Tools and bind_tools

A tool is a Python function a model can call. @tool turns a function into one, and model.bind_tools([...]) returns a new model that is allowed to request it.

Last updated: 27 Sep, 2026 · LangChain 1.4

A model on its own can only write text. A tool lets it look something up or take an action. The model does not run the tool; it asks for it, and your program runs it.

The @tool decorator and bind_tools

from langchain.tools import tool

@tool
def name(arg: str) -> str:
    """What the tool does (the model reads this)."""
    return result

model_with_tools = model.bind_tools([name])   # returns a NEW model

Turning a function into a tool

Start by turning a plain function into a tool. The @tool decorator reads the type hints for the input schema and the docstring for the description the model sees.

from langchain.tools import tool

@tool
def lookup_order(order_id: str) -> str:
    """Look up the status of an order by its id."""   # the model reads this line
    return f"Order {order_id}: shipped on 3 March."

Calling the tool directly

Run the tool yourself with .invoke, passing its arguments as a dict. The model would ask for it, but here you call it directly to see what it returns.

print(lookup_order.invoke({"order_id": "A17"}))   # call the tool with its argument

The tool in a run

The tool and the call together:

from langchain.tools import tool

@tool
def lookup_order(order_id: str) -> str:
    """Look up the status of an order by its id."""
    return f"Order {order_id}: shipped on 3 March."

print(lookup_order.invoke({"order_id": "A17"}))

What @tool adds to a function

• The type hints are required: they define the tool's input schema.
• The docstring becomes the tool's description, which the model reads to decide when to call it.
• bind_tools returns a NEW model that knows about the tool; the original model is unchanged.

Plain function vs @tool

Where tools fit

• Any action an agent takes: search, look up an order, send an email, run a query.
• You give the model the tools; it chooses which to call and with what arguments.

Related

• Previous: Chat models: init_chat_model
• Next: ToolNode and tools_condition
• Reference: Tools