# Reading stop_reason — Agent Loops, Stop Conditions and Token Budgets

Source: https://www.geekswithgeeks.com/en/agent-loops/loop-stop-reasons

> Handle the common stop reasons correctly: end of turn, tool use, token limit and stop sequence.

## Why the model stopped

Each response says why generation ended. Common values in the Claude Messages API are `end_turn` (it finished its answer), `tool_use` (it wants a tool run), `max_tokens` (it hit your output limit mid-answer) and `stop_sequence` (it produced a sequence you asked it to stop at). Your loop should handle each one on purpose, never by accident.

## Handling each reason

The `max_tokens` case matters: the answer or a tool call may be cut off, so it is not safe to treat the reply as complete.

```python
match reply.stop_reason:
    case "end_turn":
        return final_text(reply)
    case "tool_use":
        messages.append(run_tools(reply))
    case "max_tokens":
        raise OutputTruncated("reply cut off; raise max_tokens or ask for less")
    case _:
        raise UnexpectedStop(reply.stop_reason)
```

## Never assume end_turn

A loop that treats every non-tool reply as a finished answer will happily return half a sentence when `max_tokens` is hit. Check the reason explicitly.

**Quiz:** What does stop_reason "max_tokens" tell you?

- [ ] The reply finished normally
- [x] The reply may be cut off at your output limit
- [ ] The model refused the task
- [ ] The API key expired

*Answer:* The reply may be cut off at your output limit. It means generation hit the output cap, so the content can be incomplete.