diff --git a/libs/partners/openai/langchain_openai/chat_models/base.py b/libs/partners/openai/langchain_openai/chat_models/base.py
index bfa293b446e..5631eb1190d 100644
--- a/libs/partners/openai/langchain_openai/chat_models/base.py
+++ b/libs/partners/openai/langchain_openai/chat_models/base.py
@@ -726,6 +726,7 @@ class BaseChatOpenAI(BaseChatModel):
         _, encoding_model = self._get_encoding_model()
         return encoding_model.encode(text)
 
+    # TODO: Count bound tools as part of input.
     def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
         """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
 
@@ -760,7 +761,13 @@ class BaseChatOpenAI(BaseChatModel):
         for message in messages_dict:
             num_tokens += tokens_per_message
             for key, value in message.items():
+                # This is an inferred approximation. OpenAI does not document how to
+                # count tool message tokens.
+                if key == "tool_call_id":
+                    num_tokens += 3
+                    continue
                 if isinstance(value, list):
+                    # content or tool calls
                     for val in value:
                         if isinstance(val, str) or val["type"] == "text":
                             text = val["text"] if isinstance(val, dict) else val
@@ -773,10 +780,19 @@ class BaseChatOpenAI(BaseChatModel):
                                 if not image_size:
                                     continue
                                 num_tokens += _count_image_tokens(*image_size)
+                        # Tool/function call token counting is not documented by OpenAI.
+                        # This is an approximation.
+                        elif val["type"] == "function":
+                            num_tokens += len(
+                                encoding.encode(val["function"]["arguments"])
+                            )
+                            num_tokens += len(encoding.encode(val["function"]["name"]))
                         else:
                             raise ValueError(
                                 f"Unrecognized content block type\n\n{val}"
                             )
+                elif not value:
+                    continue
                 else:
                     # Cast str(value) in case the message value is not a string
                     # This occurs with function messages
diff --git a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py
index 1250e02e0a9..f523aacc2c5 100644
--- a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py
+++ b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py
@@ -334,3 +334,38 @@ def test_with_structured_output(schema: Union[Type[BaseModel], dict]) -> None:
     """Test passing in manually construct tool call message."""
     llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
     llm.with_structured_output(schema)
+
+
+def test_get_num_tokens_from_messages() -> None:
+    llm = ChatOpenAI(model="gpt-4o")
+    messages = [
+        SystemMessage("you're a good assistant"),
+        HumanMessage("how are you"),
+        HumanMessage(
+            [
+                {"type": "text", "text": "what's in this image"},
+                {"type": "image_url", "image_url": {"url": "https://foobar.com"}},
+                {
+                    "type": "image_url",
+                    "image_url": {"url": "https://foobar.com", "detail": "low"},
+                },
+            ]
+        ),
+        AIMessage("a nice bird"),
+        AIMessage(
+            "", tool_calls=[ToolCall(id="foo", name="bar", args={"arg1": "arg1"})]
+        ),
+        AIMessage(
+            "",
+            additional_kwargs={
+                "function_call": json.dumps({"arguments": "old", "name": "fun"})
+            },
+        ),
+        AIMessage(
+            "text", tool_calls=[ToolCall(id="foo", name="bar", args={"arg1": "arg1"})]
+        ),
+        ToolMessage("foobar", tool_call_id="foo"),
+    ]
+    expected = 170
+    actual = llm.get_num_tokens_from_messages(messages)
+    assert expected == actual