Originally published on Homingo Learn .

An agent stuck in an infinite loop, calling the same tool again and again, is a common way agent runs fail. A few small guards turn it into a clean stop with a reason.

Agent loops look alike: a search returns nothing useful, so the agent searches again with the same words; a test fails, the agent makes the same edit and runs it again; two steps undo each other. The model is not broken. It has no signal that it is repeating itself and no clear definition of finished. This guide adds both, in plain Python 3.10 or newer, around any agent loop that asks a model for the next action. Put the code blocks in one file, in order.

Frameworks have a step cap built in: LangChain’s legacy AgentExecutor has max_iterations (“Agent stopped due to iteration limit or time limit.”), LangGraph has recursion_limit (“Recursion limit of 25 reached without hitting a stop condition”), and the OpenAI Agents SDK has max_turns .…