Optimizing Multi-AI Workflows with OpenClaw and MemOS

OpenClaw is an AI tool designed for automation, but as discovered through user experience, managing multi-AI setups requires a structured approach. While OpenClaw can handle tasks, its memory capabilities pose challenges, particularly in complex workflows where context retention across tasks and tools are crucial.

A user initially attempted to leverage OpenClaw with the smaller model gpt-oss-20b. However, it became evident that OpenClaw struggles with context when handling longer inputs, indicating that model size plays a critical role. Eventually, switching to Grok 4.1 provided more stability, particularly with generating sensible summaries, though it wasn't a complete solution.

The real challenge arose in integrating multiple AI systems, including OpenClaw for task execution, Grok for summaries, and Notion AI for note-taking. Each tool tended to operate in isolation, remembering only its own activities, which caused a fragmented workflow experience.

The implementation of the MemOS plugin significantly improved the workflow by serving as an external memory layer. MemOS operates by integrating memory across the different AI tools, allowing for shared context and retrieval of historical information across tools. This integration meant that Grok could access OpenClaw's past activities, and Notion AI could refer back to prior notes, preventing the need to restart processes from scratch.

The key takeaway from this experience is that utilizing a larger model combined with a comprehensive memory management system like MemOS is crucial for effectively managing complex, multi-AI workflows. MemOS facilitates the linking of tasks over time or across projects, enhancing workflow efficiency and stability.