PandaProbe Cloud
Summary
Agent debugging without full trajectory capture is guesswork — you see the final output, not the five tool calls and two LLM hops that produced it. PandaProbe instruments those paths from the first line of startup code and scores them with evaluation metrics built for sessions that run longer than a single prompt-response cycle.
The core loop is trace, eval, monitor: capture every span across a session, run research-grounded scoring against those traces, then schedule that scoring on a cron so regressions surface before users do. One-line instrumentation covers LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, and others — so you are not writing custom middleware to get signal. The session-level evaluation is the differentiator; most observability tooling scores individual calls, not the drift that accumulates across a 40-step agent trajectory. Self-hosted deployment is available under Apache 2.0, which matters for teams whose data cannot leave their infrastructure. The free tier caps trace ingestion and session eval runs at counts that support experimentation but not sustained production load.
Bottom line: PandaProbe earns its place on a team instrumenting a multi-framework agent stack that needs trajectory-level evals and behavioral drift detection — but teams running high-volume production traffic will hit the session eval run ceiling on paid tiers and need to negotiate enterprise limits or architect around the quotas.
Pricing Plans
Subscription- Free Tier
- 100 base trace ingestion/mo, 100 trace eval runs/mo, 10 session eval runs/mo, 1 seat
Hobby
For hobbyists
- 100 base traces/mo
- Community support
Pro
For developers and small teams
- 5k traces/mo
- Email support
- 2 seats
Startup
For scaling projects
- 50k traces/mo
- 10 seats
- Private Slack
Enterprise
For large organizations
- Unlimited
- Dedicated support
- Custom SSO
View full pricing on pandaprobe.com →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- One-line framework instrumentation across LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, and others, so you get full span and metadata capture without writing custom middleware that breaks on every framework update.
- Session-level trajectory scoring rather than per-call scoring, which means you detect the uncertainty that accumulates across 30 steps instead of only catching the single bad tool call that a simpler tool would flag.
- Cron-scheduled eval runs against production traffic, so behavioral drift surfaces in a Slack alert before a user screenshots the wrong output and files a bug.
- Apache 2.0 self-hosted deployment path, so teams with data residency requirements are not forced onto cloud infrastructure or into a vendor negotiation to keep traces off third-party servers.
- CLI and SKILL.md integration for coding agents, which means Claude Code or Cursor can manage PandaProbe traces and eval runs directly — removing the manual dashboard step from an AI-assisted development loop.
Cons
Sign in to edit- Session eval run quotas are tight at every tier below enterprise: the free tier allows 10 session eval runs per month and paid tiers scale incrementally. Teams running continuous trajectory evals against a production agent that handles real user volume will exhaust the monthly allotment mid-sprint and face a choice between overage costs, batching evals to stay under quota, or renegotiating tier limits — none of which is the friction-free monitoring loop the product promises.
- The tool is Python-only based on the SDK and integration documentation. Teams running agents in TypeScript or Go have no supported instrumentation path and would need to build against the raw API or abandon PandaProbe for an observability layer that ships a native SDK for their runtime.
- Seat limits at lower tiers constrain team-wide access: the free tier is capped at one seat, and small team seats expand slowly across tiers. A five-person team where both engineers and a product manager need to review eval results will hit this ceiling before they hit usage quotas, at which point they are paying for seat access rather than usage — and that framing favors a competitor with per-seat pricing that matches the team's actual headcount needs.
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About
- Platforms
- Python SDK, CLI, self-hosted, cloud
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-06-18T06:14:15.317Z
Best For
Who it's for
- Developers building and iterating on agents
- Teams needing observability for agent frameworks
- Self-hosting users requiring scalable tracing
- Coding agents managing their own workflows
What it does well
- Tracing agent trajectories for debugging
- Evaluating long-running agent sessions
- Monitoring production agent performance
- Detecting behavioral drift and uncertainty
Integrations
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Frequently Asked Questions
- Is PandaProbe Cloud free?
- PandaProbe Cloud is a paid tool. No permanent free tier is offered.
- Is PandaProbe Cloud open source?
- No — PandaProbe Cloud is a closed-source tool. Source code is not publicly available.
- Does PandaProbe Cloud have an API?
- Yes. PandaProbe Cloud exposes a developer API. See the official documentation at https://pandaprobe.com for details.
- Can I self-host PandaProbe Cloud?
- Yes. PandaProbe Cloud supports self-hosting on your own infrastructure.
- What platforms does PandaProbe Cloud support?
- PandaProbe Cloud is available on: Python SDK, CLI, self-hosted, cloud.
Hours Saved & ROI Stories Community
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Curated lists that include this category
Most agent observability tools treat a session as a bag of individual spans. PandaProbe treats it as a trajectory — an ordered sequence of tool calls, LLM hops, and decision branches that only makes sense scored end-to-end. The workflow is: instrument once at startup using a framework-specific adapter, collect full session traces with spans and metadata captured automatically, then run eval jobs that score those traces with metrics designed to detect uncertainty accumulation and behavioral drift across long-running sessions. Results feed a monitoring layer where you set regression thresholds and alert schedules.
The session-level evaluation metrics are the architectural bet PandaProbe makes that most competitors do not. The vendor describes these as research-grounded, purpose-built for long agent lifecycles — the goal being to catch where an agent’s confidence degrades over many steps, not just flag a bad individual call. LLM-as-judge scoring returns structured, actionable feedback rather than a raw score, so the output of an eval run points at a specific span rather than returning a number you have to interpret yourself.
PandaProbe fits teams actively iterating on agents across frameworks — the integration list covers LangGraph, LangChain, CrewAI, Google ADK, Claude Agent SDK, OpenAI Agents SDK, and DeepAgents, with model provider support spanning OpenAI, Anthropic, Gemini, Mistral, and AWS Bedrock. It also ships a CLI and a SKILL.md file designed so coding agents like Claude Code, Cursor, or Codex can manage traces and eval runs through natural language commands — meaning the tool can wire itself into an AI-assisted development loop without manual dashboard interaction. Where it breaks is volume: session eval run quotas enforce a ceiling on how many full-trajectory evaluations you can run per month at each tier, and teams running continuous evals against high-traffic production agents will exhaust those quotas and face either overage costs or architectural compromises.
Self-hosting is available under Apache 2.0 and is listed as a first-class deployment path, not an afterthought. The CLI exposes full API access for CI/CD scripting, and the Python SDK handles custom instrumentation for stacks not covered by the named framework adapters. Human annotation is available starting on the free tier, which is an unusual inclusion — it allows teams to blend automated scoring with manual labeling without moving to a paid seat immediately.