diff --git a/libs/langchain_v1/tests/unit_tests/agents/model.py b/libs/langchain_v1/tests/unit_tests/agents/model.py
index 16e4ca52cf04d..917673c5a9ad2 100644
--- a/libs/langchain_v1/tests/unit_tests/agents/model.py
+++ b/libs/langchain_v1/tests/unit_tests/agents/model.py
@@ -3,9 +3,7 @@
 from dataclasses import asdict, is_dataclass
 from typing import (
     Any,
-    Generic,
     Literal,
-    TypeVar,
 )
 
 from langchain_core.callbacks import CallbackManagerForLLMRun
@@ -19,13 +17,12 @@
 from langchain_core.runnables import Runnable
 from langchain_core.tools import BaseTool
 from pydantic import BaseModel
+from typing_extensions import override
 
-StructuredResponseT = TypeVar("StructuredResponseT")
 
-
-class FakeToolCallingModel(BaseChatModel, Generic[StructuredResponseT]):
-    tool_calls: list[list[ToolCall]] | list[list[dict]] | None = None
-    structured_response: StructuredResponseT | None = None
+class FakeToolCallingModel(BaseChatModel):
+    tool_calls: list[list[ToolCall]] | list[list[dict[str, Any]]] | None = None
+    structured_response: Any | None = None
     index: int = 0
     tool_style: Literal["openai", "anthropic"] = "openai"
 
@@ -52,7 +49,9 @@ def _generate(
         if is_native and not tool_calls:
             if isinstance(self.structured_response, BaseModel):
                 content_obj = self.structured_response.model_dump()
-            elif is_dataclass(self.structured_response):
+            elif is_dataclass(self.structured_response) and not isinstance(
+                self.structured_response, type
+            ):
                 content_obj = asdict(self.structured_response)
             elif isinstance(self.structured_response, dict):
                 content_obj = self.structured_response
@@ -71,11 +70,14 @@ def _generate(
     def _llm_type(self) -> str:
         return "fake-tool-call-model"
 
+    @override
     def bind_tools(
         self,
-        tools: Sequence[dict[str, Any] | type[BaseModel] | Callable | BaseTool],
+        tools: Sequence[dict[str, Any] | type | Callable[..., Any] | BaseTool],
+        *,
+        tool_choice: str | None = None,
         **kwargs: Any,
-    ) -> Runnable[LanguageModelInput, BaseMessage]:
+    ) -> Runnable[LanguageModelInput, AIMessage]:
         if len(tools) == 0:
             msg = "Must provide at least one tool"
             raise ValueError(msg)
diff --git a/libs/langchain_v1/tests/unit_tests/agents/test_react_agent.py b/libs/langchain_v1/tests/unit_tests/agents/test_react_agent.py
index be561179cfd73..bfcc87987f320 100644
--- a/libs/langchain_v1/tests/unit_tests/agents/test_react_agent.py
+++ b/libs/langchain_v1/tests/unit_tests/agents/test_react_agent.py
@@ -362,7 +362,7 @@
 #         return "The weather is sunny and 75°F."
 
 #     expected_structured_response = WeatherResponse(temperature=75)
-#     model = FakeToolCallingModel[WeatherResponse](
+#     model = FakeToolCallingModel(
 #         tool_calls=tool_calls, structured_response=expected_structured_response
 #     )
 #     agent = create_agent(
diff --git a/libs/langchain_v1/tests/unit_tests/agents/test_response_format.py b/libs/langchain_v1/tests/unit_tests/agents/test_response_format.py
index 44573309069e7..b19a0c588523a 100644
--- a/libs/langchain_v1/tests/unit_tests/agents/test_response_format.py
+++ b/libs/langchain_v1/tests/unit_tests/agents/test_response_format.py
@@ -343,7 +343,7 @@ def test_union_of_types(self) -> None:
             ],
         ]
 
-        model = FakeToolCallingModel[WeatherBaseModel | LocationResponse](tool_calls=tool_calls)
+        model = FakeToolCallingModel(tool_calls=tool_calls)
 
         agent = create_agent(
             model,
@@ -655,7 +655,7 @@ def test_pydantic_model(self) -> None:
             [{"args": {}, "id": "1", "name": "get_weather"}],
         ]
 
-        model = FakeToolCallingModel[WeatherBaseModel](
+        model = FakeToolCallingModel(
             tool_calls=tool_calls, structured_response=EXPECTED_WEATHER_PYDANTIC
         )
 
@@ -678,7 +678,7 @@ def test_validation_error_with_invalid_response(self) -> None:
         ]
 
         # But we're using WeatherBaseModel which has different field requirements
-        model = FakeToolCallingModel[dict](
+        model = FakeToolCallingModel(
             tool_calls=tool_calls,
             structured_response={"invalid": "data"},  # Wrong structure
         )
@@ -699,7 +699,7 @@ def test_dataclass(self) -> None:
             [{"args": {}, "id": "1", "name": "get_weather"}],
         ]
 
-        model = FakeToolCallingModel[WeatherDataclass](
+        model = FakeToolCallingModel(
             tool_calls=tool_calls, structured_response=EXPECTED_WEATHER_DATACLASS
         )
 
@@ -719,7 +719,7 @@ def test_typed_dict(self) -> None:
             [{"args": {}, "id": "1", "name": "get_weather"}],
         ]
 
-        model = FakeToolCallingModel[WeatherTypedDict](
+        model = FakeToolCallingModel(
             tool_calls=tool_calls, structured_response=EXPECTED_WEATHER_DICT
         )
 
@@ -737,7 +737,7 @@ def test_json_schema(self) -> None:
             [{"args": {}, "id": "1", "name": "get_weather"}],
         ]
 
-        model = FakeToolCallingModel[dict](
+        model = FakeToolCallingModel(
             tool_calls=tool_calls, structured_response=EXPECTED_WEATHER_DICT
         )
 
@@ -858,7 +858,7 @@ def test_union_of_types() -> None:
         ],
     ]
 
-    model = FakeToolCallingModel[WeatherBaseModel | LocationResponse](
+    model = FakeToolCallingModel(
         tool_calls=tool_calls, structured_response=EXPECTED_WEATHER_PYDANTIC
     )