diff --git a/libs/community/langchain_community/chat_models/mlx.py b/libs/community/langchain_community/chat_models/mlx.py
index e3a28c73c7f..3cd687e87c2 100644
--- a/libs/community/langchain_community/chat_models/mlx.py
+++ b/libs/community/langchain_community/chat_models/mlx.py
@@ -1,11 +1,23 @@
 """MLX Chat Wrapper."""
 
-from typing import Any, Iterator, List, Optional
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Iterator,
+    List,
+    Literal,
+    Optional,
+    Sequence,
+    Type,
+    Union,
+)
 
 from langchain_core.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
+from langchain_core.language_models import LanguageModelInput
 from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_core.messages import (
     AIMessage,
@@ -20,6 +32,9 @@ from langchain_core.outputs import (
     ChatResult,
     LLMResult,
 )
+from langchain_core.runnables import Runnable
+from langchain_core.tools import BaseTool
+from langchain_core.utils.function_calling import convert_to_openai_tool
 
 from langchain_community.llms.mlx_pipeline import MLXPipeline
 
@@ -94,7 +109,6 @@ class ChatMLX(BaseChatModel):
             raise ValueError("Last message must be a HumanMessage!")
 
         messages_dicts = [self._to_chatml_format(m) for m in messages]
-
         return self.tokenizer.apply_chat_template(
             messages_dicts,
             tokenize=tokenize,
@@ -173,15 +187,18 @@ class ChatMLX(BaseChatModel):
             generate_step(
                 prompt_tokens,
                 self.llm.model,
-                temp,
-                repetition_penalty,
-                repetition_context_size,
+                temp=temp,
+                repetition_penalty=repetition_penalty,
+                repetition_context_size=repetition_context_size,
             ),
             range(max_new_tokens),
         ):
             # identify text to yield
             text: Optional[str] = None
-            text = self.tokenizer.decode(token.item())
+            if not isinstance(token, int):
+                text = self.tokenizer.decode(token.item())
+            else:
+                text = self.tokenizer.decode(token)
 
             # yield text, if any
             if text:
@@ -193,3 +210,59 @@ class ChatMLX(BaseChatModel):
             # break if stop sequence found
             if token == eos_token_id or (stop is not None and text in stop):
                 break
+
+    def bind_tools(
+        self,
+        tools: Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]],
+        *,
+        tool_choice: Optional[Union[dict, str, Literal["auto", "none"], bool]] = None,
+        **kwargs: Any,
+    ) -> Runnable[LanguageModelInput, BaseMessage]:
+        """Bind tool-like objects to this chat model.
+
+        Assumes model is compatible with OpenAI tool-calling API.
+
+        Args:
+            tools: A list of tool definitions to bind to this chat model.
+                Supports any tool definition handled by
+                :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`.
+            tool_choice: Which tool to require the model to call.
+                Must be the name of the single provided function or
+                "auto" to automatically determine which function to call
+                (if any), or a dict of the form:
+                {"type": "function", "function": {"name": <<tool_name>>}}.
+            **kwargs: Any additional parameters to pass to the
+                :class:`~langchain.runnable.Runnable` constructor.
+        """
+
+        formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
+        if tool_choice is not None and tool_choice:
+            if len(formatted_tools) != 1:
+                raise ValueError(
+                    "When specifying `tool_choice`, you must provide exactly one "
+                    f"tool. Received {len(formatted_tools)} tools."
+                )
+            if isinstance(tool_choice, str):
+                if tool_choice not in ("auto", "none"):
+                    tool_choice = {
+                        "type": "function",
+                        "function": {"name": tool_choice},
+                    }
+            elif isinstance(tool_choice, bool):
+                tool_choice = formatted_tools[0]
+            elif isinstance(tool_choice, dict):
+                if (
+                    formatted_tools[0]["function"]["name"]
+                    != tool_choice["function"]["name"]
+                ):
+                    raise ValueError(
+                        f"Tool choice {tool_choice} was specified, but the only "
+                        f"provided tool was {formatted_tools[0]['function']['name']}."
+                    )
+            else:
+                raise ValueError(
+                    f"Unrecognized tool_choice type. Expected str, bool or dict. "
+                    f"Received: {tool_choice}"
+                )
+            kwargs["tool_choice"] = tool_choice
+        return super().bind(tools=formatted_tools, **kwargs)