Programming Tutorials

  You are watching your terminal, waiting for your multi-agent system to produce a result. The logic seems sound, the tools are imported, and the goal is clear. But after several minutes of processing, the application crashes with the dreaded exception: RuntimeError: Agent stopped due to iteration limit or time limit. This is the most common bottleneck in production-grade CrewAI applications. It usually indicates that your agent has entered a "cognitive loop"—trying the same failing action repeatedly until the safety mechanism kicks in. This guide analyzes the root cause of this error within the ReAct (Reasoning and Acting) pattern and provides three distinct architectural fixes to resolve it. The Root Cause: The ReAct Loop Trap To fix the error, you must understand the underlying mechanism. CrewAI agents utilize the ReAct pattern. They do not simply "answer"; they follow a strict loop: Thought:  Analyze the current state. Action:  Decide to use a specific tool. Obs...