Products/Developer Tools/ClawTrace

ClawTrace

Make your OpenClaw better, cheaper, and faster

Developer ToolsAutomatic trajectory capture for every LLM callTool use trackingSub-agent execution monitoringCost tracking per operationTracy doctor agent for querying execution history liveReal-time identification of failures and inefficiencies

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Our Take

ClawTrace is solving a genuinely annoying problem: nobody actually knows what's happening inside their AI agent workflows. It captures every LLM call, tool use, and sub-agent execution so you can see exactly where your tokens are being burned and what went wrong when things break, and Tracy, the doctor agent, can query that history live to figure out how to fix it. The "self-evolving" framing sounds ambitious, and while I have questions about how autonomously agents can actually improve from execution data alone, the observability layer is the real value here. It's free, it targets a specific pain point, and for teams running OpenClaw at scale, this is low-key the move they've been missing.

ClawTrace closes the self-evolving loop for OpenClaw agents. It captures every trajectory automatically — every LLM call, tool use, sub-agent, and cost — so Tracy, the doctor agent, can query OpenClaw's execution history live and tell exactly what failed, what was wasted, and how OpenClaw should evolve next.

Problem It Solves

OpenClaw agents lack visibility into their own execution history — teams don't know how many tokens were burned, whether web searches ran inefficiently, or what the biggest bottlenecks were in their AI agent workflows.

Target Customer

Developers and teams building OpenClaw AI agents who need observability, cost tracking, and self-improvement capabilities.

Use Cases

Monitor OpenClaw agent execution in real-time, Identify token waste and inefficiencies, Enable AI agents to self-evolve from execution history, Debug failed agent tasks, Optimize costs, Track LLM call performance and bottlenecks

Free Tier

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Differentiator

First tool to close the self-evolving loop for OpenClaw agents by capturing full execution trajectory and enabling agents to query their own history.

Why Now

With AI agents becoming more complex and costly, there's urgent need for observability into agent execution to identify failures, wasted resources, and optimization opportunities.

Traction

Notable Metrics: 112 points (Day Rank #13)