diff --git a/libs/experimental/langchain_experimental/llms/ollama_functions.py b/libs/experimental/langchain_experimental/llms/ollama_functions.py
index f71f91c01a..d9ba49f7b1 100644
--- a/libs/experimental/langchain_experimental/llms/ollama_functions.py
+++ b/libs/experimental/langchain_experimental/llms/ollama_functions.py
@@ -17,7 +17,10 @@ from typing import (
 )
 
 from langchain_community.chat_models.ollama import ChatOllama
-from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
 from langchain_core.language_models import LanguageModelInput
 from langchain_core.messages import AIMessage, BaseMessage, ToolCall
 from langchain_core.output_parsers.base import OutputParserLike
@@ -369,6 +372,86 @@ class OllamaFunctions(ChatOllama):
             generations=[ChatGeneration(message=response_message_with_functions)]
         )
 
+    async def _agenerate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        functions = kwargs.get("functions", [])
+        if "functions" in kwargs:
+            del kwargs["functions"]
+        if "function_call" in kwargs:
+            functions = [
+                fn for fn in functions if fn["name"] == kwargs["function_call"]["name"]
+            ]
+            if not functions:
+                raise ValueError(
+                    "If `function_call` is specified, you must also pass a "
+                    "matching function in `functions`."
+                )
+            del kwargs["function_call"]
+        elif not functions:
+            functions.append(DEFAULT_RESPONSE_FUNCTION)
+        if _is_pydantic_class(functions[0]):
+            functions = [convert_to_ollama_tool(fn) for fn in functions]
+        system_message_prompt_template = SystemMessagePromptTemplate.from_template(
+            self.tool_system_prompt_template
+        )
+        system_message = system_message_prompt_template.format(
+            tools=json.dumps(functions, indent=2)
+        )
+        response_message = await super()._agenerate(
+            [system_message] + messages, stop=stop, run_manager=run_manager, **kwargs
+        )
+        chat_generation_content = response_message.generations[0].text
+        if not isinstance(chat_generation_content, str):
+            raise ValueError("OllamaFunctions does not support non-string output.")
+        try:
+            parsed_chat_result = json.loads(chat_generation_content)
+        except json.JSONDecodeError:
+            raise ValueError(
+                f"""'{self.model}' did not respond with valid JSON. 
+                Please try again. 
+                Response: {chat_generation_content}"""
+            )
+        called_tool_name = parsed_chat_result["tool"]
+        called_tool_arguments = parsed_chat_result["tool_input"]
+        called_tool = next(
+            (fn for fn in functions if fn["name"] == called_tool_name), None
+        )
+        if called_tool is None:
+            raise ValueError(
+                f"Failed to parse a function call from {self.model} output: "
+                f"{chat_generation_content}"
+            )
+        if called_tool["name"] == DEFAULT_RESPONSE_FUNCTION["name"]:
+            return ChatResult(
+                generations=[
+                    ChatGeneration(
+                        message=AIMessage(
+                            content=called_tool_arguments["response"],
+                        )
+                    )
+                ]
+            )
+
+        response_message_with_functions = AIMessage(
+            content="",
+            additional_kwargs={
+                "function_call": {
+                    "name": called_tool_name,
+                    "arguments": json.dumps(called_tool_arguments)
+                    if called_tool_arguments
+                    else "",
+                },
+            },
+        )
+        return ChatResult(
+            generations=[ChatGeneration(message=response_message_with_functions)]
+        )
+
     @property
     def _llm_type(self) -> str:
         return "ollama_functions"