Tools

Tools are functions that agents and language models can invoke to interact with external systems, perform computations, or retrieve information. LangChain provides a flexible system for defining, validating, and executing tools.

Tool Basics

Tools in LangChain are defined using the @tool decorator or by extending BaseTool:

Simple Tool with Decorator

from langchain_core.tools import tool

@tool
def calculator(expression: str) -> str:
    """Evaluate a mathematical expression.
    
    Args:
        expression: A valid Python math expression (e.g., "2 + 2", "10 * 5")
    """
    try:
        result = eval(expression)
        return str(result)
    except Exception as e:
        return f"Error: {str(e)}"

# Use the tool
result = calculator.invoke({"expression": "25 * 4"})
print(result)  # "100"

The @tool decorator automatically:

Extracts the function name as tool name
Uses the docstring as tool description
Infers parameter schema from type hints
Creates a BaseTool subclass

See: /libs/core/langchain_core/tools/base.py

Tool with Pydantic Schema

For complex inputs, use Pydantic models:

from langchain_core.tools import tool
from pydantic import BaseModel, Field

class SearchInput(BaseModel):
    """Input schema for search tool."""
    query: str = Field(description="Search query string")
    max_results: int = Field(default=5, description="Maximum number of results", ge=1, le=20)
    include_snippets: bool = Field(default=True, description="Include text snippets")

@tool(args_schema=SearchInput)
def search(query: str, max_results: int = 5, include_snippets: bool = True) -> str:
    """Search for information on the web.
    
    Returns formatted search results with titles and URLs.
    """
    # Search implementation
    return f"Found {max_results} results for '{query}'"

# Tool automatically validates inputs
result = search.invoke({
    "query": "LangChain documentation",
    "max_results": 10
})

See schema creation in /libs/core/langchain_core/tools/base.py:289

BaseTool Class

For more control, extend BaseTool directly:

from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
from typing import Optional

class WeatherInput(BaseModel):
    location: str = Field(description="City name or ZIP code")
    units: str = Field(default="fahrenheit", description="Temperature units")

class WeatherTool(BaseTool):
    name: str = "get_weather"
    description: str = "Get current weather for a location"
    args_schema: type[BaseModel] = WeatherInput
    
    def _run(self, location: str, units: str = "fahrenheit") -> str:
        """Synchronous implementation."""
        # Call weather API
        return f"Weather in {location}: 72°{units[0].upper()}, Sunny"
    
    async def _arun(self, location: str, units: str = "fahrenheit") -> str:
        """Async implementation."""
        # Async API call
        return f"Weather in {location}: 72°{units[0].upper()}, Sunny"

tool = WeatherTool()
result = tool.invoke({"location": "San Francisco"})
print(result)  # "Weather in San Francisco: 72°F, Sunny"

Tool Properties

Every tool has these key properties:

name

Unique identifier for the tool:

@tool
def my_tool(x: int) -> int:
    """Double a number."""
    return x * 2

print(my_tool.name)  # "my_tool"

Override the name:

@tool("custom_name")
def my_tool(x: int) -> int:
    """Double a number."""
    return x * 2

print(my_tool.name)  # "custom_name"

description

Helps the LLM understand when to use the tool:

@tool
def search_docs(query: str) -> str:
    """Search LangChain documentation for relevant information.
    
    Use this when the user asks about:
    - LangChain features and capabilities
    - Code examples and tutorials
    - API reference and function signatures
    
    Do not use for:
    - General web search
    - Real-time information
    - Non-LangChain topics
    """
    return search_api(query)

print(search_docs.description)

Clear, detailed descriptions significantly improve agent performance. Include:

What the tool does
When to use it
When NOT to use it
Parameter requirements

args_schema

Pydantic model defining input validation:

from langchain_core.tools import tool
from pydantic import BaseModel, Field, field_validator

class CalculatorInput(BaseModel):
    expression: str = Field(
        description="Mathematical expression to evaluate",
        min_length=1,
        max_length=200
    )
    
