Call tools

Tools encapsulate a callable function and its input schema. These can be passed to compatible chat models, allowing the model to decide whether to invoke a tool and determine the appropriate arguments. You can define your own tools or use prebuilt tools

Define a tool

Define a basic tool with the @tool decorator:

from langchain_core.tools import tool

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

Run a tool

Tools conform to the Runnable interface, which means you can run a tool using the invoke method:

multiply.invoke({"a": 6, "b": 7})  # returns 42

If the tool is invoked with type="tool_call", it will return a ToolMessage:

tool_call = {
    "type": "tool_call",
    "id": "1",
    "args": {"a": 42, "b": 7}
}
multiply.invoke(tool_call) # returns a ToolMessage object

Output:

ToolMessage(content='294', name='multiply', tool_call_id='1')

Use in an agent

To create a tool-calling agent, you can use the prebuilt create_react_agent:

from langchain_core.tools import tool
# highlight-next-line
from langgraph.prebuilt import create_react_agent

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

# highlight-next-line
agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet",
    tools=[multiply]
)
agent.invoke({"messages": [{"role": "user", "content": "what's 42 x 7?"}]})

Dynamically select tools

Configure tool availability at runtime based on context:

from dataclasses import dataclass
from typing import Literal

from langchain.chat_models import init_chat_model
from langchain_core.tools import tool

from langgraph.prebuilt import create_react_agent
from langgraph.prebuilt.chat_agent_executor import AgentState
from langgraph.runtime import Runtime


@dataclass
class CustomContext:
    tools: list[Literal["weather", "compass"]]


@tool
def weather() -> str:
    """Returns the current weather conditions."""
    return "It's nice and sunny."


@tool
def compass() -> str:
    """Returns the direction the user is facing."""
    return "North"

model = init_chat_model("anthropic:claude-sonnet-4-20250514")

# highlight-next-line
def configure_model(state: AgentState, runtime: Runtime[CustomContext]):
    """Configure the model with tools based on runtime context."""
    selected_tools = [
        tool
        for tool in [weather, compass]
        if tool.name in runtime.context.tools
    ]
    return model.bind_tools(selected_tools)


agent = create_react_agent(
    # Dynamically configure the model with tools based on runtime context
    # highlight-next-line
    configure_model,
    # Initialize with all tools available
    # highlight-next-line
    tools=[weather, compass]
)

output = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "Who are you and what tools do you have access to?",
            }
        ]
    },
    # highlight-next-line
    context=CustomContext(tools=["weather"]),  # Only enable the weather tool
)

print(output["messages"][-1].text())

New in langgraph>=0.6

Use in a workflow

If you are writing a custom workflow, you will need to:

register the tools with the chat model
call the tool if the model decides to use it

Use model.bind_tools() to register the tools with the model.

from langchain.chat_models import init_chat_model

model = init_chat_model(model="claude-3-5-haiku-latest")

# highlight-next-line
model_with_tools = model.bind_tools([multiply])

LLMs automatically determine if a tool invocation is necessary and handle calling the tool with the appropriate arguments.

Extended example: attach tools to a chat model

from langchain_core.tools import tool
from langchain.chat_models import init_chat_model

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

model = init_chat_model(model="claude-3-5-haiku-latest")
# highlight-next-line
model_with_tools = model.bind_tools([multiply])

response_message = model_with_tools.invoke("what's 42 x 7?")
tool_call = response_message.tool_calls[0]

multiply.invoke(tool_call)

ToolMessage(
    content='294',
    name='multiply',
    tool_call_id='toolu_0176DV4YKSD8FndkeuuLj36c'
)

ToolNode

To execute tools in custom workflows, use the prebuilt ToolNode or implement your own custom node. ToolNode is a specialized node for executing tools in a workflow. It provides the following features:

Supports both synchronous and asynchronous tools.
Executes multiple tools concurrently.
Handles errors during tool execution (handle_tool_errors=True, enabled by default). See handling tool errors for more details.

