PandaProbe
Open-source observability and self-repair for AI agents, turning production failures into validated, reusable rules.
PandaProbe's differentiator is the Repair Harness: validated rules get written, tested against real tasks, and promoted into a shared workspace so agents stop repeating the same failure. Tracing and LLM-as-judge evaluation cover the standard observability ground you'd expect from LLM monitoring tools, but the self-repair loop and the agent-facing CLI are the part competitors like plain LLM tracing dashboards don't offer. For a team already on LangGraph, CrewAI, or Google ADK and comfortable with a CLI, it's worth a trial. If you need no-code integrations or generic APM, look elsewhere.
Verified 15d ago · liveness 62/100 · cite: rightaichoice.com/tools/pandaprobe
- AI agent developers shipping multi-step agents to production
- Teams already on LangGraph, LangChain, CrewAI, or Google ADK
- Engineering-led orgs comfortable with CLI and instrumentation
- Companies that need agent observability to stay inside their VPC or data center
- Teams wanting simple single-turn LLM monitoring without agent-specific features
- Non-technical users who can't handle instrumentation or CLI workflows
- Traditional software teams looking for generic APM rather than agent observability
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Skip PandaProbe if you need simple single-turn LLM monitoring or a no-code dashboard and aren't willing to instrument agents or work from a CLI.
Pro and Startup quotes include fixed monthly trace and eval quotas; everything beyond them is billed pay-as-you-go, so a traffic spike lands on your invoice.
The $0 Hobby tier is genuinely useful for a solo developer trialling the repair loop, and $29/mo Pro fits a two-person team instrumenting one production agent. The $299/mo Startup tier is where seats (10), rate limits, and retention management make it viable for a scaling team. Below this price band sit general-purpose LLM tracing tools that lack self-repair; above it sit enterprise observability platforms that charge for seats and volume without the validated-rule loop.
In short
PandaProbe — Open-source observability and self-repair for AI agents, turning production failures into validated, reusable rules. Best for AI agent developers shipping multi-step agents to production, Teams already on LangGraph, LangChain, CrewAI, or Google ADK, Engineering-led orgs comfortable with CLI and instrumentation. Free to start; paid plans from $29/mo.
What people actually say about PandaProbe — is it worth it?
We scanned public community sources for PandaProbe on Sep 22, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 3 of the posts we fetched could be positively tied to PandaProbe. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is PandaProbe? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Full-trajectory agent tracing: model calls, tool use, sub-agent activity, decisions, timing, outputs
- Outcome evaluation that detects incorrect, incomplete, or risky runs even without an error
- LLM-as-judge scoring with structured feedback
- Session-level evaluation across multiple traces, not just isolated runs
- Automated monitoring with scheduled eval runs
- Alerting on metric regressions across agent versions
- Repair Harness: writes, tests, promotes, and retires scoped rules
- Persistent workspace of learned rules organized by task, workflow, or domain
- Developer-controlled task replay with restricted tools for safe repair validation
- Live trials to validate candidate rules against real tasks
- Structured CLI for agents to inspect traces, run evals, and retrieve scores
- Human annotation support
- Self-hosted deployment under Apache 2.0 (on-prem, VPC, or local)
- Data retention management (Startup tier and up)
- Custom SSO (Enterprise tier)
About PandaProbe
PandaProbe is an open-source (Apache 2.0) observability and self-repair stack for AI agents running in production. It works in three layers. Tracing records everything an agent did across a full trajectory — model calls, tool use, sub-agent activity, decisions, timing, and outputs. Evaluation then judges whether the run actually succeeded, catching incorrect, incomplete, or risky work even when no error was thrown. The Repair Harness writes a scoped candidate rule into the agent's workspace, tests it against real tasks through developer-controlled replay or live trials, promotes rules that improve outcomes, and retires those that don't. PandaProbe reports task success climbing from a 61% baseline to 79% (+18pp) once validated rules accumulate. It keeps your existing orchestration and execution loop and wraps the agent frameworks and providers you already run — LangGraph, LangChain, DeepAgents, CrewAI, Google ADK, Claude Agent SDK, OpenAI Agents SDK, plus OpenAI, Gemini, Anthropic, Mistral AI, and AWS Bedrock. The control plane is exposed through a structured CLI that agents themselves can use (pp traces get, pp eval run, pp scores list), so agents inspect traces and retrieve scores without a human dashboard. It's built for developers shipping production agents who want failures to become learning rather than log noise.
