diff --git a/libs/aws/langchain_aws/chat_models/bedrock.py b/libs/aws/langchain_aws/chat_models/bedrock.py
index 914b1800..e68cfdcc 100644
--- a/libs/aws/langchain_aws/chat_models/bedrock.py
+++ b/libs/aws/langchain_aws/chat_models/bedrock.py
@@ -35,6 +35,7 @@
 )
 from langchain_core.messages.ai import UsageMetadata
 from langchain_core.messages.tool import ToolCall, ToolMessage
+from langchain_core.messages.utils import convert_to_openai_messages
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
 from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
 from langchain_core.tools import BaseTool
@@ -285,6 +286,50 @@ def convert_messages_to_prompt_writer(messages: List[BaseMessage]) -> str:
     )
 
 
+def _convert_one_message_to_text_openai(message: BaseMessage) -> str:
+    if isinstance(message, SystemMessage):
+        message_text = (
+            f"<|start|>system<|message|>{message.content}<|end|>"
+        )
+    elif isinstance(message, ChatMessage):
+        # developer role messages
+        message_text = (
+            f"<|start|>{message.role}<|message|>{message.content}<|end|>"
+        )
+    elif isinstance(message, HumanMessage):
+        message_text = (
+            f"<|start|>user<|message|>{message.content}<|end|>"
+        )
+    elif isinstance(message, AIMessage):
+        message_text = (
+            f"<|start|>assistant<|channel|>final<|message|>{message.content}<|end|>"
+        )
+    elif isinstance(message, ToolMessage):
+      # TODO: Tool messages in the OpenAI format should use "<|start|>{toolname} to=assistant<|message|>"
+      # Need to extract the tool name from the ToolMessage content or tool_call_id
+      # For now using generic "to=assistant" format as placeholder until we implement tool calling
+      # Will be resolved in follow-up PR with full tool support
+        message_text = (
+            f"<|start|>to=assistant<|channel|>commentary<|message|>{message.content}<|end|>"
+        )
+    else:
+        raise ValueError(f"Got unknown type {message}")
+
+    return message_text
+
+
+def convert_messages_to_prompt_openai(messages: List[BaseMessage]) -> str:
+    """Convert a list of messages to a Harmony format prompt for OpenAI Responses API."""
+
+    prompt = "\n"
+    for message in messages:
+        prompt += _convert_one_message_to_text_openai(message)
+
+    prompt += "<|start|>assistant\n\n"
+
+    return prompt
+
+
 def _format_image(image_url: str) -> Dict:
     """
     Formats an image of format data:image/jpeg;base64,{b64_string}
@@ -640,6 +685,8 @@ def convert_messages_to_prompt(
             )
         elif provider == "writer":
             prompt = convert_messages_to_prompt_writer(messages=messages)
+        elif provider == "openai":
+            prompt = convert_messages_to_prompt_openai(messages=messages)
         else:
             raise NotImplementedError(
                 f"Provider {provider} model does not support chat."
@@ -649,10 +696,11 @@ def convert_messages_to_prompt(
     @classmethod
     def format_messages(
         cls, provider: str, messages: List[BaseMessage]
-    ) -> Tuple[Optional[str], List[Dict]]:
+    ) -> Union[Tuple[Optional[str], List[Dict]], List[Dict]]:
         if provider == "anthropic":
             return _format_anthropic_messages(messages)
-
+        elif provider == "openai":
+            return convert_to_openai_messages(messages)
         raise NotImplementedError(
             f"Provider {provider} not supported for format_messages"
         )
@@ -777,6 +825,8 @@ def _stream(
                     system = self.system_prompt_with_tools + f"\n{system}"
                 else:
                     system = self.system_prompt_with_tools
+        elif provider == "openai":
+            formatted_messages = ChatPromptAdapter.format_messages(provider, messages)
         else:
             prompt = ChatPromptAdapter.convert_messages_to_prompt(
                 provider=provider, messages=messages, model=self._get_base_model()
@@ -876,6 +926,8 @@ def _generate(
                         system = self.system_prompt_with_tools + f"\n{system}"
                     else:
                         system = self.system_prompt_with_tools
+            elif provider == "openai":
+                formatted_messages = ChatPromptAdapter.format_messages(provider, messages)
             else:
                 prompt = ChatPromptAdapter.convert_messages_to_prompt(
                     provider=provider, messages=messages, model=self._get_base_model()
diff --git a/libs/aws/langchain_aws/llms/bedrock.py b/libs/aws/langchain_aws/llms/bedrock.py
index 3204363d..0542a528 100644
--- a/libs/aws/langchain_aws/llms/bedrock.py
+++ b/libs/aws/langchain_aws/llms/bedrock.py
@@ -377,6 +377,8 @@ def prepare_input(
                     input_body["max_tokens"] = max_tokens
                 elif provider == "writer":
                     input_body["max_tokens"] = max_tokens
+                elif provider == "openai":
+                    input_body["max_output_tokens"] = max_tokens
                 else:
                     # TODO: Add AI21 support, param depends on specific model.
                     pass
@@ -391,6 +393,13 @@ def prepare_input(
                 input_body["textGenerationConfig"]["maxTokenCount"] = max_tokens
             if temperature is not None:
                 input_body["textGenerationConfig"]["temperature"] = temperature
+
+        elif provider == "openai":
+            input_body["messages"] = messages
+            if max_tokens:
+                input_body["max_tokens"] = max_tokens
+            if temperature is not None:
+                input_body["temperature"] = temperature
         else:
             input_body["inputText"] = prompt
 
@@ -442,6 +451,8 @@ def prepare_output(cls, provider: str, response: Any) -> dict:
                 text = response_body.get("generation")
             elif provider == "mistral":
                 text = response_body.get("outputs")[0].get("text")
+            elif provider == "openai":
+                text = response_body.get("choices")[0].get("message").get("content")
             else:
                 text = response_body.get("results")[0].get("outputText")