feat(core): add 'approximate' alias in place of count_tokens_approximately (#33045)

### Description: 
earlier we have to use like below:
```python
from langchain_core.messages import trim_messages
from langchain_core.messages.utils import count_tokens_approximately

trim_messages(..., token_counter=count_tokens_approximately)
```
Now can be used as like this also
```python
from langchain_core.messages import trim_messages

trim_messages(..., token_counter="approximate")
```
- [x] **Added tests**
- [x] **Lint and test**: Run this as I made change in langchain/core, uv
run --group test pytest tests/unit_tests/messages/test_utils.py -v
<img width="1006" height="66" alt="image"
src="https://github.com/user-attachments/assets/c6938c29-a781-4e7f-871b-8e888ee764b7"
/>

---------

Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>

libs/core/langchain_core/messages/utils.py

@@ -720,7 +720,8 @@ def trim_messages(
     max_tokens: int,
     token_counter: Callable[[list[BaseMessage]], int]
     | Callable[[BaseMessage], int]
-    | BaseLanguageModel,
+    | BaseLanguageModel
+    | Literal["approximate"],
     strategy: Literal["first", "last"] = "last",
     allow_partial: bool = False,
     end_on: str | type[BaseMessage] | Sequence[str | type[BaseMessage]] | None = None,
@@ -758,53 +759,65 @@ def trim_messages(
         messages: Sequence of Message-like objects to trim.
         max_tokens: Max token count of trimmed messages.
         token_counter: Function or llm for counting tokens in a `BaseMessage` or a
-            list of `BaseMessage`. If a `BaseLanguageModel` is passed in then
+            list of `BaseMessage`.
-            `BaseLanguageModel.get_num_tokens_from_messages()` will be used.
+
-            Set to `len` to count the number of **messages** in the chat history.
+            If a `BaseLanguageModel` is passed in then
+            `BaseLanguageModel.get_num_tokens_from_messages()` will be used. Set to
+            `len` to count the number of **messages** in the chat history.
+
+            You can also use string shortcuts for convenience:
+
+            - `'approximate'`: Uses `count_tokens_approximately` for fast, approximate
+                token counts.
 
             !!! note
 
-                Use `count_tokens_approximately` to get fast, approximate token
+                `count_tokens_approximately` (or the shortcut `'approximate'`) is
-                counts.
+                recommended for using `trim_messages` on the hot path, where exact token
-
+                counting is not necessary.
-                This is recommended for using `trim_messages` on the hot path, where
-                exact token counting is not necessary.
 
         strategy: Strategy for trimming.
+
             - `'first'`: Keep the first `<= n_count` tokens of the messages.
             - `'last'`: Keep the last `<= n_count` tokens of the messages.
         allow_partial: Whether to split a message if only part of the message can be
-            included. If `strategy='last'` then the last partial contents of a message
+            included.
-            are included. If `strategy='first'` then the first partial contents of a
-            message are included.
-        end_on: The message type to end on. If specified then every message after the
-            last occurrence of this type is ignored. If `strategy='last'` then this
-            is done before we attempt to get the last `max_tokens`. If
-            `strategy='first'` then this is done after we get the first
-            `max_tokens`. Can be specified as string names (e.g. `'system'`,
-            `'human'`, `'ai'`, ...) or as `BaseMessage` classes (e.g.
-            `SystemMessage`, `HumanMessage`, `AIMessage`, ...). Can be a single
-            type or a list of types.
 
-        start_on: The message type to start on. Should only be specified if
+            If `strategy='last'` then the last partial contents of a message are
-            `strategy='last'`. If specified then every message before
+            included. If `strategy='first'` then the first partial contents of a
-            the first occurrence of this type is ignored. This is done after we trim
+            message are included.
-            the initial messages to the last `max_tokens`. Does not
+        end_on: The message type to end on.
-            apply to a `SystemMessage` at index 0 if `include_system=True`. Can be
+
-            specified as string names (e.g. `'system'`, `'human'`, `'ai'`, ...) or
+            If specified then every message after the last occurrence of this type is
-            as `BaseMessage` classes (e.g. `SystemMessage`, `HumanMessage`,
+            ignored. If `strategy='last'` then this is done before we attempt to get the
-            `AIMessage`, ...). Can be a single type or a list of types.
+            last `max_tokens`. If `strategy='first'` then this is done after we get the
+            first `max_tokens`. Can be specified as string names (e.g. `'system'`,
+            `'human'`, `'ai'`, ...) or as `BaseMessage` classes (e.g. `SystemMessage`,
+            `HumanMessage`, `AIMessage`, ...). Can be a single type or a list of types.
+
+        start_on: The message type to start on.
+
+            Should only be specified if `strategy='last'`. If specified then every
+            message before the first occurrence of this type is ignored. This is done
+            after we trim the initial messages to the last `max_tokens`. Does not apply
+            to a `SystemMessage` at index 0 if `include_system=True`. Can be specified
+            as string names (e.g. `'system'`, `'human'`, `'ai'`, ...) or as
+            `BaseMessage` classes (e.g. `SystemMessage`, `HumanMessage`, `AIMessage`,
+            ...). Can be a single type or a list of types.
 