ToolNode operates on MessagesState:

Input: MessagesState, where the last message is an AIMessage containing the tool_calls parameter.
Output: MessagesState updated with the resulting ToolMessage from executed tools.

# highlight-next-line
from langgraph.prebuilt import ToolNode

def get_weather(location: str):
    """Call to get the current weather."""
    if location.lower() in ["sf", "san francisco"]:
        return "It's 60 degrees and foggy."
    else:
        return "It's 90 degrees and sunny."

def get_coolest_cities():
    """Get a list of coolest cities"""
    return "nyc, sf"

# highlight-next-line
tool_node = ToolNode([get_weather, get_coolest_cities])
tool_node.invoke({"messages": [...]})

Single tool call

from langchain_core.messages import AIMessage
from langgraph.prebuilt import ToolNode

# Define tools
@tool
def get_weather(location: str):
    """Call to get the current weather."""
    if location.lower() in ["sf", "san francisco"]:
        return "It's 60 degrees and foggy."
    else:
        return "It's 90 degrees and sunny."

# highlight-next-line
tool_node = ToolNode([get_weather])

message_with_single_tool_call = AIMessage(
    content="",
    tool_calls=[
        {
            "name": "get_weather",
            "args": {"location": "sf"},
            "id": "tool_call_id",
            "type": "tool_call",
        }
    ],
)

tool_node.invoke({"messages": [message_with_single_tool_call]})

{'messages': [ToolMessage(content="It's 60 degrees and foggy.", name='get_weather', tool_call_id='tool_call_id')]}

Multiple tool calls

from langchain_core.messages import AIMessage
from langgraph.prebuilt import ToolNode

# Define tools

def get_weather(location: str):
    """Call to get the current weather."""
    if location.lower() in ["sf", "san francisco"]:
        return "It's 60 degrees and foggy."
    else:
        return "It's 90 degrees and sunny."

def get_coolest_cities():
    """Get a list of coolest cities"""
    return "nyc, sf"

# highlight-next-line
tool_node = ToolNode([get_weather, get_coolest_cities])

message_with_multiple_tool_calls = AIMessage(
    content="",
    tool_calls=[
        {
            "name": "get_coolest_cities",
            "args": {},
            "id": "tool_call_id_1",
            "type": "tool_call",
        },
        {
            "name": "get_weather",
            "args": {"location": "sf"},
            "id": "tool_call_id_2",
            "type": "tool_call",
        },
    ],
)

# highlight-next-line
tool_node.invoke({"messages": [message_with_multiple_tool_calls]})  # (1)!

ToolNode will execute both tools in parallel

{
    'messages': [
        ToolMessage(content='nyc, sf', name='get_coolest_cities', tool_call_id='tool_call_id_1'),
        ToolMessage(content="It's 60 degrees and foggy.", name='get_weather', tool_call_id='tool_call_id_2')
    ]
}

Use with a chat model

from langchain.chat_models import init_chat_model
from langgraph.prebuilt import ToolNode

def get_weather(location: str):
    """Call to get the current weather."""
    if location.lower() in ["sf", "san francisco"]:
        return "It's 60 degrees and foggy."
    else:
        return "It's 90 degrees and sunny."

# highlight-next-line
tool_node = ToolNode([get_weather])

model = init_chat_model(model="claude-3-5-haiku-latest")
# highlight-next-line
model_with_tools = model.bind_tools([get_weather])  # (1)!


# highlight-next-line
response_message = model_with_tools.invoke("what's the weather in sf?")
tool_node.invoke({"messages": [response_message]})

Use .bind_tools() to attach the tool schema to the chat model

{'messages': [ToolMessage(content="It's 60 degrees and foggy.", name='get_weather', tool_call_id='toolu_01Pnkgw5JeTRxXAU7tyHT4UW')]}

Use in a tool-calling agent

This is an example of creating a tool-calling agent from scratch using ToolNode. You can also use LangGraph’s prebuilt agent.

from langchain.chat_models import init_chat_model
from langgraph.prebuilt import ToolNode
from langgraph.graph import StateGraph, MessagesState, START, END

def get_weather(location: str):
    """Call to get the current weather."""
    if location.lower() in ["sf", "san francisco"]:
        return "It's 60 degrees and foggy."
    else:
        return "It's 90 degrees and sunny."