    @field_validator("expression")
    @classmethod
    def validate_expression(cls, v: str) -> str:
        # Only allow safe math operations
        allowed = set("0123456789+-*/(). ")
        if not all(c in allowed for c in v):
            raise ValueError("Expression contains invalid characters")
        return v

@tool(args_schema=CalculatorInput)
def calculator(expression: str) -> str:
    """Evaluate math expressions."""
    return str(eval(expression))

# Schema used for validation
print(calculator.args_schema.model_json_schema())

See: /libs/core/langchain_core/tools/base.py:289

Tool Invocation

Tools are Runnables and support standard invocation methods:

invoke / ainvoke

@tool
def greet(name: str) -> str:
    """Greet someone by name."""
    return f"Hello, {name}!"

# Synchronous
result = greet.invoke({"name": "Alice"})
print(result)  # "Hello, Alice!"

# Asynchronous
result = await greet.ainvoke({"name": "Bob"})

batch / abatch

inputs = [
    {"expression": "2 + 2"},
    {"expression": "5 * 5"},
    {"expression": "10 - 3"}
]

results = calculator.batch(inputs)
print(results)  # ["4", "25", "7"]

With Configuration

from langchain_core.runnables import RunnableConfig

result = tool.invoke(
    {"query": "search term"},
    config=RunnableConfig(
        tags=["production"],
        metadata={"user_id": "123"}
    )
)

Tool Integration with Models

Binding Tools to Models

Modern chat models support native tool calling:

from langchain_openai import ChatOpenAI
from langchain_core.tools import tool

@tool
def get_weather(location: str) -> str:
    """Get current weather."""
    return f"Weather in {location}: Sunny, 72°F"

@tool
def get_time(timezone: str = "UTC") -> str:
    """Get current time."""
    from datetime import datetime
    return datetime.now().strftime("%H:%M:%S")

# Bind tools to model
model = ChatOpenAI(model="gpt-4")
model_with_tools = model.bind_tools([get_weather, get_time])

# Model can now request tool calls
response = model_with_tools.invoke("What's the weather in NYC?")
print(response.tool_calls)
# [{'name': 'get_weather', 'args': {'location': 'NYC'}, 'id': 'call_...'}]

Executing Tool Calls

Handle tool call responses:

from langchain_core.messages import HumanMessage, ToolMessage

messages = [HumanMessage(content="What's the weather in SF?")]

# Get AI response with tool calls
ai_message = model_with_tools.invoke(messages)
messages.append(ai_message)

# Execute each tool call
for tool_call in ai_message.tool_calls:
    tool_name = tool_call["name"]
    tool_args = tool_call["args"]
    
    # Find and invoke the tool
    if tool_name == "get_weather":
        result = get_weather.invoke(tool_args)
    elif tool_name == "get_time":
        result = get_time.invoke(tool_args)
    
    # Add tool result to messages
    messages.append(ToolMessage(
        content=result,
        tool_call_id=tool_call["id"]
    ))

# Get final response
final_response = model_with_tools.invoke(messages)
print(final_response.content)  # "The weather in SF is sunny and 72°F"

Tool Choice Control

Force or prevent tool usage:

# Force tool usage (must call a tool)
model_with_tools = model.bind_tools(
    [get_weather, get_time],
    tool_choice="required"
)

# Force specific tool
model_with_tools = model.bind_tools(
    [get_weather, get_time],
    tool_choice={"type": "tool", "name": "get_weather"}
)

# Disable tools (regular response)
model_with_tools = model.bind_tools(
    [get_weather, get_time],
    tool_choice="none"
)

Structured Tools

For tools that return structured data:

from langchain_core.tools import StructuredTool
from pydantic import BaseModel

class UserInfo(BaseModel):
    name: str
    email: str
    age: int

class GetUserInput(BaseModel):
    user_id: str

def get_user(user_id: str) -> UserInfo:
    # Database lookup
    return UserInfo(
        name="Alice",
        email="alice@example.com",
        age=30
    )

tool = StructuredTool.from_function(
    func=get_user,
    name="get_user",
    description="Retrieve user information by ID",
    args_schema=GetUserInput,
)

result = tool.invoke({"user_id": "123"})
print(result)  # UserInfo object

See: /libs/core/langchain_core/tools/structured.py

Tool Error Handling

Handle tool failures gracefully:

from langchain_core.tools import tool

@tool
def divide(a: float, b: float) -> str:
    """Divide two numbers.
    
