token-optimizer

You can reduce OpenClaw token usage and API costs by optimizing context loading, applying smart model routing, and managing heartbeats to ensure the cheapest model handles each specific task.

Smart model routing automates task classification to select the cheapest appropriate model tier for each operation, reserving expensive models for complex reasoning and using cheaper models for simple triage.

Lazy context loading significantly cuts down text loaded into agent memory. You can set it up by generating an optimized AGENTS.md file to control how context is loaded into your agent.

Yes, you can manage OpenClaw context using configuration patches that enable native features like session pruning and bootstrap limits to further reduce token consumption.

Yes, token optimization works for customer support ticket processing by loading only relevant ticket information and using cheap models for initial triage, reserving expensive models for complex problem-solving.