Behind the Verdict
PandaProbe sits at an interesting seam. Traditional LLM observability tools give humans a dashboard of traces and alerts; PandaProbe adds a layer that turns a confirmed failure into a persisted rule the agent itself can re-discover on later runs. That's the whole pitch, and it's backed by a concrete number on the vendor site: task success rising from 61% to 79% as validated rules accumulate. The three-layer model is clean. Tracing captures model calls, tool use, sub-agent activity, decisions, timing and outputs. Evaluation determines whether the run actually succeeded — including incorrect, incomplete or risky outcomes where no exception was raised. The Repair Harness then writes a scoped candidate rule, tests it via developer-controlled replay or live trials with restricted tools, and only promotes what improves evaluated outcomes. Rules live in a persistent workspace organized by task, workflow, or domain, so agents sharing a workspace can reuse fixes across turns, sessions, and multi-agent workflows. The agent-facing angle is what distinguishes it. The control plane is exposed as a structured CLI used by the harness itself, so agents can inspect traces, run evals and retrieve scores without a human dashboard in the loop — a real advantage for autonomous fleets. Strengths: open-source core under Apache 2.0 with self-hosting on bare metal, your own Kubernetes, or your cloud account; a free Hobby tier with no credit card; and support for the frameworks most teams already run (LangGraph, LangChain, DeepAgents, CrewAI, Google ADK, Claude Agent SDK, OpenAI Agents SDK) plus the major model providers. Enterprise adds hybrid and self-hosted hosting, custom SSO, a support SLA and architectural guidance. Weaknesses to weigh honestly: the Hobby plan caps you at 100 base traces, 100 trace eval runs and 10 session eval runs per month, which is enough to try the loop but not to run it in production. Paid tiers are seat-limited (2 on Pro, 10 on Startup) and overages are pay-as-you-go. The workflow assumes you're willing to instrument agents and work from a CLI — there's no no-code path, so non-technical users and teams wanting simple single-turn LLM monitoring will find it overkill. And it is agent-specific by design, not a general APM replacement. Where it fits: engineering teams shipping multi-step agents who want failures to become durable learning instead of recurring manual repair, and organizations that need observability to stay inside their own network. Where it doesn't: teams without engineering capacity for instrumentation, or anyone whose problem is generic service monitoring rather than agent behavior.
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Real-world workflow fit
Concrete scenarios for the personas PandaProbe actually fits — and what changes day-one when you adopt it.
You ship a LangGraph support agent, trace every run on the Hobby tier, and notice a class of runs finishing 'successfully' but producing incomplete answers. You run an eval, confirm the failure, and let the Repair Harness write a candidate rule that is replayed against three past tasks before it becomes trusted.
Outcome: The rule is promoted into the workspace and later runs stop repeating that failure class, without you manually patching the prompt each time.
Your CrewAI fleet runs scheduled eval sessions against production traffic. When automated monitoring flags a metric regression across agent versions, you use the CLI to pull the latest failed trace, inspect the score, and trigger a repair candidate tested under restricted tool boundaries.
Outcome: Regression is caught before it spreads across the fleet, and the fix is shared through the workspace so every agent on that workflow picks it up.
You self-host the full stack inside your own Kubernetes cluster, keep orchestration unchanged, and point existing agents at the CLI control plane. Enterprise SSO and a support SLA are required before rollout; retention is managed centrally.
Outcome: Trace data never leaves your perimeter, agents reuse learned rules across teams, and you hold policy control over which repairs are promoted.
Use Cases
- Trace multi-step agent workflows in development to catch failures before they reach production.
- Run scheduled eval sessions against production traffic to detect uncertainty and behavioral drift.
- Use LLM-as-judge scoring to get structured feedback on agent decision quality.
- Promote validated repair rules into a shared workspace so agents stop repeating known mistakes.
- Replay failed tasks under restricted tool boundaries before trusting a candidate fix.
- Wire PandaProbe CLI commands into CI/CD for automated agent testing.
- Self-host observability so trace data never leaves your own network.
Models Under the Hood
as of 2026-09-28
Limitations
- The free Hobby plan is capped at 100 base trace ingestions, 100 trace eval runs, and 10 session eval runs per month with a single seat.
- Paid Cloud tiers are seat-limited (2 on Pro, 10 on Startup, unlimited on Enterprise) and usage beyond the included quotas is billed pay-as-you-go.
- Data retention management starts at the Startup tier, custom SSO and dedicated support are Enterprise-only, and high rate limits begin at Startup.
- Self-hosting of all core platform features and APIs is available free under the Apache 2.0 license.
as of 2026-09-23
Verification history
We have re-verified PandaProbe 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
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Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published PandaProbe tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Hobby
$0/forever
Ideal for
Solo hobbyist or one developer tracing a single agent to see whether the repair loop is worth adopting.
