PandaProbe Cloud

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.