diff --git a/libs/partners/groq/langchain_groq/chat_models.py b/libs/partners/groq/langchain_groq/chat_models.py
index 05dc4724570..8f1e1f68046 100644
--- a/libs/partners/groq/langchain_groq/chat_models.py
+++ b/libs/partners/groq/langchain_groq/chat_models.py
@@ -52,7 +52,6 @@ from langchain_core.messages import (
     ToolMessage,
     ToolMessageChunk,
 )
-from langchain_core.messages.tool import tool_call_chunk as create_tool_call_chunk
 from langchain_core.output_parsers import (
     JsonOutputParser,
     PydanticOutputParser,
@@ -387,9 +386,9 @@ class ChatGroq(BaseChatModel):
             self.temperature = 1e-8
 
         client_params: Dict[str, Any] = {
-            "api_key": self.groq_api_key.get_secret_value()
-            if self.groq_api_key
-            else None,
+            "api_key": (
+                self.groq_api_key.get_secret_value() if self.groq_api_key else None
+            ),
             "base_url": self.groq_api_base,
             "timeout": self.request_timeout,
             "max_retries": self.max_retries,
@@ -504,42 +503,6 @@ class ChatGroq(BaseChatModel):
     ) -> Iterator[ChatGenerationChunk]:
         message_dicts, params = self._create_message_dicts(messages, stop)
 
-        # groq api does not support streaming with tools yet
-        if "tools" in kwargs:
-            response = self.client.create(
-                messages=message_dicts, **{**params, **kwargs}
-            )
-            chat_result = self._create_chat_result(response)
-            generation = chat_result.generations[0]
-            message = cast(AIMessage, generation.message)
-            tool_call_chunks = [
-                create_tool_call_chunk(
-                    name=rtc["function"].get("name"),
-                    args=rtc["function"].get("arguments"),
-                    id=rtc.get("id"),
-                    index=rtc.get("index"),
-                )
-                for rtc in message.additional_kwargs.get("tool_calls", [])
-            ]
-            chunk_ = ChatGenerationChunk(
-                message=AIMessageChunk(
-                    content=message.content,
-                    additional_kwargs=message.additional_kwargs,
-                    tool_call_chunks=tool_call_chunks,
-                    usage_metadata=message.usage_metadata,
-                ),
-                generation_info=generation.generation_info,
-            )
-            if run_manager:
-                geninfo = chunk_.generation_info or {}
-                run_manager.on_llm_new_token(
-                    chunk_.text,
-                    chunk=chunk_,
-                    logprobs=geninfo.get("logprobs"),
-                )
-            yield chunk_
-            return
-
         params = {**params, **kwargs, "stream": True}
 
         default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
@@ -576,42 +539,6 @@ class ChatGroq(BaseChatModel):
     ) -> AsyncIterator[ChatGenerationChunk]:
         message_dicts, params = self._create_message_dicts(messages, stop)
 
-        # groq api does not support streaming with tools yet
-        if "tools" in kwargs:
-            response = await self.async_client.create(
-                messages=message_dicts, **{**params, **kwargs}
-            )
-            chat_result = self._create_chat_result(response)
-            generation = chat_result.generations[0]
-            message = cast(AIMessage, generation.message)
-            tool_call_chunks = [
-                {
-                    "name": rtc["function"].get("name"),
-                    "args": rtc["function"].get("arguments"),
-                    "id": rtc.get("id"),
-                    "index": rtc.get("index"),
-                }
-                for rtc in message.additional_kwargs.get("tool_calls", [])
-            ]
-            chunk_ = ChatGenerationChunk(
-                message=AIMessageChunk(
-                    content=message.content,
-                    additional_kwargs=message.additional_kwargs,
-                    tool_call_chunks=tool_call_chunks,  # type: ignore[arg-type]
-                    usage_metadata=message.usage_metadata,
-                ),
-                generation_info=generation.generation_info,
-            )
-            if run_manager:
-                geninfo = chunk_.generation_info or {}
-                await run_manager.on_llm_new_token(
-                    chunk_.text,
-                    chunk=chunk_,
-                    logprobs=geninfo.get("logprobs"),
-                )
-            yield chunk_
-            return
-
         params = {**params, **kwargs, "stream": True}
 
         default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk