayebati
👋 Hey Product Hunt!
I’m Sina, founder of PandaProbe. Building AI agents is getting easier, but understanding and trusting them in production is still hard.
Once agents call LLMs, tools, APIs, MCPs, and sub-agents, logs are not enough. You need to know what happened, why it failed, whether quality regressed, and if the agent is reliable across a full session.
PandaProbe is my attempt to solve that: an open-source agent engineering platform for tracing, evaluation, monitoring, and debugging AI agent applications.
What PandaProbe provides
🔎 Agent Tracing — capture LLM calls, tool calls, sub-agents, and custom logic as traces and spans.
🧵 Sessions — group related traces to understand the full agent lifecycle.
📊 Evaluations — score traces and sessions with built-in, research-driven, and agent-focused metrics.
⏱️ Monitoring — schedule recurring evaluations automatically.
🛠️ Open source + cloud — self-host from GitHub or use PandaProbe Cloud.
Who it’s for
🧑💻 AI engineers debugging AI agent behavior.
🏗️ Agent platform teams — monitor quality, regressions, and reliability in production.
🔬 Teams experimenting with agents — understand failures faster and compare iterations.
🚀 Startups building AI products — add observability and evaluation early before agents become impossible to reason about.
Quick links
GitHub: https://github.com/chirpz-ai/pandaprobe
Docs: https://docs.pandaprobe.com
Cloud: https://www.pandaprobe.com
I’ll be here all day answering questions and collecting feedback.
If you’re building agents, what’s the hardest part to debug or evaluate?
Thanks for checking it out 🙏
— Sina