How do you create custom tools?

There are three ways to create custom tools in LangChain, in order of increasing complexity: the @tool decorator, StructuredTool.from_function(), and subclassing BaseTool.

@tool decorator — simplest approach for single-string input tools:

from langchain_core.tools import tool

@tool
def get_word_count(text: str) -> int:
    """Counts the number of words in the provided text. Use when asked about word count."""
    return len(text.split())

# Tool name: 'get_word_count', description from docstring
print(get_word_count.invoke("Hello world"))  # 2

StructuredTool.from_function() — for tools with multiple inputs:

from langchain_core.tools import StructuredTool
from pydantic import BaseModel

class MultiplyInput(BaseModel):
    a: float
    b: float

def multiply(a: float, b: float) -> float:
    """Multiplies two numbers together."""
    return a * b

multiply_tool = StructuredTool.from_function(
    func=multiply,
    name="multiply",
    description="Multiplies two numbers together.",
    args_schema=MultiplyInput,
)

BaseTool subclass — for full control, async support, and complex logic:

from langchain_core.tools import BaseTool

class DatabaseQueryTool(BaseTool):
    name = "database_query"
    description = "Query the internal product database. Input should be a SQL WHERE clause."

    def _run(self, query: str) -> str:
        return db.execute(f"SELECT * FROM products WHERE {query}")

    async def _arun(self, query: str) -> str:
        return await db.async_execute(query)

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