fix: keep stopwords updated

lib/crewai/src/crewai/llms/hooks/transport.py

@@ -25,7 +25,7 @@ if TYPE_CHECKING:
     from crewai.llms.hooks.base import BaseInterceptor
 
 
-class HTTPTransportKwargs(TypedDict):
+class HTTPTransportKwargs(TypedDict, total=False):
     """Typed dictionary for httpx.HTTPTransport initialization parameters.
 
     These parameters configure the underlying HTTP transport behavior including
@@ -33,14 +33,14 @@ class HTTPTransportKwargs(TypedDict):
     """
 
     verify: bool | str | SSLContext
-    cert: NotRequired[CertTypes | None]
+    cert: NotRequired[CertTypes]
     trust_env: bool
     http1: bool
     http2: bool
     limits: Limits
-    proxy: NotRequired[ProxyTypes | None]
+    proxy: NotRequired[ProxyTypes]
-    uds: NotRequired[str | None]
+    uds: NotRequired[str]
-    local_address: NotRequired[str | None]
+    local_address: NotRequired[str]
     retries: int
     socket_options: NotRequired[
         Iterable[
@@ -48,7 +48,6 @@ class HTTPTransportKwargs(TypedDict):
             | tuple[int, int, bytes | bytearray]
             | tuple[int, int, None, int]
         ]
-        | None
     ]
 
 

lib/crewai/src/crewai/llms/providers/anthropic/completion.py

@@ -94,6 +94,30 @@ class AnthropicCompletion(BaseLLM):
         self.is_claude_3 = "claude-3" in model.lower()
         self.supports_tools = self.is_claude_3  # Claude 3+ supports tool use
 
+    @property
+    def stop(self) -> list[str]:
+        """Get stop sequences sent to the API."""
+        return self.stop_sequences
+
+    @stop.setter
+    def stop(self, value: list[str] | str | None) -> None:
+        """Set stop sequences.
+
+        Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
+        are properly sent to the Anthropic API.
+
+        Args:
+            value: Stop sequences as a list, single string, or None
+        """
+        if value is None:
+            self.stop_sequences = []
+        elif isinstance(value, str):
+            self.stop_sequences = [value]
+        elif isinstance(value, list):
+            self.stop_sequences = value
+        else:
+            self.stop_sequences = []
+
     def _get_client_params(self) -> dict[str, Any]:
         """Get client parameters."""
 

lib/crewai/src/crewai/llms/providers/bedrock/completion.py

@@ -243,6 +243,30 @@ class BedrockCompletion(BaseLLM):
         # Handle inference profiles for newer models
         self.model_id = model
 
+    @property
+    def stop(self) -> list[str]:
+        """Get stop sequences sent to the API."""
+        return list(self.stop_sequences)
+
+    @stop.setter
+    def stop(self, value: Sequence[str] | str | None) -> None:
+        """Set stop sequences.
+
+        Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
+        are properly sent to the Bedrock API.
+
+        Args:
+            value: Stop sequences as a Sequence, single string, or None
+        """
+        if value is None:
+            self.stop_sequences = []
+        elif isinstance(value, str):
+            self.stop_sequences = [value]
+        elif isinstance(value, Sequence):
+            self.stop_sequences = list(value)
+        else:
+            self.stop_sequences = []
+
     def call(
         self,
         messages: str | list[LLMMessage],

lib/crewai/src/crewai/llms/providers/gemini/completion.py

@@ -104,6 +104,30 @@ class GeminiCompletion(BaseLLM):
         self.is_gemini_1_5 = "gemini-1.5" in model.lower()
         self.supports_tools = self.is_gemini_1_5 or self.is_gemini_2
 
+    @property
+    def stop(self) -> list[str]:
+        """Get stop sequences sent to the API."""
+        return self.stop_sequences
+
+    @stop.setter
+    def stop(self, value: list[str] | str | None) -> None:
+        """Set stop sequences.
+
+        Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
+        are properly sent to the Gemini API.
+
+        Args:
+            value: Stop sequences as a list, single string, or None
+        """
+        if value is None:
+            self.stop_sequences = []
+        elif isinstance(value, str):
+            self.stop_sequences = [value]
+        elif isinstance(value, list):
+            self.stop_sequences = value
+        else:
+            self.stop_sequences = []
+
     def _initialize_client(self, use_vertexai: bool = False) -> genai.Client:  # type: ignore[no-any-unimported]
         """Initialize the Google Gen AI client with proper parameter handling.
 

lib/crewai/tests/cassettes/test_anthropic_stop_sequences_sent_to_api.yaml

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+- request:
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+    headers:
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lib/crewai/tests/llms/anthropic/test_anthropic.py

