PandaProbe
Open-source observability and evaluation for AI agents in production.
PandaProbe fills a genuine gap in agent-specific observability with research-backed uncertainty metrics and open-source flexibility. The free Hobby tier is generous for evaluation, though production usage will quickly require paid plans. Strong pick for teams that need deep agent tracing and eval, especially those already using supported frameworks like LangGraph, CrewAI, or Google ADK.
Verified 7d ago · liveness 52/100 · cite: rightaichoice.com/tools/pandaprobe
- AI agent developers shipping production agents
- Teams needing deep observability into multi-step agent behavior
- Researchers and engineers evaluating agent uncertainty and drift
- Open-source enthusiasts wanting self-hosted agent monitoring
- Teams looking for simple single-turn LLM monitoring without agent-specific needs
- Non-technical users who cannot handle basic instrumentation or CLI usage
- Organizations requiring extensive no-code integrations (currently limited)
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Skip PandaProbe if you need a simple no-code LLM monitoring dashboard and aren't building multi-step agents, or if you can't handle basic instrumentation.
Going over your monthly trace limit incurs pay-as-you-go charges, which can add up quickly at high volume.
PandaProbe's pricing is competitive for agent-focused teams, with a $29/mo Pro tier that's cheaper than many general observability tools but more expensive than some simple logging services. The free tier is generous for evaluation, but serious production use will quickly push you to paid plans. Compared to open-source alternatives like LangSmith (which also has a free tier), PandaProbe offers unique uncertainty metrics. For startups scaling, the $299/mo tier is reasonable, but larger
In short
PandaProbe — Open-source observability and evaluation for AI agents in production. Best for AI agent developers shipping production agents, Teams needing deep observability into multi-step agent behavior, Researchers and engineers evaluating agent uncertainty and drift. Free to start; paid plans from $29/mo.
What people actually say about PandaProbe — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
1 mentions across 1 source (GitHub) · researched Jul 2, 2026.
- +Open-source and self-hostable under Apache 2.0 license.
- +Captures full agent trajectories—every tool call and decision branch.
- +One-line instrumentation for major agent frameworks.
- +Session-level evaluation metrics for long-running agents.
- +SOTA uncertainty detection for agent failure modes.
- −Limited community feedback—only GitHub data available.
- −No public user reviews or independent benchmarks.
- −Potential instability due to early-stage development.
- −Support response times unknown for the free tier.
- −May require significant setup for self-hosted deployment.
- • Self-hosting requires infrastructure and DevOps effort.
- • Enterprise pricing undisclosed; may be expensive for small teams.
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: August 2026
How we score →Key Features
- Full agent tracing: every tool call, LLM hop, decision branch
- One-line instrumentation for LangGraph, LangChain, CrewAI, Google ADK, Claude Agent SDK, OpenAI Agents SDK
- Works with OpenAI, Gemini, Anthropic, Mistral AI, AWS Bedrock
- SOTA uncertainty detection over long trajectories
- LLM-as-judge scoring with structured feedback
- Session-level evaluation (not just isolated traces)
- Automated monitoring with scheduled eval runs
- Alerting on metric regressions across agent versions
- Self-healing layer: autonomously detect, diagnose, and prove fixes
- PandaProbe Skill for coding agents (Claude Code, Cursor, Codex)
- PandaProbe CLI for terminal-based trace/eval management
- Open-source self-hosting (Apache 2.0)
- Human annotation support
- Data retention management (Startup+ tiers)
- Custom SSO (Enterprise tier)
About PandaProbe
PandaProbe is an open-source agent engineering platform that gives developers deep observability, evaluation, and monitoring for AI agents in production. It captures full agent trajectories—every tool call, LLM hop, and decision branch—via one-line instrumentation for major agent frameworks and LLM providers. Designed for teams shipping production agents, it offers research-grounded metrics like uncertainty detection over long trajectories, LLM-as-judge scoring, and session-level evaluation. Automated monitoring with scheduled eval runs and alerts on metric regressions helps catch issues before users do. PandaProbe includes a CLI and a Skill that integrates with coding agents such as Claude Code, Cursor, and Codex. Unlike traditional LLM observability tools, PandaProbe treats agent-specific failure modes—uncertainty, behavioral drift, multi-step decision quality—as first-class concerns. It is built by a team with PhD research in AI agent uncertainty and robustness, and offers both cloud-hosted and self-hosted open-source options under Apache 2.0.
