PandaProbe
PandaProbe · Coding
PandaProbe is an open-source, self-hostable platform for building and running AI agents that fix their own mistakes. It traces every agent run, evaluates the outcomes, and turns failures into validated fixes the agent can reuse later. Teams that already run agents in production use it as an agent debugging tool to catch the messy, unpredictable failures that regular software monitoring can't, and to keep those failures from repeating across an entire fleet. The platform doubles as AI agent monitoring, watching live runs instead of waiting for a human to read logs.
About PandaProbe
What Is PandaProbe
PandaProbe is an agent engineering platform, not another chatbot. It sits around the orchestration and execution loop you already run and adds three things: tracing, evaluation, and a repair loop. The idea is simple. Agents fail in ways ordinary software doesn't. Most tools turn those failures into information for humans. PandaProbe turns them into validated fixes for the agents themselves. That's the whole pitch.
The company built it after failing to find a tool that treated agent observability as a first-class concern, and decided to make it open source from day one. That matters if your team won't ship sensitive workloads to a closed dashboard. You can run the full stack in your own data center, inside your cloud account, or even on a laptop.
The obvious limit is scope. PandaProbe is aimed at teams that already operate agents and care about production reliability. If you just want a no-code way to build your first agent, this is more machinery than you need. It also assumes some engineering comfort, since the main control plane is a CLI rather than a point-and-click app. That trade-off is deliberate.
Getting Started
- Sign up on the PandaProbe site and pick the free Hobby plan, which needs no credit card.
- Connect your agent by pointing it at the PandaProbe control plane through the structured CLI.
- Run a first task and let the tracing layer capture what happened at each step.
- Set up evaluations so outcomes get scored automatically instead of by hand.
- Let the repair loop test candidate fixes and keep the ones that improve results.
Product Information
A quick look at PandaProbe's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- AI engineering teams running agents in production
- Platform and infrastructure engineers
- Startups watching model costs
Tasks
- Debugging agent failures
- Writing automated evaluations
- Building self-repairing agents
- Reducing manual repair work
Scenarios
- A multi-agent workflow where one agent's mistake quietly spreads to the others.
- Weekly regression checks before a prompt or model change ships to production.
- Sensitive deployments where policy demands the whole stack run offline on local hardware.
- Scaling a large agent fleet without scaling the headcount needed to babysit it.
Key features
End-to-end agent tracing
PandaProbe captures a trace of every agent run, step by step, so you can see what the agent actually did rather than what you hoped it would. For teams debugging a flaky workflow, that trace is the difference between a quick fix and a long afternoon of guesswork. No more guessing.
Automated evaluations
The platform scores agent outcomes on traces and sessions, turning quality into a number you can track over time. You set the runs and let evaluation happen on schedule instead of reviewing transcripts by hand. It's the part that makes "is this agent getting better?" an answerable question.
Validated repair loop
This is the piece that sets PandaProbe apart. When an agent fails, the repair loop takes the trace and evaluation as a starting point, generates candidate rules, and tests them against real tasks. Only fixes that improve measured outcomes become trusted, reusable learning. No blind patching.
Persistent learning memory
Learned rules live in a workspace organized by task, workflow, or domain. Agents connected to the same workspace can pull in relevant fixes across turns, sessions, and multi-agent setups. So a mistake solved on Monday doesn't have to be solved again on Friday.
CLI-first control plane
PandaProbe Cloud is exposed through a structured CLI that the repair loop uses directly. Agents can inspect traces, run evaluations, and retrieve scores without a human clicking through a dashboard. That's unusual, and it's the reason agents can act on their own results.
Self-hosted deployment
You can deploy PandaProbe in your own data center on bare metal or Kubernetes, inside your AWS, GCP, or Azure account, or locally on a workstation. BYOC is available from day one. For regulated workloads, that's often the deciding factor. Data stays put.
Monitoring for agent fleets
Automated monitoring watches runs as they happen and flags trouble early. Reusable learning can then be scoped and shared across workflows, so more of the fleet runs on smaller models with less operational overhead. Cheaper runs, fewer babysitters.
Pros and cons
Pros
- Open source from day one, so you can inspect the code before trusting it with production agents.
- Self-hosting on bare metal, in your VPC, or locally keeps run data inside your perimeter.
- The repair loop validates fixes against real tasks, which beats patching on a hunch.
- A CLI control plane means agents, not just humans, can act on trace and eval results.
- A free Hobby plan with no credit card makes it cheap to test the whole approach.
Cons
- The CLI-first design is a hurdle if your team expects a full visual dashboard out of the box.
- It's built for teams that already run agents, so it's heavy for someone building a first prototype.
- The low free tier caps traces and eval runs, so real fleet testing means moving to a paid plan.
Frequently asked questions
It traces, evaluates, and monitors AI agents, then turns their failures into validated fixes they can reuse on later runs. Think of it as observability plus a repair loop specifically for agents.
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