# highlight-next-line
tool_node = ToolNode([get_weather])

model = init_chat_model(model="claude-3-5-haiku-latest")
# highlight-next-line
model_with_tools = model.bind_tools([get_weather])

def should_continue(state: MessagesState):
    messages = state["messages"]
    last_message = messages[-1]
    if last_message.tool_calls:
        return "tools"
    return END

def call_model(state: MessagesState):
    messages = state["messages"]
    response = model_with_tools.invoke(messages)
    return {"messages": [response]}

builder = StateGraph(MessagesState)

# Define the two nodes we will cycle between
builder.add_node("call_model", call_model)
# highlight-next-line
builder.add_node("tools", tool_node)

builder.add_edge(START, "call_model")
builder.add_conditional_edges("call_model", should_continue, ["tools", END])
builder.add_edge("tools", "call_model")

graph = builder.compile()

graph.invoke({"messages": [{"role": "user", "content": "what's the weather in sf?"}]})

{
    'messages': [
        HumanMessage(content="what's the weather in sf?"),
        AIMessage(
            content=[{'text': "I'll help you check the weather in San Francisco right now.", 'type': 'text'}, {'id': 'toolu_01A4vwUEgBKxfFVc5H3v1CNs', 'input': {'location': 'San Francisco'}, 'name': 'get_weather', 'type': 'tool_use'}],
            tool_calls=[{'name': 'get_weather', 'args': {'location': 'San Francisco'}, 'id': 'toolu_01A4vwUEgBKxfFVc5H3v1CNs', 'type': 'tool_call'}]
        ),
        ToolMessage(content="It's 60 degrees and foggy."),
        AIMessage(content="The current weather in San Francisco is 60 degrees and foggy. Typical San Francisco weather with its famous marine layer!")
    ]
}

Tool customization

For more control over tool behavior, use the @tool decorator.

Parameter descriptions

Auto-generate descriptions from docstrings:

# highlight-next-line
from langchain_core.tools import tool

# highlight-next-line
@tool("multiply_tool", parse_docstring=True)
def multiply(a: int, b: int) -> int:
    """Multiply two numbers.

    Args:
        a: First operand
        b: Second operand
    """
    return a * b

Explicit input schema

Define schemas using args_schema:

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

class MultiplyInputSchema(BaseModel):
    """Multiply two numbers"""
    a: int = Field(description="First operand")
    b: int = Field(description="Second operand")

# highlight-next-line
@tool("multiply_tool", args_schema=MultiplyInputSchema)
def multiply(a: int, b: int) -> int:
    return a * b

Tool name

Override the default tool name using the first argument or name property:

from langchain_core.tools import tool

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

Context management

Tools within LangGraph sometimes require context data, such as runtime-only arguments (e.g., user IDs or session details), that should not be controlled by the model. LangGraph provides three methods for managing such context:

Type	Usage Scenario	Mutable	Lifetime
Configuration	Static, immutable runtime data	❌	Single invocation
Short-term memory	Dynamic, changing data during invocation	✅	Single invocation
Long-term memory	Persistent, cross-session data	✅	Across multiple sessions

Configuration

Use configuration when you have immutable runtime data that tools require, such as user identifiers. You pass these arguments via RunnableConfig at invocation and access them in the tool:

from langchain_core.tools import tool
from langchain_core.runnables import RunnableConfig

@tool
# highlight-next-line
def get_user_info(config: RunnableConfig) -> str:
    """Retrieve user information based on user ID."""
    user_id = config["configurable"].get("user_id")
    return "User is John Smith" if user_id == "user_123" else "Unknown user"

# Invocation example with an agent
agent.invoke(
    {"messages": [{"role": "user", "content": "look up user info"}]},
    # highlight-next-line
    config={"configurable": {"user_id": "user_123"}}
)

