How to Optimize Your OpenClaw Setup with Specific Instructions and Refinements

OpenClaw is a dynamic AI system where continuous updates and refined instructions are key to optimizing setup. Each session, agents read their workspace files and act based on the provided configurations. Essential to this optimization is giving each agent a distinct identity through a SOUL.md file that defines personality traits such as pragmatism, a builder mindset, and a tactical approach to questioning tasks.

Utilizing cost optimization for AI tasks involves a three-stage model approach—Ideation, Review, and Execution. Haiku, the fast and inexpensive model, is used for ideation to generate options. The more complex Opus model reviews and refines these outputs, focusing on quality and identifying risks. Tasks are executed with the most suitable and often cost-effective models; for instance, Ollama handles code writing locally, while Sonnet addresses writing tasks. This results in efficient resource allocation, with 80% of tasks handled by cheaper models.

Specificity in instructions for OpenClaw is crucial. Instead of broad directives, detailed guidance like outlining news sources, categorization methods, significance thresholds, and output formats ensures the agent operates without guessing.

The optimization process is iterative—agents' performance is monitored, feedback is given, and instructions are updated to refine future interactions. For instance, refining the morning news digest involved reducing the number of articles from 15+ to a more manageable 5-7, improving clarity and usefulness.

The overall system is supported by a structured workspace, with files like AGENTS.md and MEMORY.md, guiding agent operations and providing historical context, respectively. A daily log in memory/YYYY-MM-DD.md tracks interactions and supports incremental improvement.