Rasa and similar frameworks require you to train NLU models with labeled intent data. You write training examples, define conversation stories, and tune ML pipelines. Before LLMs, that was the only way to build a conversational chatbot.
OpenClaw skips training entirely. It routes messages straight to an LLM (local or cloud) that understands open-ended conversation without custom data. You get a working assistant in minutes, not weeks.
There is a trade-off. Rasa gives you fine-grained control over intent classification. If you need deterministic routing for a support bot handling thousands of requests per minute, that pipeline approach may fit. For a personal or team assistant, OpenClaw's LLM-first approach is faster and more flexible.