Extended example: Access config in tools

from langchain_core.runnables import RunnableConfig
from langchain_core.tools import tool
from langgraph.prebuilt import create_react_agent

def get_user_info(
    # highlight-next-line
    config: RunnableConfig,
) -> str:
    """Look up user info."""
    # highlight-next-line
    user_id = config["configurable"].get("user_id")
    return "User is John Smith" if user_id == "user_123" else "Unknown user"

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[get_user_info],
)

agent.invoke(
    {"messages": [{"role": "user", "content": "look up user information"}]},
    # highlight-next-line
    config={"configurable": {"user_id": "user_123"}}
)

Short-term memory

Short-term memory maintains dynamic state that changes during a single execution. To access (read) the graph state inside the tools, you can use a special parameter annotation — InjectedState:

from typing import Annotated, NotRequired
from langchain_core.tools import tool
from langgraph.prebuilt import InjectedState, create_react_agent
from langgraph.prebuilt.chat_agent_executor import AgentState

class CustomState(AgentState):
    # The user_name field in short-term state
    user_name: NotRequired[str]

@tool
def get_user_name(
    # highlight-next-line
    state: Annotated[CustomState, InjectedState]
) -> str:
    """Retrieve the current user-name from state."""
    # Return stored name or a default if not set
    return state.get("user_name", "Unknown user")

# Example agent setup
agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[get_user_name],
    state_schema=CustomState,
)

# Invocation: reads the name from state (initially empty)
agent.invoke({"messages": "what's my name?"})

Use a tool that returns a Command to update user_name and append a confirmation message:

from typing import Annotated
from langgraph.types import Command
from langchain_core.messages import ToolMessage
from langchain_core.tools import tool, InjectedToolCallId

@tool
def update_user_name(
    new_name: str,
    tool_call_id: Annotated[str, InjectedToolCallId]
) -> Command:
    """Update user-name in short-term memory."""
    # highlight-next-line
    return Command(update={
        # highlight-next-line
        "user_name": new_name,
        # highlight-next-line
        "messages": [
            # highlight-next-line
            ToolMessage(f"Updated user name to {new_name}", tool_call_id=tool_call_id)
            # highlight-next-line
        ]
        # highlight-next-line
    })

If you want to use tools that return Command and update graph state, you can either use prebuilt create_react_agent / ToolNode components, or implement your own tool-executing node that collects Command objects returned by the tools and returns a list of them, e.g.:

def call_tools(state):
    ...
    commands = [tools_by_name[tool_call["name"]].invoke(tool_call) for tool_call in tool_calls]
    return commands

Long-term memory

Use long-term memory to store user-specific or application-specific data across conversations. This is useful for applications like chatbots, where you want to remember user preferences or other information. To use long-term memory, you need to:

Configure a store to persist data across invocations.
Access the store from within tools.

To access information in the store:

from langchain_core.runnables import RunnableConfig
from langchain_core.tools import tool
from langgraph.graph import StateGraph
# highlight-next-line
from langgraph.config import get_store

@tool
def get_user_info(config: RunnableConfig) -> str:
    """Look up user info."""
    # Same as that provided to `builder.compile(store=store)`
    # or `create_react_agent`
    # highlight-next-line
    store = get_store()
    user_id = config["configurable"].get("user_id")
    # highlight-next-line
    user_info = store.get(("users",), user_id)
    return str(user_info.value) if user_info else "Unknown user"

builder = StateGraph(...)
...
graph = builder.compile(store=store)

Access long-term memory

from langchain_core.runnables import RunnableConfig
from langchain_core.tools import tool
from langgraph.config import get_store
from langgraph.prebuilt import create_react_agent
from langgraph.store.memory import InMemoryStore

# highlight-next-line
store = InMemoryStore() # (1)!

# highlight-next-line
store.put(  # (2)!
    ("users",),  # (3)!
    "user_123",  # (4)!
    {
        "name": "John Smith",
        "language": "English",
    } # (5)!
)

