Testing OpenClaw for Multi-Country Trip Planning with MoLOS Integration

OpenClaw and MoLOS Stack for Automated Travel Planning

A developer tested OpenClaw's capabilities beyond standard ChatGPT responses by using it with MoLOS to plan a multi-city China-Japan trip with minimal manual intervention.

Technical Setup and Process

The test used a self-hosted stack with:

• MoLOS as a structured productivity memory layer for managing tasks and notes
• OpenClaw as an AI agent operator for actions

The process involved:

• Feeding the system with trip data: dates, interests, and budget
• Letting OpenClaw create planning tasks automatically
• Generating day-by-day itineraries
• Suggesting flights and hotels
• Assigning places to visit
• MoLOS logging everything into tasks/projects

What Worked

• Initial itinerary was structured and detected scheduling overlaps
• Automatic time adjustments for conflicts
• Centralized data storage in MoLOS prevented data loss across apps
• Automatic task creation (e.g., "Book Beijing-Shanghai flight" and "Buy JR Rail Pass")
• Approval workflow: user reviewed city options and bookings, then wrote decisions into tasks
• MoLOS automatically communicated with OpenClaw to continue the workflow
• Resulted in an editable plan with 50+ completed tasks and complete trip documentation

Limitations Identified

• Errors in transport times (sometimes off)
• Some attractions were invalid
• Manual validation still required for visas and access requirements
• Not a 100% autonomous system yet

The developer described the experience as less about using isolated tools and more about supervising a system that thinks for them, with OpenClaw and MoLOS currently serving as their day-to-day productivity driver.