diff --git a/langchain/chains/openai_functions/base.py b/langchain/chains/openai_functions/base.py
index 5ee1bfd3ef3..aa5598e3f01 100644
--- a/langchain/chains/openai_functions/base.py
+++ b/langchain/chains/openai_functions/base.py
@@ -9,6 +9,7 @@ from langchain.base_language import BaseLanguageModel
 from langchain.chains import LLMChain
 from langchain.output_parsers.openai_functions import (
     JsonOutputFunctionsParser,
+    PydanticAttrOutputFunctionsParser,
     PydanticOutputFunctionsParser,
 )
 from langchain.prompts import BasePromptTemplate
@@ -318,17 +319,26 @@ def create_structured_output_chain(
                 chain.run("Harry was a chubby brown beagle who loved chicken")
                 # -> Dog(name="Harry", color="brown", fav_food="chicken")
     """  # noqa: E501
-    function: Dict = {
-        "name": "output_formatter",
-        "description": (
-            "Output formatter. Should always be used to format your response to the"
-            " user."
-        ),
-    }
-    parameters = (
-        output_schema if isinstance(output_schema, dict) else output_schema.schema()
-    )
-    function["parameters"] = parameters
+    if isinstance(output_schema, dict):
+        function: Any = {
+            "name": "output_formatter",
+            "description": (
+                "Output formatter. Should always be used to format your response to the"
+                " user."
+            ),
+            "parameters": output_schema,
+        }
+    else:
+
+        class _OutputFormatter(BaseModel):
+            """Output formatter. Should always be used to format your response to the user."""  # noqa: E501
+
+            output: output_schema  # type: ignore
+
+        function = _OutputFormatter
+        output_parser = output_parser or PydanticAttrOutputFunctionsParser(
+            pydantic_schema=_OutputFormatter, attr_name="output"
+        )
     return create_openai_fn_chain(
         [function], llm, prompt, output_parser=output_parser, **kwargs
     )