Optimizing OpenClaw Setup: Practical Patterns and Insights

OpenClaw users have shared valuable insights based on running the tool consistently. These practical patterns focus on optimized scheduling, agent design, memory management, and cost control, aiming to maximize efficiency and reduce unnecessary overhead.

Cron vs Heartbeat

Initially, all tasks were managed through a HEARTBEAT.md, which led to excessive token usage. The refined approach now distinguishes tasks that require precise timing and those that need conversational context:

• Cron: Ideal for scheduled tasks with specific timing requirements, such as daily digests and weekly reviews.
• Heartbeat: Reserved for quick status checks needing real-time conversational context.

Rule of thumb: If a task can run independently, it belongs in a cron job.

Sub-agents Configuration

Creating specialized agent personas for different tasks, each with its own SOUL.md and memory folder, has proven beneficial. The principal agent remains uncluttered while sub-agents manage specialized functions. Crucially, sub-agents are more effective when they are constrained to specific capabilities rather than being general-purpose.

Memory Management

Agents inevitably forget, making systematic memory management crucial:

• Daily Logs: Commit to memory/YYYY-MM-DD.md for daily events.
• Long-term Memory: Use MEMORY.md for curated, lasting knowledge.
• Task-specific Files: Maintain separate files for ongoing projects.

The first task for agents in every session is to read the relevant memory files to remain contextually informed.

Managing Costs

Cost optimization remains a design challenge. The default model is set to Haiku for regular tasks, escalating to more resource-intensive models like Opus or Sonnet only when necessary. Background tasks should utilize the less expensive model to conserve resources. Additionally, aggressive context management by not loading all models simultaneously also helps in cost reduction.

Monitoring Silence

Adopting a 'silent by default' strategy for monitoring tasks by returning HEARTBEAT_OK unless something demands attention reduces noise effectively.