Show HN: Visualizing OpenClaw runs to debug flaws and token spikes (github.com)

A new plugin called ClawTrace has been introduced for the OpenClaw agent, aimed at helping developers visualize and debug their LLM (Large Language Model) runs. After an incident where an agent unnecessarily consumed a massive token budget due to poor logging practices, the ClawTrace tool was developed to provide clear insights into each agent run. This tool allows users to inspect execution details such as token usage, Gantt charts of execution timelines, and even full input/output payloads for each run, helping to identify inefficiencies and issues that could lead to cost overruns. For the AI and machine learning community, ClawTrace represents a significant leap in debugging capabilities, offering a structured view of agent operations through a graph model. Users can interact with Tracy, an AI analyst integrated within ClawTrace, to query their agent's performance and receive tailored insights. The ability to self-evolve agents by learning from run trajectories and automatically adjusting to minimize costs further enhances the efficacy of deploying LLMs in production. This tool not only helps in tracking performance but also serves as a framework for ongoing optimization and improvement in AI model utilization.