@tool
def get_user_info(config: RunnableConfig) -> str:
    """Look up user info."""
    # Same as that provided to `create_react_agent`
    # highlight-next-line
    store = get_store() # (6)!
    user_id = config["configurable"].get("user_id")
    # highlight-next-line
    user_info = store.get(("users",), user_id) # (7)!
    return str(user_info.value) if user_info else "Unknown user"

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[get_user_info],
    # highlight-next-line
    store=store # (8)!
)

# Run the agent
agent.invoke(
    {"messages": [{"role": "user", "content": "look up user information"}]},
    # highlight-next-line
    config={"configurable": {"user_id": "user_123"}}
)

The InMemoryStore is a store that stores data in memory. In a production setting, you would typically use a database or other persistent storage. Please review the store documentation for more options. If you’re deploying with LangGraph Platform, the platform will provide a production-ready store for you.
For this example, we write some sample data to the store using the put method. Please see the BaseStore.put API reference for more details.
The first argument is the namespace. This is used to group related data together. In this case, we are using the users namespace to group user data.
A key within the namespace. This example uses a user ID for the key.
The data that we want to store for the given user.
The get_store function is used to access the store. You can call it from anywhere in your code, including tools and prompts. This function returns the store that was passed to the agent when it was created.
The get method is used to retrieve data from the store. The first argument is the namespace, and the second argument is the key. This will return a StoreValue object, which contains the value and metadata about the value.
The store is passed to the agent. This enables the agent to access the store when running tools. You can also use the get_store function to access the store from anywhere in your code.

To update information in the store:

from langchain_core.runnables import RunnableConfig
from langchain_core.tools import tool
from langgraph.graph import StateGraph
# highlight-next-line
from langgraph.config import get_store

@tool
def save_user_info(user_info: str, config: RunnableConfig) -> str:
    """Save user info."""
    # Same as that provided to `builder.compile(store=store)`
    # or `create_react_agent`
    # highlight-next-line
    store = get_store()
    user_id = config["configurable"].get("user_id")
    # highlight-next-line
    store.put(("users",), user_id, user_info)
    return "Successfully saved user info."

builder = StateGraph(...)
...
graph = builder.compile(store=store)

Update long-term memory

from typing_extensions import TypedDict

from langchain_core.tools import tool
from langgraph.config import get_store
from langchain_core.runnables import RunnableConfig
from langgraph.prebuilt import create_react_agent
from langgraph.store.memory import InMemoryStore

store = InMemoryStore() # (1)!

class UserInfo(TypedDict): # (2)!
    name: str

@tool
def save_user_info(user_info: UserInfo, config: RunnableConfig) -> str: # (3)!
    """Save user info."""
    # Same as that provided to `create_react_agent`
    # highlight-next-line
    store = get_store() # (4)!
    user_id = config["configurable"].get("user_id")
    # highlight-next-line
    store.put(("users",), user_id, user_info) # (5)!
    return "Successfully saved user info."

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[save_user_info],
    # highlight-next-line
    store=store
)

# Run the agent
agent.invoke(
    {"messages": [{"role": "user", "content": "My name is John Smith"}]},
    # highlight-next-line
    config={"configurable": {"user_id": "user_123"}} # (6)!
)

# You can access the store directly to get the value
store.get(("users",), "user_123").value

The InMemoryStore is a store that stores data in memory. In a production setting, you would typically use a database or other persistent storage. Please review the store documentation for more options. If you’re deploying with LangGraph Platform, the platform will provide a production-ready store for you.
The UserInfo class is a TypedDict that defines the structure of the user information. The LLM will use this to format the response according to the schema.
The save_user_info function is a tool that allows an agent to update user information. This could be useful for a chat application where the user wants to update their profile information.
The get_store function is used to access the store. You can call it from anywhere in your code, including tools and prompts. This function returns the store that was passed to the agent when it was created.
The put method is used to store data in the store. The first argument is the namespace, and the second argument is the key. This will store the user information in the store.
The user_id is passed in the config. This is used to identify the user whose information is being updated.

Advanced tool features

Immediate return

Use return_direct=True to immediately return a tool’s result without executing additional logic. This is useful for tools that should not trigger further processing or tool calls, allowing you to return results directly to the user.