         include_system: Whether to keep the `SystemMessage` if there is one at index
-            `0`. Should only be specified if `strategy="last"`.
+            `0`.
+
+            Should only be specified if `strategy="last"`.
         text_splitter: Function or `langchain_text_splitters.TextSplitter` for
-            splitting the string contents of a message. Only used if
+            splitting the string contents of a message.
-            `allow_partial=True`. If `strategy='last'` then the last split tokens
+
-            from a partial message will be included. if `strategy='first'` then the
+            Only used if `allow_partial=True`. If `strategy='last'` then the last split
-            first split tokens from a partial message will be included. Token splitter
+            tokens from a partial message will be included. if `strategy='first'` then
-            assumes that separators are kept, so that split contents can be directly
+            the first split tokens from a partial message will be included. Token
-            concatenated to recreate the original text. Defaults to splitting on
+            splitter assumes that separators are kept, so that split contents can be
-            newlines.
+            directly concatenated to recreate the original text. Defaults to splitting
+            on newlines.
 
     Returns:
         List of trimmed `BaseMessage`.
@@ -815,8 +828,8 @@ def trim_messages(
 
     Example:
         Trim chat history based on token count, keeping the `SystemMessage` if
-        present, and ensuring that the chat history starts with a `HumanMessage` (
+        present, and ensuring that the chat history starts with a `HumanMessage` (or a
-        or a `SystemMessage` followed by a `HumanMessage`).
+        `SystemMessage` followed by a `HumanMessage`).
 
         ```python
         from langchain_core.messages import (
@@ -869,8 +882,34 @@ def trim_messages(
         ]
         ```
 
+        Trim chat history using approximate token counting with `'approximate'`:
+
+        ```python
+        trim_messages(
+            messages,
+            max_tokens=45,
+            strategy="last",
+            # Using the "approximate" shortcut for fast token counting
+            token_counter="approximate",
+            start_on="human",
+            include_system=True,
+        )
+
+        # This is equivalent to using `count_tokens_approximately` directly
+        from langchain_core.messages.utils import count_tokens_approximately
+
+        trim_messages(
+            messages,
+            max_tokens=45,
+            strategy="last",
+            token_counter=count_tokens_approximately,
+            start_on="human",
+            include_system=True,
+        )
+        ```
+
         Trim chat history based on the message count, keeping the `SystemMessage` if
-        present, and ensuring that the chat history starts with a `HumanMessage` (
+        present, and ensuring that the chat history starts with a HumanMessage (
         or a `SystemMessage` followed by a `HumanMessage`).
 
             trim_messages(
@@ -992,24 +1031,44 @@ def trim_messages(
         raise ValueError(msg)
 
     messages = convert_to_messages(messages)
-    if hasattr(token_counter, "get_num_tokens_from_messages"):
+
-        list_token_counter = token_counter.get_num_tokens_from_messages
+    # Handle string shortcuts for token counter
-    elif callable(token_counter):
+    if isinstance(token_counter, str):
+        if token_counter in _TOKEN_COUNTER_SHORTCUTS:
+            actual_token_counter = _TOKEN_COUNTER_SHORTCUTS[token_counter]
+        else:
+            available_shortcuts = ", ".join(
+                f"'{key}'" for key in _TOKEN_COUNTER_SHORTCUTS
+            )
+            msg = (
+                f"Invalid token_counter shortcut '{token_counter}'. "
+                f"Available shortcuts: {available_shortcuts}."
+            )
+            raise ValueError(msg)
+    else:
+        # Type narrowing: at this point token_counter is not a str
+        actual_token_counter = token_counter  # type: ignore[assignment]
+
+    if hasattr(actual_token_counter, "get_num_tokens_from_messages"):
+        list_token_counter = actual_token_counter.get_num_tokens_from_messages
+    elif callable(actual_token_counter):
         if (
-            next(iter(inspect.signature(token_counter).parameters.values())).annotation
+            next(
+                iter(inspect.signature(actual_token_counter).parameters.values())
+            ).annotation
             is BaseMessage
         ):
 
