From traces to SOTA evals: detect agent uncertainty over long trajectories before your users do.
The self-healing layer for agents.
Wrap any agent loop and it starts detecting its own reliability problems, diagnosing them, and proving each fix before it's trusted.
The foundation for evals.
Capture full agent trajectories — every tool call, LLM hop, and decision branch — so your evals have the signal they need to score accurately.
| 1 | from pandaprobe.integrations.google_adk import GoogleADKAdapter |
| 2 | |
| 3 | # Call once at startup — before creating any agents |
| 4 | adapter = GoogleADKAdapter( |
| 5 | session_id="session-abc", |
| 6 | user_id="user-123", |
| 7 | tags=["production"], |
| 8 | ) |
| 9 | adapter.instrument() |
| 10 | |
| 11 | # All ADK runners are now fully traced |
| 12 | # — tool calls, LLM hops, token usage, TTFT |
SOTA metrics for agent behavior.
Research-grounded evaluation metrics purpose-built for long-running agents. Detect uncertainty, score trajectories, and pinpoint exactly where your agent drifts — across entire lifecycles, not just single calls.
Catch regressions before users do.
Schedule eval runs against production traffic on any cadence. Spot behavioral drift and performance regressions the moment they appear.
PandaProbe works by default with your coding agents. Install our Skill + CLI and let Claude Code, Cursor and Codex do the hard work.
A ready-made skill for your coding agent. Manage traces and evals through natural language — no dashboard or manual API calls needed.
Full API access from the terminal. Let coding agents manage PandaProbe for you, or script your workflows in CI/CD.
Python SDK featuring seamless integrations with leading agent frameworks and LLM providers, plus support for custom instrumentation.
No credit card required. Scale as you grow.
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