How to create custom tools

When constructing an agent, you will need to provide it with a list of Tools that it can use. Besides the actual function that is called, the Tool consists of several components:

Attribute	Type	Description
name	str	Must be unique within a set of tools provided to an LLM or agent.
description	str	Describes what the tool does. Used as context by the LLM or agent.
args_schema	Pydantic BaseModel	Optional but recommended, can be used to provide more information (e.g., few-shot examples) or validation for expected parameters
return_direct	boolean	Only relevant for agents. When True, after invoking the given tool, the agent will stop and return the result direcly to the user.

LangChain provides 3 ways to create tools:

1. Using @tool decorator -- the simplest way to define a custom tool.
2. Using StructuredTool.from_function class method -- this is similar to the @tool decorator, but allows more configuration and specification of both sync and async implementations.
3. By sub-classing from BaseTool -- This is the most flexible method, it provides the largest degree of control, at the expense of more effort and code.

The @tool or the StructuredTool.from_function class method should be sufficient for most use cases.

tip

Models will perform better if the tools have well chosen names, descriptions and JSON schemas.

@tool decorator

This @tool decorator is the simplest way to define a custom tool. The decorator uses the function name as the tool name by default, but this can be overridden by passing a string as the first argument. Additionally, the decorator will use the function's docstring as the tool's description - so a docstring MUST be provided.

from langchain_core.tools import tool


@tool
def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b


# Let's inspect some of the attributes associated with the tool.
print(multiply.name)
print(multiply.description)
print(multiply.args)

API Reference:tool

multiply
multiply(a: int, b: int) -> int - Multiply two numbers.
{'a': {'title': 'A', 'type': 'integer'}, 'b': {'title': 'B', 'type': 'integer'}}

Or create an async implementation, like this:

from langchain_core.tools import tool


@tool
async def amultiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b

API Reference:tool

You can also customize the tool name and JSON args by passing them into the tool decorator.

from langchain.pydantic_v1 import BaseModel, Field


class CalculatorInput(BaseModel):
    a: int = Field(description="first number")
    b: int = Field(description="second number")


@tool("multiplication-tool", args_schema=CalculatorInput, return_direct=True)
def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b


# Let's inspect some of the attributes associated with the tool.
print(multiply.name)
print(multiply.description)
print(multiply.args)
print(multiply.return_direct)

multiplication-tool
multiplication-tool(a: int, b: int) -> int - Multiply two numbers.
{'a': {'title': 'A', 'description': 'first number', 'type': 'integer'}, 'b': {'title': 'B', 'description': 'second number', 'type': 'integer'}}
True

StructuredTool

The StrurcturedTool.from_function class method provides a bit more configurability than the @tool decorator, without requiring much additional code.

from langchain_core.tools import StructuredTool


def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b


async def amultiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b


calculator = StructuredTool.from_function(func=multiply, coroutine=amultiply)

print(calculator.invoke({"a": 2, "b": 3}))
print(await calculator.ainvoke({"a": 2, "b": 5}))

API Reference:StructuredTool

6
10

To configure it:

class CalculatorInput(BaseModel):
    a: int = Field(description="first number")
    b: int = Field(description="second number")


def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b


calculator = StructuredTool.from_function(
    func=multiply,
    name="Calculator",
    description="multiply numbers",
    args_schema=CalculatorInput,
    return_direct=True,
    # coroutine= ... <- you can specify an async method if desired as well
)

print(calculator.invoke({"a": 2, "b": 3}))
print(calculator.name)
print(calculator.description)
print(calculator.args)

6
Calculator
Calculator(a: int, b: int) -> int - multiply numbers
{'a': {'title': 'A', 'description': 'first number', 'type': 'integer'}, 'b': {'title': 'B', 'description': 'second number', 'type': 'integer'}}

Subclass BaseTool

You can define a custom tool by sub-classing from BaseTool. This provides maximal control over the tool definition, but requires writing more code.

from typing import Optional, Type

from langchain.pydantic_v1 import BaseModel
from langchain_core.callbacks import (
    AsyncCallbackManagerForToolRun,
    CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool


class CalculatorInput(BaseModel):
    a: int = Field(description="first number")
    b: int = Field(description="second number")


class CustomCalculatorTool(BaseTool):
    name = "Calculator"
    description = "useful for when you need to answer questions about math"
    args_schema: Type[BaseModel] = CalculatorInput
    return_direct: bool = True

    def _run(
        self, a: int, b: int, run_manager: Optional[CallbackManagerForToolRun] = None
    ) -> str:
        """Use the tool."""
        return a * b

    async def _arun(
        self,
        a: int,
        b: int,
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool asynchronously."""
        # If the calculation is cheap, you can just delegate to the sync implementation
        # as shown below.
        # If the sync calculation is expensive, you should delete the entire _arun method.
        # LangChain will automatically provide a better implementation that will
        # kick off the task in a thread to make sure it doesn't block other async code.
        return self._run(a, b, run_manager=run_manager.get_sync())