Behind the Verdict
PandaProbe stands out by focusing exclusively on agents, not just LLM calls. The platform's core strength is its research-grounded approach to uncertainty detection over long trajectories, which is a differentiator for teams debugging complex multi-step behaviors. The self-healing layer, Harness, is a bold feature that wraps agent loops to detect, diagnose, and prove fixes automatically, potentially saving significant debugging time. The open-source Apache 2.0 license is a big plus for organizations that want to self-host and customize. The integrations with major agent frameworks and LLM providers are well-executed, and the PandaProbe Skill for coding agents is a thoughtful touch for developer workflows. However, the tool requires technical proficiency; it's not for non-developers. The free tier is quite limited (100 traces/month), which may frustrate serious evaluation work unless you upgrade. Overall, PandaProbe is a strong choice for AI engineering teams that need deep agent observability and are comfortable with instrumentation.
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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.
Integrating PandaProbe with LangGraph to monitor a production customer-support agent.
Outcome: One-line instrumentation captures all tool calls and LLM hops. Scheduled eval runs flag uncertainty drift before users complain. The self-healing layer automatically proposes a fix, which the engineer validates in a few clicks.
Setting up session-level evals for a multi-step research agent built on CrewAI.
Outcome: Uses PandaProbe's uncertainty metrics to identify where the agent becomes unreliable over long trajectories. LLM-as-judge scoring gives structured feedback on decision quality. Publishes findings to guide model improvements.
Integrating PandaProbe CLI into CI/CD pipeline for regression testing.
Outcome: Each commit triggers a suite of eval runs, comparing metrics against previous versions. Any regression triggers an alert, and the harness autonomously diagnoses the root cause. Fixes are proven before being rolled out.
Use Cases
- Trace and debug multi-step agent workflows in development to catch failures early.
- Run scheduled eval sessions against production traffic to detect agent uncertainty drift.
- Use LLM-as-judge scoring to get structured feedback on agent decision quality.
- Integrate PandaProbe CLI into CI/CD pipelines for automated agent testing.
- Leverage the PandaProbe Skill to let coding agents manage traces and evals via natural language.
Models Under the Hood
as of 2026-08-20
Limitations
- The free Hobby plan is limited to 100 base traces and 100 trace eval runs per month.
- Higher tiers have quotas up to 50k base traces, 50k trace eval runs, and 1K session eval runs per month.
- Overages are pay-as-you-go.
- Self-hosted version is fully open source under Apache 2.0.
as of 2026-08-11
Verification history
We have re-verified PandaProbe 6 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-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-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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 developers or hobbyists experimenting with agent observability and evals, okay with 100 traces/month.
What this tier adds
Free entry point with 100 base traces, 100 trace eval runs, 10 session eval runs, and community support.
Pro
$29/month
Ideal for
Independent developers or small teams shipping production agents who need more headroom and email support.
What this tier adds
Adds 5k base traces, 5k trace eval runs, 100 session eval runs, 2 seats, and email support.
Startup
$299/month
Ideal for
Scaling startups with higher volumes and a need for high rate limits, private Slack support, and data retention management.
What this tier adds
Adds 50k base traces, 50k trace eval runs, 1k session eval runs, 10 seats, high rate limits, private Slack, and data retention.
Enterprise
Custom
Ideal for
Large organizations requiring hybrid/self-hosted deployment, custom SSO, SLAs, and dedicated engineering support.
What this tier adds
Adds alternative hosting, custom SSO, dedicated engineering team, support SLA, trainings, unlimited seats, and dedicated support.
Open Source
Free
Ideal for
Organizations that want to self-host all core features without limits or cost, with full customization freedom.
What this tier adds
Apache 2.0 license with all core features, scalability of PandaProbe Cloud, deployment docs, and community support.
Where the pricing makes sense
The company stage and team size where PandaProbe's pricing actually pencils out — and where peers do it cheaper.
PandaProbe's pricing is competitive for agent-focused teams, with a $29/mo Pro tier that's cheaper than many general observability tools but more expensive than some simple logging services. The free tier is generous for evaluation, but serious production use will quickly push you to paid plans. Compared to open-source alternatives like LangSmith (which also has a free tier), PandaProbe offers unique uncertainty metrics. For startups scaling, the $299/mo tier is reasonable, but larger
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.
For a supported agent framework like LangGraph or CrewAI, you can instrument your agent with one line of code and start seeing traces within minutes. The CLI and Skill for coding agents let you manage evals via natural language. Most users get first value in under 30 minutes, though deeper setup for custom instrumentation or self-hosting may take a few hours.
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 LangSmith: Export your trace data and import it into PandaProbe via the API or SDK; PandaProbe offers similar tracing but with more agent-specific metrics.
- ↗To LangSmith: Export traces from PandaProbe via API and import into LangSmith if you need a different feature set.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with PandaProbe
Common stack mates teams adopt alongside PandaProbe, with the specific reason each pairing earns its keep.
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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