Agent Observability

AI agents, while powerful, can incur significant costs, get stuck in loops, or perform unpredictably. This tool addresses the critical need for unified observability in Model Context Protocol (MCP) environments. It offers detailed tracing of every LLM call, tool invocation, and decision, coupled with automatic cost calculation using built-in pricing tables. Furthermore, it monitors performance, detects anomalies like cost spikes or agent loops, and provides robust audit trails, empowering developers and teams to optimize agent efficiency, manage expenses, and ensure compliance.

Key Features

Use Cases