    Args:
        a: Numerator
        b: Denominator (must be non-zero)
    """
    try:
        if b == 0:
            return "Error: Cannot divide by zero"
        result = a / b
        return f"Result: {result}"
    except Exception as e:
        return f"Error: {str(e)}"

# Tool returns error message instead of raising
result = divide.invoke({"a": 10, "b": 0})
print(result)  # "Error: Cannot divide by zero"

For tools that should raise exceptions:

@tool(handle_tool_error=True)
def risky_operation(value: int) -> str:
    """Operation that might fail."""
    if value < 0:
        raise ValueError("Value must be positive")
    return f"Result: {value * 2}"

# Exceptions are caught and returned as error messages
result = risky_operation.invoke({"value": -5})
print(result)  # "Error: Value must be positive"

Retriever Tools

Convert retrievers to tools:

from langchain_core.tools import create_retriever_tool
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings

# Create vector store retriever
vectorstore = FAISS.from_texts(
    ["LangChain is a framework", "Python is a language"],
    embedding=OpenAIEmbeddings()
)
retriever = vectorstore.as_retriever()

# Convert to tool
retriever_tool = create_retriever_tool(
    retriever,
    name="search_docs",
    description="Search documentation for relevant information"
)

# Use like any other tool
result = retriever_tool.invoke({"query": "What is LangChain?"})

See: /libs/core/langchain_core/tools/retriever.py

Tool Rendering

Convert tools to various formats:

OpenAI Format

from langchain_core.utils.function_calling import convert_to_openai_tool

@tool
def calculator(expression: str) -> str:
    """Evaluate math expressions."""
    return str(eval(expression))

openai_format = convert_to_openai_tool(calculator)
print(openai_format)
# {
#   "type": "function",
#   "function": {
#     "name": "calculator",
#     "description": "Evaluate math expressions.",
#     "parameters": {...}
#   }
# }

JSON Schema

schema = calculator.args_schema.model_json_schema()
print(schema)
# {
#   "type": "object",
#   "properties": {
#     "expression": {"type": "string", ...}
#   },
#   "required": ["expression"]
# }

Advanced Tool Patterns

Stateful Tools

Tools that maintain state:

from langchain_core.tools import BaseTool
from pydantic import Field

class Counter(BaseTool):
    name: str = "counter"
    description: str = "Increment and return a counter"
    count: int = Field(default=0)
    
    def _run(self) -> str:
        self.count += 1
        return f"Count: {self.count}"

counter = Counter()
print(counter.invoke({}))  # "Count: 1"
print(counter.invoke({}))  # "Count: 2"

Tool with Callbacks

from langchain_core.callbacks import CallbackManagerForToolRun

class VerboseTool(BaseTool):
    name: str = "verbose_tool"
    description: str = "A tool that logs its execution"
    
    def _run(
        self,
        query: str,
        run_manager: CallbackManagerForToolRun | None = None
    ) -> str:
        if run_manager:
            run_manager.on_text(f"Processing: {query}\n")
        
        result = f"Result for {query}"
        
        if run_manager:
            run_manager.on_text(f"Completed: {result}\n")
        
        return result

Tool Composition

Compose tools with other Runnables:

from langchain_core.runnables import RunnableLambda

@tool
def fetch_data(url: str) -> str:
    """Fetch data from URL."""
    return f"Data from {url}"

def parse_data(data: str) -> dict:
    return {"parsed": data.upper()}

# Compose tool with processing
chain = fetch_data | RunnableLambda(parse_data)
result = chain.invoke({"url": "https://example.com"})

Best Practices

Write clear tool descriptions

The description is critical for LLM tool selection:

@tool
def search(query: str) -> str:
    """Search for current information on the web.
    