API Reference:AsyncCallbackManagerForToolRun | CallbackManagerForToolRun | BaseTool

multiply = CustomCalculatorTool()
print(multiply.name)
print(multiply.description)
print(multiply.args)
print(multiply.return_direct)

print(multiply.invoke({"a": 2, "b": 3}))
print(await multiply.ainvoke({"a": 2, "b": 3}))

Calculator
useful for when you need to answer questions about math
{'a': {'title': 'A', 'description': 'first number', 'type': 'integer'}, 'b': {'title': 'B', 'description': 'second number', 'type': 'integer'}}
True
6
6

How to create async tools

LangChain Tools implement the Runnable interface 🏃.

All Runnables expose the invoke and ainvoke methods (as well as other methods like batch, abatch, astream etc).

So even if you only provide an sync implementation of a tool, you could still use the ainvoke interface, but there are some important things to know:

• LangChain's by default provides an async implementation that assumes that the function is expensive to compute, so it'll delegate execution to another thread.
• If you're working in an async codebase, you should create async tools rather than sync tools, to avoid incuring a small overhead due to that thread.
• If you need both sync and async implementations, use StructuredTool.from_function or sub-class from BaseTool.
• If implementing both sync and async, and the sync code is fast to run, override the default LangChain async implementation and simply call the sync code.
• You CANNOT and SHOULD NOT use the sync invoke with an async tool.

from langchain_core.tools import StructuredTool


def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b


calculator = StructuredTool.from_function(func=multiply)

print(calculator.invoke({"a": 2, "b": 3}))
print(
    await calculator.ainvoke({"a": 2, "b": 5})
)  # Uses default LangChain async implementation incurs small overhead

API Reference:StructuredTool

6
10

from langchain_core.tools import StructuredTool


def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b


async def amultiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b


calculator = StructuredTool.from_function(func=multiply, coroutine=amultiply)

print(calculator.invoke({"a": 2, "b": 3}))
print(
    await calculator.ainvoke({"a": 2, "b": 5})
)  # Uses use provided amultiply without additional overhead

API Reference:StructuredTool

6
10

You should not and cannot use .invoke when providing only an async definition.

@tool
async def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b


try:
    multiply.invoke({"a": 2, "b": 3})
except NotImplementedError:
    print("Raised not implemented error. You should not be doing this.")

Raised not implemented error. You should not be doing this.

Handling Tool Errors

If you're using tools with agents, you will likely need an error handling strategy, so the agent can recover from the error and continue execution.

A simple strategy is to throw a ToolException from inside the tool and specify an error handler using handle_tool_error.

When the error handler is specified, the exception will be caught and the error handler will decide which output to return from the tool.

You can set handle_tool_error to True, a string value, or a function. If it's a function, the function should take a ToolException as a parameter and return a value.

Please note that only raising a ToolException won't be effective. You need to first set the handle_tool_error of the tool because its default value is False.

from langchain_core.tools import ToolException


def get_weather(city: str) -> int:
    """Get weather for the given city."""
    raise ToolException(f"Error: There is no city by the name of {city}.")

API Reference:ToolException

Here's an example with the default handle_tool_error=True behavior.

get_weather_tool = StructuredTool.from_function(
    func=get_weather,
    handle_tool_error=True,
)

get_weather_tool.invoke({"city": "foobar"})

'Error: There is no city by the name of foobar.'

We can set handle_tool_error to a string that will always be returned.

get_weather_tool = StructuredTool.from_function(
    func=get_weather,
    handle_tool_error="There is no such city, but it's probably above 0K there!",
)

get_weather_tool.invoke({"city": "foobar"})

"There is no such city, but it's probably above 0K there!"

Handling the error using a function:

def _handle_error(error: ToolException) -> str:
    return f"The following errors occurred during tool execution: `{error.args[0]}`"


get_weather_tool = StructuredTool.from_function(
    func=get_weather,
    handle_tool_error=_handle_error,
)

get_weather_tool.invoke({"city": "foobar"})

'The following errors occurred during tool execution: `Error: There is no city by the name of foobar.`'