diff --git a/libs/langchain_v1/langchain/agents/middleware/summarization.py b/libs/langchain_v1/langchain/agents/middleware/summarization.py
index 10baf724662c0..ab4465e042bf0 100644
--- a/libs/langchain_v1/langchain/agents/middleware/summarization.py
+++ b/libs/langchain_v1/langchain/agents/middleware/summarization.py
@@ -482,6 +482,49 @@ def _find_safe_cutoff_point(self, messages: list[AnyMessage], cutoff_index: int)
             cutoff_index += 1
         return cutoff_index
 
+    def _format_clean_history(self, messages: list[AnyMessage]) -> str:
+        """Formats messages to a simple string (Role: Content) to save tokens.
+
+        This method strips away metadata (token usage, logprobs) and handles
+        multimodal content (like images) by extracting only the text.
+
+        Args:
+            messages: The list of conversation messages to format.
+
+        Returns:
+            A clean string representation of the conversation history.
+        """
+        formatted_lines = []
+        for msg in messages:
+            # 1. Map standard message types to readable roles
+            role = msg.type
+            role_mapping = {
+                "human": "User",
+                "ai": "Assistant",
+                "system": "System",
+                "tool": "Tool Output",
+                "function": "Function Output",
+                "chat": "Chat",
+            }
+            display_role = role_mapping.get(role.lower(), role.capitalize())
+
+            # 2. Handle content (strings vs multimodal lists)
+            content = msg.content
+            if isinstance(content, list):
+                text_parts = []
+                for item in content:
+                    if isinstance(item, str):
+                        text_parts.append(item)
+                    elif isinstance(item, dict):
+                        text_parts.append(item.get("text", str(item)))
+                    else:
+                        text_parts.append(str(item))
+                content = " ".join(text_parts)
+
+            formatted_lines.append(f"{display_role}: {content}")
+
+        return "\n".join(formatted_lines)
+
     def _create_summary(self, messages_to_summarize: list[AnyMessage]) -> str:
         """Generate summary for the given messages."""
         if not messages_to_summarize:
@@ -491,8 +534,11 @@ def _create_summary(self, messages_to_summarize: list[AnyMessage]) -> str:
         if not trimmed_messages:
             return "Previous conversation was too long to summarize."
 
+        # FIX: Convert rich objects to a plain string before formatting
+        clean_history = self._format_clean_history(trimmed_messages)
+
         try:
-            response = self.model.invoke(self.summary_prompt.format(messages=trimmed_messages))
+            response = self.model.invoke(self.summary_prompt.format(messages=clean_history))
             return response.text.strip()
         except Exception as e:
             return f"Error generating summary: {e!s}"
@@ -506,10 +552,11 @@ async def _acreate_summary(self, messages_to_summarize: list[AnyMessage]) -> str
         if not trimmed_messages:
             return "Previous conversation was too long to summarize."
 
+        # FIX: Convert rich objects to a plain string before formatting
+        clean_history = self._format_clean_history(trimmed_messages)
+
         try:
-            response = await self.model.ainvoke(
-                self.summary_prompt.format(messages=trimmed_messages)
-            )
+            response = await self.model.ainvoke(self.summary_prompt.format(messages=clean_history))
             return response.text.strip()
         except Exception as e:
             return f"Error generating summary: {e!s}"
diff --git a/libs/langchain_v1/tests/unit_tests/agents/middleware/implementations/test_summarization_middleware.py b/libs/langchain_v1/tests/unit_tests/agents/middleware/implementations/test_summarization_middleware.py
new file mode 100644
index 0000000000000..291f00d5cdd50
--- /dev/null
+++ b/libs/langchain_v1/tests/unit_tests/agents/middleware/implementations/test_summarization_middleware.py
@@ -0,0 +1,74 @@
+from typing import Any
+
+from langchain_core.language_models import BaseChatModel
+from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, ToolMessage
+from langchain_core.outputs import ChatGeneration, ChatResult
+
+from langchain.agents.middleware.summarization import SummarizationMiddleware
+
+
+# --- 1. Define a Mock Model for Testing ---
+# This prevents us from needing real API keys (like OpenAI) for unit tests
+class FakeChatModel(BaseChatModel):
+    def _generate(
+        self, messages: list[BaseMessage], stop: list[str] | None = None, **kwargs: Any
+    ) -> ChatResult:
+        return ChatResult(generations=[ChatGeneration(message=AIMessage(content="Summary"))])
+
+    @property
+    def _llm_type(self) -> str:
+        return "fake-chat-model"
+
+
+# --- 2. The Tests ---
+
+
+def test_format_clean_history_basic():
+    """Test that basic human/ai messages are formatted correctly."""
+    # Use the FakeChatModel instead of "gpt-3.5-turbo"
+    middleware = SummarizationMiddleware(model=FakeChatModel())
+
+    messages = [
+        HumanMessage(content="Hello"),
+        AIMessage(
+            content="Hi there", response_metadata={"token_usage": 100}
+        ),  # Metadata that should be stripped
+    ]
+
+    result = middleware._format_clean_history(messages)
+
+    expected = "User: Hello\nAssistant: Hi there"
+    assert result == expected
+
+
+def test_format_clean_history_tool_messages():
+    """Test that tool messages are handled correctly."""
+    middleware = SummarizationMiddleware(model=FakeChatModel())
+
+    messages = [
+        HumanMessage(content="Search for apples"),
+        AIMessage(content="", tool_calls=[{"name": "search", "args": {}, "id": "123"}]),
+        ToolMessage(content="Found red apples", tool_call_id="123"),
+    ]
+
+    result = middleware._format_clean_history(messages)
+
+    # We expect the tool output to be included clearly
+    assert "Tool Output: Found red apples" in result
+
+
+def test_format_clean_history_multimodal():
+    """Test that multimodal (list) content is flattened to string."""
+    middleware = SummarizationMiddleware(model=FakeChatModel())
+
+    # Simulate a GPT-4o multimodal message
+    multimodal_content = [
+        {"type": "text", "text": "Look at this image"},
+        {"type": "image_url", "image_url": {"url": "http://image.com"}},
+    ]
+    messages = [HumanMessage(content=multimodal_content)]
+
+    result = middleware._format_clean_history(messages)
+
+    # It should extract "Look at this image" and handle the dict gracefully
+    assert "User: Look at this image" in result