# highlight-next-line
@tool(return_direct=True)
def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b

Extended example: Using return_direct in a prebuilt agent

from langchain_core.tools import tool
from langgraph.prebuilt import create_react_agent

# highlight-next-line
@tool(return_direct=True)
def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[add]
)

agent.invoke(
    {"messages": [{"role": "user", "content": "what's 3 + 5?"}]}
)

Using without prebuilt components If you are building a custom workflow and are not relying on create_react_agent or ToolNode, you will also need to implement the control flow to handle return_direct=True.

Force tool use

If you need to force a specific tool to be used, you will need to configure this at the model level using the tool_choice parameter in the bind_tools method. Force specific tool usage via tool_choice:

@tool(return_direct=True)
def greet(user_name: str) -> int:
    """Greet user."""
    return f"Hello {user_name}!"

tools = [greet]

configured_model = model.bind_tools(
    tools,
    # Force the use of the 'greet' tool
    # highlight-next-line
    tool_choice={"type": "tool", "name": "greet"}
)

Extended example: Force tool usage in an agent

To force the agent to use specific tools, you can set the tool_choice option in model.bind_tools():

from langchain_core.tools import tool

# highlight-next-line
@tool(return_direct=True)
def greet(user_name: str) -> int:
    """Greet user."""
    return f"Hello {user_name}!"

tools = [greet]

agent = create_react_agent(
    # highlight-next-line
    model=model.bind_tools(tools, tool_choice={"type": "tool", "name": "greet"}),
    tools=tools
)

agent.invoke(
    {"messages": [{"role": "user", "content": "Hi, I am Bob"}]}
)

Avoid infinite loops Forcing tool usage without stopping conditions can create infinite loops. Use one of the following safeguards:

Mark the tool with return_direct=True to end the loop after execution.
Set recursion_limit to restrict the number of execution steps.

Tool choice configuration The tool_choice parameter is used to configure which tool should be used by the model when it decides to call a tool. This is useful when you want to ensure that a specific tool is always called for a particular task or when you want to override the model’s default behavior of choosing a tool based on its internal logic.Note that not all models support this feature, and the exact configuration may vary depending on the model you are using.

Disable parallel calls

For supported providers, you can disable parallel tool calling by setting parallel_tool_calls=False via the model.bind_tools() method:

model.bind_tools(
    tools,
    # highlight-next-line
    parallel_tool_calls=False
)

Extended example: disable parallel tool calls in a prebuilt agent

from langchain.chat_models import init_chat_model

def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b

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

model = init_chat_model("anthropic:claude-3-5-sonnet-latest", temperature=0)
tools = [add, multiply]
agent = create_react_agent(
    # disable parallel tool calls
    # highlight-next-line
    model=model.bind_tools(tools, parallel_tool_calls=False),
    tools=tools
)

agent.invoke(
    {"messages": [{"role": "user", "content": "what's 3 + 5 and 4 * 7?"}]}
)

Handle errors

LangGraph provides built-in error handling for tool execution through the prebuilt ToolNode component, used both independently and in prebuilt agents. By default, ToolNode catches exceptions raised during tool execution and returns them as ToolMessage objects with a status indicating an error.

from langchain_core.messages import AIMessage
from langgraph.prebuilt import ToolNode

def multiply(a: int, b: int) -> int:
    if a == 42:
        raise ValueError("The ultimate error")
    return a * b

# Default error handling (enabled by default)
tool_node = ToolNode([multiply])

message = AIMessage(
    content="",
    tool_calls=[{
        "name": "multiply",
        "args": {"a": 42, "b": 7},
        "id": "tool_call_id",
        "type": "tool_call"
    }]
)

result = tool_node.invoke({"messages": [message]})

Output:

{'messages': [
    ToolMessage(
        content="Error: ValueError('The ultimate error')\n Please fix your mistakes.",
        name='multiply',
        tool_call_id='tool_call_id',
        status='error'
    )
]}

Disable error handling

To propagate exceptions directly, disable error handling:

tool_node = ToolNode([multiply], handle_tool_errors=False)

With error handling disabled, exceptions raised by tools will propagate up, requiring explicit management.