@@ -664,3 +664,37 @@ def test_anthropic_token_usage_tracking():
         assert usage["input_tokens"] == 50
         assert usage["output_tokens"] == 25
         assert usage["total_tokens"] == 75
+
+
+def test_anthropic_stop_sequences_sync():
+    """Test that stop and stop_sequences attributes stay synchronized."""
+    llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")
+
+    # Test setting stop as a list
+    llm.stop = ["\nObservation:", "\nThought:"]
+    assert llm.stop_sequences == ["\nObservation:", "\nThought:"]
+    assert llm.stop == ["\nObservation:", "\nThought:"]
+
+    # Test setting stop as a string
+    llm.stop = "\nFinal Answer:"
+    assert llm.stop_sequences == ["\nFinal Answer:"]
+    assert llm.stop == ["\nFinal Answer:"]
+
+    # Test setting stop as None
+    llm.stop = None
+    assert llm.stop_sequences == []
+    assert llm.stop == []
+
+
+@pytest.mark.vcr(filter_headers=["authorization", "x-api-key"])
+def test_anthropic_stop_sequences_sent_to_api():
+    """Test that stop_sequences are properly sent to the Anthropic API."""
+    llm = LLM(model="anthropic/claude-3-5-haiku-20241022")
+
+    llm.stop = ["\nObservation:", "\nThought:"]
+
+    result = llm.call("Say hello in one word")
+
+    assert result is not None
+    assert isinstance(result, str)
+    assert len(result) > 0

lib/crewai/tests/llms/bedrock/test_bedrock.py

@@ -736,3 +736,56 @@ def test_bedrock_client_error_handling():
         with pytest.raises(RuntimeError) as exc_info:
             llm.call("Hello")
         assert "throttled" in str(exc_info.value).lower()
+
+
+def test_bedrock_stop_sequences_sync():
+    """Test that stop and stop_sequences attributes stay synchronized."""
+    llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
+
+    # Test setting stop as a list
+    llm.stop = ["\nObservation:", "\nThought:"]
+    assert list(llm.stop_sequences) == ["\nObservation:", "\nThought:"]
+    assert llm.stop == ["\nObservation:", "\nThought:"]
+
+    # Test setting stop as a string
+    llm.stop = "\nFinal Answer:"
+    assert list(llm.stop_sequences) == ["\nFinal Answer:"]
+    assert llm.stop == ["\nFinal Answer:"]
+
+    # Test setting stop as None
+    llm.stop = None
+    assert list(llm.stop_sequences) == []
+    assert llm.stop == []
+
+
+def test_bedrock_stop_sequences_sent_to_api():
+    """Test that stop_sequences are properly sent to the Bedrock API."""
+    llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
+
+    # Set stop sequences via the stop attribute (simulating CrewAgentExecutor)
+    llm.stop = ["\nObservation:", "\nThought:"]
+
+    # Patch the API call to capture parameters without making real call
+    with patch.object(llm.client, 'converse') as mock_converse:
+        mock_response = {
+            'output': {
+                'message': {
+                    'role': 'assistant',
+                    'content': [{'text': 'Hello'}]
+                }
+            },
+            'usage': {
+                'inputTokens': 10,
+                'outputTokens': 5,
+                'totalTokens': 15
+            }
+        }
+        mock_converse.return_value = mock_response
+
+        llm.call("Say hello in one word")
+
+        # Verify stop_sequences were passed to the API in the inference config
+        call_kwargs = mock_converse.call_args[1]
+        assert "inferenceConfig" in call_kwargs
+        assert "stopSequences" in call_kwargs["inferenceConfig"]
+        assert call_kwargs["inferenceConfig"]["stopSequences"] == ["\nObservation:", "\nThought:"]

lib/crewai/tests/llms/google/test_google.py

@@ -648,3 +648,55 @@ def test_gemini_token_usage_tracking():
         assert usage["candidates_token_count"] == 25
         assert usage["total_token_count"] == 75
         assert usage["total_tokens"] == 75
+
+
+def test_gemini_stop_sequences_sync():
+    """Test that stop and stop_sequences attributes stay synchronized."""
+    llm = LLM(model="google/gemini-2.0-flash-001")
+
+    # Test setting stop as a list
+    llm.stop = ["\nObservation:", "\nThought:"]
+    assert llm.stop_sequences == ["\nObservation:", "\nThought:"]
+    assert llm.stop == ["\nObservation:", "\nThought:"]
+
+    # Test setting stop as a string
+    llm.stop = "\nFinal Answer:"
+    assert llm.stop_sequences == ["\nFinal Answer:"]
+    assert llm.stop == ["\nFinal Answer:"]
+
+    # Test setting stop as None
+    llm.stop = None
+    assert llm.stop_sequences == []
+    assert llm.stop == []
+
+
+def test_gemini_stop_sequences_sent_to_api():
+    """Test that stop_sequences are properly sent to the Gemini API."""
+    llm = LLM(model="google/gemini-2.0-flash-001")
+
+    # Set stop sequences via the stop attribute (simulating CrewAgentExecutor)
+    llm.stop = ["\nObservation:", "\nThought:"]
+
+    # Patch the API call to capture parameters without making real call
+    with patch.object(llm.client.models, 'generate_content') as mock_generate:
+        mock_response = MagicMock()
+        mock_response.text = "Hello"
+        mock_response.candidates = []
+        mock_response.usage_metadata = MagicMock(
+            prompt_token_count=10,
+            candidates_token_count=5,
+            total_token_count=15
+        )
+        mock_generate.return_value = mock_response
+
+        llm.call("Say hello in one word")
+
+        # Verify stop_sequences were passed to the API in the config
+        call_kwargs = mock_generate.call_args[1]
+        assert "config" in call_kwargs
+        # The config object should have stop_sequences set
+        config = call_kwargs["config"]
+        # Check if the config has stop_sequences attribute
+        assert hasattr(config, 'stop_sequences') or 'stop_sequences' in config.__dict__
+        if hasattr(config, 'stop_sequences'):
+            assert config.stop_sequences == ["\nObservation:", "\nThought:"]