Build the LangGraph calculator quickstart
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Install the LangChain Docs MCP servers to give your agent access to up-to-date LangChain documentation and examples.
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Install LangChain Skills to improve your agent’s performance on LangChain ecosystem tasks.
Install LangChain Skills
- Use the Graph API if you prefer to define your agent as a graph of nodes and edges.
- Use the Functional API if you prefer to define your agent as a single function.
For this example, you will need to set up a Claude (Anthropic) account and get an API key. Then, set the
ANTHROPIC_API_KEY environment variable in your terminal. See chat model integrations for all available providers. If you use LangSmith Gateway, you can bring your own provider keys or use Gateway Credits to access models without a provider key.- Use the Graph API
- Use the Functional API
1. Define tools and model
In this example, we’ll use the Claude Sonnet 4.5 model and define tools for addition, multiplication, and division.from langchain.tools import tool
from langchain.chat_models import init_chat_model
model = init_chat_model(
"claude-sonnet-4-6",
temperature=0
)
# Define tools
@tool
def multiply(a: int, b: int) -> int:
"""Multiply `a` and `b`.
Args:
a: First int
b: Second int
"""
return a * b
@tool
def add(a: int, b: int) -> int:
"""Adds `a` and `b`.
Args:
a: First int
b: Second int
"""
return a + b
@tool
def divide(a: int, b: int) -> float:
"""Divide `a` and `b`.
Args:
a: First int
b: Second int
"""
return a / b
# Augment the LLM with tools
tools = [add, multiply, divide]
tools_by_name = {tool.name: tool for tool in tools}
model_with_tools = model.bind_tools(tools)
2. Define state
The graph’s state is used to store the messages and the number of LLM calls.State in LangGraph persists throughout the agent’s execution.The
Annotated type with operator.add ensures that new messages are appended to the existing list rather than replacing it.from langchain.messages import AnyMessage
from typing_extensions import TypedDict, Annotated
import operator
class MessagesState(TypedDict):
messages: Annotated[list[AnyMessage], operator.add]
llm_calls: int
3. Define model node
The model node is used to call the LLM and decide whether to call a tool or not.from langchain.messages import SystemMessage
def llm_call(state: dict):
"""LLM decides whether to call a tool or not"""
return {
"messages": [
model_with_tools.invoke(
[
SystemMessage(
content="You are a helpful assistant tasked with performing arithmetic on a set of inputs."
)
]
+ state["messages"]
)
],
"llm_calls": state.get('llm_calls', 0) + 1
}
4. Define tool node
The tool node is used to call the tools and return the results.from langchain.messages import ToolMessage
def tool_node(state: dict):
"""Performs the tool call"""
result = []
for tool_call in state["messages"][-1].tool_calls:
tool = tools_by_name[tool_call["name"]]
observation = tool.invoke(tool_call["args"])
result.append(ToolMessage(content=observation, tool_call_id=tool_call["id"]))
return {"messages": result}
5. Define end logic
The conditional edge function is used to route to the tool node or end based upon whether the LLM made a tool call.from typing import Literal
from langgraph.graph import StateGraph, START, END
def should_continue(state: MessagesState) -> Literal["tool_node", END]:
"""Decide if we should continue the loop or stop based upon whether the LLM made a tool call"""
messages = state["messages"]
last_message = messages[-1]
# If the LLM makes a tool call, then perform an action
if last_message.tool_calls:
return "tool_node"
# Otherwise, we stop (reply to the user)
return END
6. Build and compile the agent
The agent is built using theStateGraph class and compiled using the compile method.# Build workflow
agent_builder = StateGraph(MessagesState)
# Add nodes
agent_builder.add_node("llm_call", llm_call)
agent_builder.add_node("tool_node", tool_node)
# Add edges to connect nodes
agent_builder.add_edge(START, "llm_call")
agent_builder.add_conditional_edges(
"llm_call",
should_continue,
["tool_node", END]
)
agent_builder.add_edge("tool_node", "llm_call")
# Compile the agent
agent = agent_builder.compile()
# Show the agent
from IPython.display import Image, display
display(Image(agent.get_graph(xray=True).draw_mermaid_png()))
# Invoke
from langchain.messages import HumanMessage
messages = [HumanMessage(content="Add 3 and 4.")]
messages = agent.invoke({"messages": messages})
for m in messages["messages"]:
m.pretty_print()
Trace and debug your agent with LangSmith. Follow the tracing quickstart to get set up. When ready for production, see Deploy for hosting options.We recommend you also set up LangSmith Engine which monitors your traces, detects issues, and proposes fixes.
