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pandaprobe
by chirpz-ai
Trace, evaluate, and monitor agent behavior for improved reliability
Python
Updated Jul 12, 2026
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What It Does
Collects traces, evaluations, and runtime metrics to help debug and improve AI agents. Pipes agent interactions into structured traces and evaluation harnesses, and integrates with LangGraph, CrewAI, and Claude Agent SDK for end-to-end visibility. Exposes metrics and eval results to help pinpoint failure modes and measure agent reliability over time, guided by Evaluation-Driven Development (EDDOps).
Key Benefits
As agent systems become distributed and autonomous, observability and continuous evaluation are essential to establish who to trust and why. PandaProbe makes agent interactions and outcomes auditable, turning raw traces into signal you can use for agent-to-agent evaluation and tracking an agent's track record. That visibility is a prerequisite for meaningful Continuous Monitoring and RepKit-style reputation systems.
When to Use
Teams building Multi-Agent System that need tracing, Evaluation-Driven Development (EDDOps) and metrics to diagnose failures and measure agent reliability.
Use Cases
- Instrument agent interactions to capture structured traces and timelines
- Run continuous evaluation suites to detect regressions in agent behavior
- Aggregate metrics to identify recurring failure modes and weak delegation patterns
Works With
langgraphcrewaiclaude-agent-sdkopenai
Topics
agent-engineeringagent-evaluationagent-observabilityagentic-aiclaude-agent-sdkcrewailanggraphmonitoringopen-sourceopenai-agents-sdk+2 more
Similar Tools
agent-playgroundlanggraph
Keywords
multi-agent trustagent track recordagent-evaluationagent-observability