The following configuration options are available for resource optimization:
- dynamic agent pool: By
--agentsparameters (e.g.claude-code --agents 5) Control the number of concurrent agents - session isolation: Each agent runs on a separate
tmuxIn-session (pre-installation required), memory footprints are isolated from each other - Monitoring Tools: Use
tmux attach -t claude_agentsView resource usage in real time, or analyzelogs/The session log for the catalog - Smart Sleep: Inactive agents automatically release the API call credits through the
active_work_registry.jsonEnabling stateful retention
After reasonable configuration, the system can reduce the cost of 30%-50% API calls while maintaining high performance.
This answer comes from the articleClaude-Code-Multi-Agent: a tool for coordinating multiple AI agents to accomplish code developmentThe
May not be reproduced without permission:AI productivity tools " How to optimize the resource consumption problem of AI agents?
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