Full code example
Full code example
# Step 1: Define tools and model
from langchain.tools import tool
from langchain.chat_models import init_chat_model
model = init_chat_model(
"claude-sonnet-4-6",
temperature=0
)
# Define tools
@tool
def multiply(a: int, b: int) -> int:
"""Multiply `a` and `b`.
Args:
a: First int
b: Second int
"""
return a * b
@tool
def add(a: int, b: int) -> int:
"""Adds `a` and `b`.
Args:
a: First int
b: Second int
"""
return a + b
@tool
def divide(a: int, b: int) -> float:
"""Divide `a` and `b`.
Args:
a: First int
b: Second int
"""
return a / b
# Augment the LLM with tools
tools = [add, multiply, divide]
tools_by_name = {tool.name: tool for tool in tools}
model_with_tools = model.bind_tools(tools)
# Step 2: Define state
from langchain.messages import AnyMessage
from typing_extensions import TypedDict, Annotated
import operator
class MessagesState(TypedDict):
messages: Annotated[list[AnyMessage], operator.add]
llm_calls: int
# Step 3: Define model node
from langchain.messages import SystemMessage
def llm_call(state: MessagesState):
"""LLM decides whether to call a tool or not"""
return {
"messages": [
model_with_tools.invoke(
[
SystemMessage(
content="You are a helpful assistant tasked with performing arithmetic on a set of inputs."
)
]
+ state["messages"]
)
],
"llm_calls": state.get('llm_calls', 0) + 1
}
# Step 4: Define tool node
from langchain.messages import ToolMessage
def tool_node(state: MessagesState):
"""Performs the tool call"""
result = []
for tool_call in state["messages"][-1].tool_calls:
tool = tools_by_name[tool_call["name"]]
observation = tool.invoke(tool_call["args"])
result.append(ToolMessage(content=observation, tool_call_id=tool_call["id"]))
return {"messages": result}
# Step 5: Define logic to determine whether to end
from typing import Literal
from langgraph.graph import StateGraph, START, END
# Conditional edge function to route to the tool node or end based upon whether the LLM made a tool call
def should_continue(state: MessagesState) -> Literal["tool_node", END]:
"""Decide if we should continue the loop or stop based upon whether the LLM made a tool call"""
messages = state["messages"]
last_message = messages[-1]
# If the LLM makes a tool call, then perform an action
if last_message.tool_calls:
return "tool_node"
# Otherwise, we stop (reply to the user)
return END
# Step 6: Build agent
# Build workflow
agent_builder = StateGraph(MessagesState)
# Add nodes
agent_builder.add_node("llm_call", llm_call)
agent_builder.add_node("tool_node", tool_node)
# Add edges to connect nodes
agent_builder.add_edge(START, "llm_call")
agent_builder.add_conditional_edges(
"llm_call",
should_continue,
["tool_node", END]
)
agent_builder.add_edge("tool_node", "llm_call")
# Compile the agent
agent = agent_builder.compile()
from IPython.display import Image, display
# Show the agent
display(Image(agent.get_graph(xray=True).draw_mermaid_png()))
# Invoke
from langchain.messages import HumanMessage
messages = [HumanMessage(content="Add 3 and 4.")]
messages = agent.invoke({"messages": messages})
for m in messages["messages"]:
m.pretty_print()
1. Define tools and model
In this example, we’ll use the Claude Sonnet 4.5 model and define tools for addition, multiplication, and division.from langchain.tools import tool
from langchain.chat_models import init_chat_model
model = init_chat_model(
"claude-sonnet-4-6",
temperature=0
)
# Define tools
@tool
def multiply(a: int, b: int) -> int:
"""Multiply `a` and `b`.