Custom error messages

Provide a custom error message by setting the error handling parameter to a string:

tool_node = ToolNode(
    [multiply],
    handle_tool_errors="Can't use 42 as the first operand, please switch operands!"
)

Example output:

{'messages': [
    ToolMessage(
        content="Can't use 42 as the first operand, please switch operands!",
        name='multiply',
        tool_call_id='tool_call_id',
        status='error'
    )
]}

Error handling in agents

Error handling in prebuilt agents (create_react_agent) leverages ToolNode:

from langgraph.prebuilt import create_react_agent

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[multiply]
)

# Default error handling
agent.invoke({"messages": [{"role": "user", "content": "what's 42 x 7?"}]})

To disable or customize error handling in prebuilt agents, explicitly pass a configured ToolNode:

custom_tool_node = ToolNode(
    [multiply],
    handle_tool_errors="Cannot use 42 as a first operand!"
)

agent_custom = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=custom_tool_node
)

agent_custom.invoke({"messages": [{"role": "user", "content": "what's 42 x 7?"}]})

Handle large numbers of tools

As the number of available tools grows, you may want to limit the scope of the LLM’s selection, to decrease token consumption and to help manage sources of error in LLM reasoning. To address this, you can dynamically adjust the tools available to a model by retrieving relevant tools at runtime using semantic search. See langgraph-bigtool prebuilt library for a ready-to-use implementation.

Prebuilt tools

LLM provider tools

You can use prebuilt tools from model providers by passing a dictionary with tool specs to the tools parameter of create_react_agent. For example, to use the web_search_preview tool from OpenAI:

from langgraph.prebuilt import create_react_agent

agent = create_react_agent(
    model="openai:gpt-4o-mini",
    tools=[{"type": "web_search_preview"}]
)
response = agent.invoke(
    {"messages": ["What was a positive news story from today?"]}
)

Please consult the documentation for the specific model you are using to see which tools are available and how to use them.

LangChain tools

Additionally, LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development. You can browse the full list of available integrations in the LangChain integrations directory. Some commonly used tool categories include:

Search: Bing, SerpAPI, Tavily
Code interpreters: Python REPL, Node.js REPL
Databases: SQL, MongoDB, Redis
Web data: Web scraping and browsing
APIs: OpenWeatherMap, NewsAPI, and others

These integrations can be configured and added to your agents using the same tools parameter shown in the examples above.

Basic configuration

Low-level configuration

Components

Define a tool

Run a tool

Use in an agent

Dynamically select tools

Use in a workflow

ToolNode

Tool customization

Parameter descriptions

Explicit input schema

Tool name

Context management

Configuration

Short-term memory

Long-term memory

Advanced tool features

Immediate return

Force tool use

Disable parallel calls

Handle errors

Disable error handling

Custom error messages

Error handling in agents

Handle large numbers of tools

Prebuilt tools

LLM provider tools

LangChain tools

Basic configuration

Low-level configuration

Components

​Define a tool

​Run a tool

​Use in an agent

​Dynamically select tools

​Use in a workflow

​ToolNode

​Tool customization

​Parameter descriptions

​Explicit input schema

​Tool name

​Context management

​Configuration

​Short-term memory

​Long-term memory

​Advanced tool features

​Immediate return

​Force tool use

​Disable parallel calls

​Handle errors

​Disable error handling

​Custom error messages

​Error handling in agents

​Handle large numbers of tools

​Prebuilt tools

​LLM provider tools

​LangChain tools

Define a tool

Run a tool

Use in an agent

Dynamically select tools

Use in a workflow

ToolNode

Tool customization

Parameter descriptions

Explicit input schema

Tool name

Context management

Configuration

Short-term memory

Long-term memory

Advanced tool features

Immediate return

Force tool use

Disable parallel calls

Handle errors

Disable error handling

Custom error messages

Error handling in agents

Handle large numbers of tools

Prebuilt tools

LLM provider tools

LangChain tools