    Use this tool when you need:
    - Recent news or events (last 24 hours)
    - Real-time data (stock prices, weather)
    - Information not in your training data
    - Verification of facts
    
    Do NOT use for:
    - General knowledge questions
    - Mathematical calculations
    - Code generation
    
    Args:
        query: Specific search terms. Be precise and use keywords.
    
    Returns:
        Top search results with titles, snippets, and URLs.
    """
    return search_api(query)

Validate inputs with Pydantic

Use Pydantic models for type safety:

from pydantic import BaseModel, Field, field_validator

class EmailInput(BaseModel):
    to: str = Field(description="Recipient email")
    subject: str = Field(min_length=1, max_length=200)
    body: str = Field(min_length=1)
    
    @field_validator("to")
    @classmethod
    def validate_email(cls, v: str) -> str:
        if "@" not in v:
            raise ValueError("Invalid email address")
        return v

@tool(args_schema=EmailInput)
def send_email(to: str, subject: str, body: str) -> str:
    """Send an email."""
    # Email sending logic
    return "Email sent"

Return user-friendly messages

Tool outputs should be clear and actionable:

@tool
def database_query(sql: str) -> str:
    """Execute SQL query."""
    try:
        results = db.execute(sql)
        if not results:
            return "Query executed successfully. No rows returned."
        return f"Found {len(results)} rows:\n{format_results(results)}"
    except Exception as e:
        return f"Query failed: {str(e)}\nPlease check the SQL syntax."

Keep tool scope focused

Single-purpose tools are easier for LLMs to use correctly:

# Good: Focused tools
@tool
def get_user_email(user_id: str) -> str:
    """Get user email address."""
    return db.get_user(user_id).email

@tool
def get_user_age(user_id: str) -> int:
    """Get user age."""
    return db.get_user(user_id).age

# Avoid: Kitchen sink tools
@tool
def get_user_data(user_id: str, field: str) -> Any:
    """Get any user field."""
    return getattr(db.get_user(user_id), field)

Next Steps

Agents

Build agents that use tools dynamically

Runnables

Compose tools with other components

Messages

Handle tool messages in conversations

LangGraph

Build advanced tool-using systems

Get Started

Core Concepts

Guides

Advanced

Tool Basics

Simple Tool with Decorator

Tool with Pydantic Schema

BaseTool Class

Tool Properties

name

description

args_schema

Tool Invocation

invoke / ainvoke

batch / abatch

With Configuration

Tool Integration with Models

Binding Tools to Models

Executing Tool Calls

Tool Choice Control

Structured Tools

Tool Error Handling

Retriever Tools

Tool Rendering

OpenAI Format

JSON Schema

Advanced Tool Patterns

Stateful Tools

Tool with Callbacks

Tool Composition

Best Practices

Next Steps

Agents

Runnables

Messages

LangGraph

Build docs developers (and LLMs) love

Get Started

Core Concepts

Guides

Advanced

Documentation Index

​Tool Basics

​Simple Tool with Decorator

​Tool with Pydantic Schema

​BaseTool Class

​Tool Properties

​name

​description

​args_schema

​Tool Invocation

​invoke / ainvoke

​batch / abatch

​With Configuration

​Tool Integration with Models

​Binding Tools to Models

​Executing Tool Calls

​Tool Choice Control

​Structured Tools

​Tool Error Handling

​Retriever Tools

​Tool Rendering

​OpenAI Format

​JSON Schema

​Advanced Tool Patterns

​Stateful Tools

​Tool with Callbacks

​Tool Composition

​Best Practices

​Next Steps

Agents

Runnables

Messages

LangGraph

Build docs developers (and LLMs) love

Tool Basics

Simple Tool with Decorator

Tool with Pydantic Schema

BaseTool Class

Tool Properties

name

description

args_schema

Tool Invocation

invoke / ainvoke

batch / abatch

With Configuration

Tool Integration with Models

Binding Tools to Models

Executing Tool Calls

Tool Choice Control

Structured Tools

Tool Error Handling

Retriever Tools

Tool Rendering

OpenAI Format

JSON Schema

Advanced Tool Patterns

Stateful Tools

Tool with Callbacks

Tool Composition

Best Practices

Next Steps