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fix: use _extract_stop_reason to safely handle non-string stop_reason values

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MagicMock objects (and other non-Anthropic responses) can return non-string
values for getattr(response, 'stop_reason', None). Add a typed extraction
helper that returns None unless the value is actually a string, preventing
Pydantic validation errors in LLMCallCompletedEvent.

Co-Authored-By: João <joao@crewai.com>
This commit is contained in:
Devin AI
2026-03-27 20:38:36 +00:00
parent 43b962f149
commit beaf862941
1 changed files with 13 additions and 7 deletions
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20
lib/crewai/src/crewai/llms/providers/anthropic/completion.py
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@@ -676,13 +676,19 @@ class AnthropicCompletion(BaseLLM):
return converted
@staticmethod
def _extract_stop_reason(response: Message | BetaMessage) -> str | None:
"""Extract stop_reason from an Anthropic response, returning None if absent."""
raw = getattr(response, "stop_reason", None)
return raw if isinstance(raw, str) else None
def _warn_if_truncated(
self,
response: Message | BetaMessage,
from_agent: Any | None = None,
) -> None:
"""Log a warning if the response was truncated due to max_tokens."""
stop_reason = getattr(response, "stop_reason", None)
stop_reason = self._extract_stop_reason(response)
if stop_reason == "max_tokens":
agent_hint = f" [{from_agent.role}]" if from_agent else ""
logging.warning(
@@ -872,7 +878,7 @@ class AnthropicCompletion(BaseLLM):
usage = self._extract_anthropic_token_usage(response)
self._track_token_usage_internal(usage)
stop_reason = getattr(response, "stop_reason", None)
stop_reason = self._extract_stop_reason(response)
self._warn_if_truncated(response, from_agent)
if _is_pydantic_model_class(response_model) and response.content:
@@ -1098,7 +1104,7 @@ class AnthropicCompletion(BaseLLM):
usage = self._extract_anthropic_token_usage(final_message)
self._track_token_usage_internal(usage)
stop_reason = getattr(final_message, "stop_reason", None)
stop_reason = self._extract_stop_reason(final_message)
self._warn_if_truncated(final_message, from_agent)
if _is_pydantic_model_class(response_model):
@@ -1302,7 +1308,7 @@ class AnthropicCompletion(BaseLLM):
follow_up_usage = self._extract_anthropic_token_usage(final_response)
self._track_token_usage_internal(follow_up_usage)
stop_reason = getattr(final_response, "stop_reason", None)
stop_reason = self._extract_stop_reason(final_response)
self._warn_if_truncated(final_response, from_agent)
final_content = ""
@@ -1410,7 +1416,7 @@ class AnthropicCompletion(BaseLLM):
usage = self._extract_anthropic_token_usage(response)
self._track_token_usage_internal(usage)
stop_reason = getattr(response, "stop_reason", None)
stop_reason = self._extract_stop_reason(response)
self._warn_if_truncated(response, from_agent)
if _is_pydantic_model_class(response_model) and response.content:
@@ -1614,7 +1620,7 @@ class AnthropicCompletion(BaseLLM):
usage = self._extract_anthropic_token_usage(final_message)
self._track_token_usage_internal(usage)
stop_reason = getattr(final_message, "stop_reason", None)
stop_reason = self._extract_stop_reason(final_message)
self._warn_if_truncated(final_message, from_agent)
if _is_pydantic_model_class(response_model):
@@ -1715,7 +1721,7 @@ class AnthropicCompletion(BaseLLM):
follow_up_usage = self._extract_anthropic_token_usage(final_response)
self._track_token_usage_internal(follow_up_usage)
stop_reason = getattr(final_response, "stop_reason", None)
stop_reason = self._extract_stop_reason(final_response)
self._warn_if_truncated(final_response, from_agent)
final_content = ""