diff --git a/pr_agent/algo/ai_handlers/langchain_ai_handler.py b/pr_agent/algo/ai_handlers/langchain_ai_handler.py
index b796f859..8e29b8bb 100644
--- a/pr_agent/algo/ai_handlers/langchain_ai_handler.py
+++ b/pr_agent/algo/ai_handlers/langchain_ai_handler.py
@@ -23,7 +23,9 @@ OPENAI_RETRIES = 5
 class LangChainOpenAIHandler(BaseAiHandler):
     def __init__(self):
         if not _LANGCHAIN_INSTALLED:
-            raise ImportError("LangChain is not installed. Please install it with `pip install langchain`.")
+            error_msg = "LangChain is not installed. Please install it with `pip install langchain`."
+            get_logger().error(error_msg)
+            raise ImportError(error_msg)
         
         super().__init__()
         self.azure = get_settings().get("OPENAI.API_TYPE", "").lower() == "azure"
@@ -42,18 +44,66 @@ class LangChainOpenAIHandler(BaseAiHandler):
         """
         return get_settings().get("OPENAI.DEPLOYMENT_ID", None)
 
+    async def _create_chat_async(self, deployment_id=None):
+        try:
+            if self.azure:
+                # Using Azure OpenAI service
+                return AzureChatOpenAI(
+                    openai_api_key=get_settings().openai.key,
+                    openai_api_version=get_settings().openai.api_version,
+                    azure_deployment=deployment_id,
+                    azure_endpoint=get_settings().openai.api_base,
+                )
+            else:
+                # Using standard OpenAI or other LLM services
+                openai_api_base = get_settings().get("OPENAI.API_BASE", None)
+                if openai_api_base is None or len(openai_api_base) == 0:
+                    return ChatOpenAI(openai_api_key=get_settings().openai.key)
+                else:
+                    return ChatOpenAI(
+                        openai_api_key=get_settings().openai.key, 
+                        openai_api_base=openai_api_base
+                    )
+        except AttributeError as e:
+            # Handle configuration errors
+            error_msg = f"OpenAI {e.name} is required" if getattr(e, "name") else str(e)
+            get_logger().error(error_msg)
+            raise ValueError(error_msg) from e
+
     @retry(
         retry=retry_if_exception_type(openai.APIError) & retry_if_not_exception_type(openai.RateLimitError),
         stop=stop_after_attempt(OPENAI_RETRIES),
     )
-    async def chat_completion(self, model: str, system: str, user: str, temperature: float = 0.2, img_path: Optional[str] = None):
+    async def chat_completion(self, model: str, system: str, user: str, temperature: float = 0.2, img_path: str = None):
         if img_path:
             get_logger().warning(f"Image path is not supported for LangChainOpenAIHandler. Ignoring image path: {img_path}")
         try:
             messages = [SystemMessage(content=system), HumanMessage(content=user)]
+            llm = await self._create_chat_async(deployment_id=self.deployment_id)
+            
+            if not hasattr(llm, 'ainvoke'):
+                error_message = (
+                    f"The Langchain LLM object ({type(llm)}) does not have an 'ainvoke' async method. "
+                    f"Please update your Langchain library to the latest version or "
+                    f"check your LLM configuration to support async calls. "
+                    f"PR-Agent is designed to utilize Langchain's async capabilities."
+                )
+                get_logger().error(error_message)
+                raise NotImplementedError(error_message)
+
+            # Handle parameters based on LLM type
+            if isinstance(llm, (ChatOpenAI, AzureChatOpenAI)):
+                # OpenAI models support all parameters
+                resp = await llm.ainvoke(
+                    input=messages,
+                    model=model,
+                    temperature=temperature
+                )
+            else:
+                # Other LLMs (like Gemini) only support input parameter
+                get_logger().info(f"Using simplified ainvoke for {type(llm)}")
+                resp = await llm.ainvoke(input=messages)
 
-            # get a chat completion from the formatted messages
-            resp = self.chat(messages, model=model, temperature=temperature)
             finish_reason = "completed"
             return resp.content, finish_reason
 
diff --git a/pr_agent/algo/ai_handlers/openai_ai_handler.py b/pr_agent/algo/ai_handlers/openai_ai_handler.py
index 253282b0..f5fb99f6 100644
--- a/pr_agent/algo/ai_handlers/openai_ai_handler.py
+++ b/pr_agent/algo/ai_handlers/openai_ai_handler.py
@@ -42,8 +42,10 @@ class OpenAIHandler(BaseAiHandler):
         retry=retry_if_exception_type(openai.APIError) & retry_if_not_exception_type(openai.RateLimitError),
         stop=stop_after_attempt(OPENAI_RETRIES),
     )
-    async def chat_completion(self, model: str, system: str, user: str, temperature: float = 0.2):
+    async def chat_completion(self, model: str, system: str, user: str, temperature: float = 0.2, img_path: str = None):
         try:
+            if img_path:
+                get_logger().warning(f"Image path is not supported for OpenAIHandler. Ignoring image path: {img_path}")
             get_logger().info("System: ", system)
             get_logger().info("User: ", user)
             messages = [{"role": "system", "content": system}, {"role": "user", "content": user}]