The continuous think-act-observe cycle that drives autonomous OpenClaw behavior
The OpenClaw agent loop is the core execution model: receive input → think (LLM inference) → act (tool use) → observe (tool results) → think again → respond. This loop continues until the agent reaches a conclusion or completes its task, and is the mechanism behind all autonomous OpenClaw behavior.
Each turn in the agent loop starts with the current context (conversation history + memory + available tools). The LLM decides whether to respond directly or call a tool. If it calls a tool (read a file, run a command, browse the web), the result is added to context and the loop continues. This repeats until the agent responds to the user. For long-running tasks, the loop can run for many iterations autonomously.
Understanding the agent loop helps you design better agents. The more effectively your SOUL.md and AGENTS.md guide the loop — what tools to use, when to stop, what to remember — the more reliably your agent accomplishes complex tasks without getting stuck or going off-track.
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