chore: remove obsolete chat model wrapper

The ToolCallArgsParsingWrapper was replaced by the model factory
approach which uses native ChatDeepSeek for DeepSeek models.

Removed:
- agent/chat_model_wrapper.py (no longer used)
- tests/unit/test_chat_model_wrapper.py (tests for removed functionality)

The model factory provides a cleaner solution by using provider-specific
implementations rather than wrapping ChatOpenAI.

agent/chat_model_wrapper.py

@@ -1,51 +0,0 @@
-"""
-Chat model wrapper - Passthrough wrapper for ChatOpenAI models.
-
-Originally created to fix DeepSeek tool_calls arg parsing issues, but investigation
-revealed DeepSeek already returns the correct format (arguments as JSON strings).
-
-This wrapper is now a simple passthrough that proxies all calls to the underlying model.
-Kept for backward compatibility and potential future use.
-"""
-
-from typing import Any
-
-
-class ToolCallArgsParsingWrapper:
-    """
-    Passthrough wrapper around ChatOpenAI models.
-
-    After systematic debugging, determined that DeepSeek returns tool_calls.arguments
-    as JSON strings (correct format), so no parsing/conversion is needed.
-
-    This wrapper simply proxies all calls to the wrapped model.
-    """
-
-    def __init__(self, model: Any, **kwargs):
-        """
-        Initialize wrapper around a chat model.
-
-        Args:
-            model: The chat model to wrap
-            **kwargs: Additional parameters (ignored, for compatibility)
-        """
-        self.wrapped_model = model
-
-    @property
-    def _llm_type(self) -> str:
-        """Return identifier for this LLM type"""
-        if hasattr(self.wrapped_model, '_llm_type'):
-            return f"wrapped-{self.wrapped_model._llm_type}"
-        return "wrapped-chat-model"
-
-    def __getattr__(self, name: str):
-        """Proxy all attributes/methods to the wrapped model"""
-        return getattr(self.wrapped_model, name)
-
-    def bind_tools(self, tools: Any, **kwargs):
-        """Bind tools to the wrapped model"""
-        return self.wrapped_model.bind_tools(tools, **kwargs)
-
-    def bind(self, **kwargs):
-        """Bind settings to the wrapped model"""
-        return self.wrapped_model.bind(**kwargs)