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Agent Behavior Monitoring is an open-source framework designed to monitor, evaluate, and improve the behavior of AI agents operating in real or simulated environments. The system focuses on agent behavior monitoring by collecting interaction data and analyzing how agents perform across different scenarios and tasks. Developers can use the framework to observe agent actions in both online production environments and offline evaluation settings, making it useful for debugging and performance analysis. Judgeval transforms agent interaction trajectories into structured evaluation datasets that can be used for reinforcement learning, supervised fine-tuning, or other forms of post-training improvement. The framework includes tools that analyze agent behavior patterns and group interaction trajectories by behavior type or topic, allowing researchers to detect weaknesses or unexpected behaviors.
Features
- Agent behavior monitoring across online and offline environments
- Trajectory analysis that groups agent actions by behavior patterns
- Evaluation datasets derived from real agent interaction logs
- Integration with reinforcement learning and post-training pipelines
- Custom scoring and evaluation modules for agent performance testing
- Error analysis tools for diagnosing agent reasoning failures
Project Samples