             def list_token_counter(messages: Sequence[BaseMessage]) -> int:
-                return sum(token_counter(msg) for msg in messages)  # type: ignore[arg-type, misc]
+                return sum(actual_token_counter(msg) for msg in messages)  # type: ignore[arg-type, misc]
 
         else:
-            list_token_counter = token_counter
+            list_token_counter = actual_token_counter
     else:
         msg = (
             f"'token_counter' expected to be a model that implements "
             f"'get_num_tokens_from_messages()' or a function. Received object of type "
-            f"{type(token_counter)}."
+            f"{type(actual_token_counter)}."
         )
         raise ValueError(msg)
 
@@ -1807,3 +1866,14 @@ def count_tokens_approximately(
 
     # round up once more time in case extra_tokens_per_message is a float
     return math.ceil(token_count)
+
+
+# Mapping from string shortcuts to token counter functions
+def _approximate_token_counter(messages: Sequence[BaseMessage]) -> int:
+    """Wrapper for `count_tokens_approximately` that matches expected signature."""
+    return count_tokens_approximately(messages)
+
+
+_TOKEN_COUNTER_SHORTCUTS = {
+    "approximate": _approximate_token_counter,
+}

libs/core/tests/unit_tests/messages/test_utils.py

@@ -673,6 +673,82 @@ def test_trim_messages_start_on_with_allow_partial() -> None:
     assert messages == messages_copy
 
 
+def test_trim_messages_token_counter_shortcut_approximate() -> None:
+    """Test that `'approximate'` shortcut works for `token_counter`."""
+    messages = [
+        SystemMessage("This is a test message"),
+        HumanMessage("Another test message", id="first"),
+        AIMessage("AI response here", id="second"),
+    ]
+    messages_copy = [m.model_copy(deep=True) for m in messages]
+
+    # Test using the "approximate" shortcut
+    result_shortcut = trim_messages(
+        messages,
+        max_tokens=50,
+        token_counter="approximate",
+        strategy="last",
+    )
+
+    # Test using count_tokens_approximately directly
+    result_direct = trim_messages(
+        messages,
+        max_tokens=50,
+        token_counter=count_tokens_approximately,
+        strategy="last",
+    )
+
+    # Both should produce the same result
+    assert result_shortcut == result_direct
+    assert messages == messages_copy
+
+
+def test_trim_messages_token_counter_shortcut_invalid() -> None:
+    """Test that invalid `token_counter` shortcut raises `ValueError`."""
+    messages = [
+        SystemMessage("This is a test message"),
+        HumanMessage("Another test message"),
+    ]
+
+    # Test with invalid shortcut - intentionally passing invalid string to verify
+    # runtime error handling for dynamically-constructed inputs
+    with pytest.raises(ValueError, match="Invalid token_counter shortcut 'invalid'"):
+        trim_messages(  # type: ignore[call-overload]
+            messages,
+            max_tokens=50,
+            token_counter="invalid",
+            strategy="last",
+        )
+
+
+def test_trim_messages_token_counter_shortcut_with_options() -> None:
+    """Test that `'approximate'` shortcut works with different trim options."""
+    messages = [
+        SystemMessage("System instructions"),
+        HumanMessage("First human message", id="first"),
+        AIMessage("First AI response", id="ai1"),
+        HumanMessage("Second human message", id="second"),
+        AIMessage("Second AI response", id="ai2"),
+    ]
+    messages_copy = [m.model_copy(deep=True) for m in messages]
+
+    # Test with various options
+    result = trim_messages(
+        messages,
+        max_tokens=100,
+        token_counter="approximate",
+        strategy="last",
+        include_system=True,
+        start_on="human",
+    )
+
+    # Should include system message and start on human
+    assert len(result) >= 2
+    assert isinstance(result[0], SystemMessage)
+    assert any(isinstance(msg, HumanMessage) for msg in result[1:])
+    assert messages == messages_copy
+
+
 class FakeTokenCountingModel(FakeChatModel):
     @override
     def get_num_tokens_from_messages(