What this tier adds
Free entry point: 100 base traces, 100 trace eval runs, 10 session eval runs per month, 1 seat, GitHub community support.
Pro
$29/month
Ideal for
One to two developers running a production agent who need real monthly quotas and email support.
What this tier adds
Adds 5k base traces, 5K trace eval runs, and 100 session eval runs per month with pay-as-you-go overages, plus a second seat.
Startup
$299/month
Ideal for
A scaling team operating multiple agents that needs high rate limits, more seats, and retention control.
What this tier adds
Raises quotas to 50k base traces, 50K trace eval runs, and 1K session eval runs, adds 10 seats, data retention management, and a private Slack channel.
Enterprise
Custom
Ideal for
Large organizations that need hybrid or self-hosted deployment, custom SSO, and a contractual support SLA.
What this tier adds
Adds alternative hosting options, custom SSO, unlimited seats, dedicated support, and access to the engineering team.
Open Source
Free
Ideal for
Teams that want to self-host the full core platform and customize it rather than buy the hosted service.
What this tier adds
Free Apache 2.0 deployment: all core platform features and APIs, deployment docs, community support, and customization.
Where the pricing makes sense
The company stage and team size where PandaProbe's pricing actually pencils out — and where peers do it cheaper.
The $0 Hobby tier is genuinely useful for a solo developer trialling the repair loop, and $29/mo Pro fits a two-person team instrumenting one production agent. The $299/mo Startup tier is where seats (10), rate limits, and retention management make it viable for a scaling team. Below this price band sit general-purpose LLM tracing tools that lack self-repair; above it sit enterprise observability platforms that charge for seats and volume without the validated-rule loop.
Setup time & first value
How long it actually takes to get something useful out of PandaProbe — broken out by persona, not the marketing-page minute.
A solo developer on Hobby can trace a first LangGraph or CrewAI agent in well under an hour — install the CLI, point it at the agent, and inspect the first trace. A small team on Pro should budget a few hours to wire scheduled eval runs and confirm the judge scores align with their definition of success. Enterprise self-hosted deployments are infrastructure projects: expect days to weeks
Switching to or from PandaProbe
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a dashboard-only LLM observability tool: keep your existing tracing instrumentation where possible and layer evaluation and the Repair Harness on top.
- →From manual prompt patching: point the harness at the same failures you were debugging by hand and let candidate rules be validated through task replay instead.
- →From a homegrown eval script: move scheduled eval runs into PandaProbe so results, scores, and resulting rules live in one workspace.
- →From no observability at all: start on the free Hobby tier with a single agent before committing to a paid quota.
- ↗To a generic LLM monitoring stack: trace data is portable in structure, but you lose the Repair Harness and the persistent rule workspace.
- ↗To a full APM platform: viable only if your primary need shifts from agent behaviour to service-level metrics.
- ↗To a self-managed OSS deployment: same Apache 2.0 codebase, no export needed, but you take on operations.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “PandaProbe”, and we withheld 6: 6 could not be judged, because “PandaProbe” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about PandaProbe.
Official links
Tools that pair well with PandaProbe
Common stack mates teams adopt alongside PandaProbe, with the specific reason each pairing earns its keep.
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Phoenix
Trace, evaluate, and iterate AI agents with Phoenix — open-source LLM observability you can self-host.
Langfuse
Open-source LLM observability, prompt management, and evaluation for teams running AI agents in production.
Featured Head-to-Head Comparisons
Pandaprobe vs Spider Cloud
Spider Cloud and PandaProbe serve entirely different stages of the AI agent pipeline — Spider Cloud excels at acquiring and structuring web data, while PandaProbe focuses on monitoring and debugging agent behavior. Choose Spider Cloud if you need a fast, low-cost scraping API for training or live data injection. Choose PandaProbe if you're shipping agents to production and need deep tracing, uncertainty detection, and regression alerting.
Pandaprobe vs Temporal Ai
If your top priority is building fault-tolerant, durable AI workflows that survive crashes and require explicit human-in-the-loop, choose Temporal AI. If you need deep, session-level observability into every tool call and LLM decision of your agents—especially for evaluation and regression detection—PandaProbe is purpose-built for that. Both are open-source but serve complementary layers: the execution platform vs. the observability layer.
Pandaprobe vs Presto Voice
Presto Voice and PandaProbe serve entirely different domains: Presto Voice is a specialized voice AI for QSR drive-thrus, while PandaProbe is an open-source observability tool for AI agent developers. Choose Presto Voice if you run a QSR chain and want automated order-taking with upselling; choose PandaProbe if you build AI agents and need deep tracing and evaluation. They are not direct competitors.
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