TokenSave for OpenClaw
Find wasted AI jobs and diagnose avoidable spend from OpenClaw exports.
Local-first
Your diagnostic files stay on your device.
Evidence-based
See why a job is flagged as waste.
Actionable
Concrete fixes and copy-ready commands for agents or operators.
Most AI waste does not come from one bad model choice
It comes from jobs that failed, ran too often, used the wrong execution mode, or delivered too little value for the cost.
TokenSave helps OpenClaw users surface these patterns quickly, so they can stop avoidable spend instead of just measuring it after the fact.
What TokenSave detects
- Failure-driven waste — jobs that repeat the same failure without solving the root cause
- Overscheduled recurring jobs — agent-turn jobs triggering too frequently
- Premium models used for simple tasks — expensive models doing routine checks
- LLM agent-turn on routine cron jobs — burning tokens on every trigger regardless of necessity
- High-cost jobs with repeated failure or weak scheduling signals
How it works
Export diagnostics from OpenClaw
Run openclaw export to generate a diagnostic archive with job metadata and run history.
Upload into TokenSave
TokenSave reads the archive locally in your browser and parses job-level cost and waste patterns.
Review evidence and recommended fixes
See highest avoidable spend jobs, failure-driven waste breakdown, and copy-ready remediation steps.
Built for waste diagnosis, not token reporting
TokenSave is designed to identify wasted jobs, not just count tokens.
It helps operators connect spend to specific job patterns: failed runs, overscheduling, and mismatched execution choices.