Args:
a: First int
b: Second int
"""
return a * b
@tool
def add(a: int, b: int) -> int:
"""Adds `a` and `b`.
Args:
a: First int
b: Second int
"""
return a + b
@tool
def divide(a: int, b: int) -> float:
"""Divide `a` and `b`.
Args:
a: First int
b: Second int
"""
return a / b
# Augment the LLM with tools
tools = [add, multiply, divide]
tools_by_name = {tool.name: tool for tool in tools}
model_with_tools = model.bind_tools(tools)
from langgraph.graph import add_messages
from langchain.messages import (
SystemMessage,
HumanMessage,
ToolCall,
)
from langchain_core.messages import BaseMessage
from langgraph.func import entrypoint, task
2. Define model node
The model node is used to call the LLM and decide whether to call a tool or not.The
@task decorator marks a function as a task that can be executed as part of the agent. Tasks can be called synchronously or asynchronously within your entrypoint function.@task
def call_llm(messages: list[BaseMessage]):
"""LLM decides whether to call a tool or not"""
return model_with_tools.invoke(
[
SystemMessage(
content="You are a helpful assistant tasked with performing arithmetic on a set of inputs."
)
]
+ messages
)
3. Define tool node
The tool node is used to call the tools and return the results.@task
def call_tool(tool_call: ToolCall):
"""Performs the tool call"""
tool = tools_by_name[tool_call["name"]]
return tool.invoke(tool_call)
4. Define agent
The agent is built using the@entrypoint function.In the Functional API, instead of defining nodes and edges explicitly, you write standard control flow logic (loops, conditionals) within a single function.
@entrypoint()
def agent(messages: list[BaseMessage]):
model_response = call_llm(messages).result()
while True:
if not model_response.tool_calls:
break
# Execute tools
tool_result_futures = [
call_tool(tool_call) for tool_call in model_response.tool_calls
]
tool_results = [fut.result() for fut in tool_result_futures]
messages = add_messages(messages, [model_response, *tool_results])
model_response = call_llm(messages).result()
messages = add_messages(messages, model_response)
return messages
# Invoke
messages = [HumanMessage(content="Add 3 and 4.")]
stream = agent.stream_events(messages, version="v3")
for snapshot in stream.values:
print(snapshot)
print("\n")
Trace and debug your agent with LangSmith. Follow the tracing quickstart to get set up. When ready for production, see Deploy for hosting options.We recommend you also set up LangSmith Engine which monitors your traces, detects issues, and proposes fixes.
Full code example
Full code example
# Step 1: Define tools and model
from langchain.tools import tool
from langchain.chat_models import init_chat_model
model = init_chat_model(
"claude-sonnet-4-6",
temperature=0
)
# Define tools
@tool
def multiply(a: int, b: int) -> int:
"""Multiply `a` and `b`.
Args:
a: First int
b: Second int
"""
return a * b
@tool
def add(a: int, b: int) -> int:
"""Adds `a` and `b`.
Args:
a: First int
b: Second int
"""
return a + b
@tool
def divide(a: int, b: int) -> float:
"""Divide `a` and `b`.
Args:
a: First int
b: Second int
"""
return a / b
# Augment the LLM with tools
tools = [add, multiply, divide]
tools_by_name = {tool.name: tool for tool in tools}
model_with_tools = model.bind_tools(tools)
from langgraph.graph import add_messages
from langchain.messages import (
SystemMessage,
HumanMessage,
ToolCall,
)
from langchain_core.messages import BaseMessage
from langgraph.func import entrypoint, task
# Step 2: Define model node
@task
def call_llm(messages: list[BaseMessage]):
"""LLM decides whether to call a tool or not"""
return model_with_tools.invoke(
[
SystemMessage(
content="You are a helpful assistant tasked with performing arithmetic on a set of inputs."
)
]
+ messages
)
# Step 3: Define tool node
@task
def call_tool(tool_call: ToolCall):
"""Performs the tool call"""
tool = tools_by_name[tool_call["name"]]
return tool.invoke(tool_call)
# Step 4: Define agent
@entrypoint()
def agent(messages: list[BaseMessage]):
model_response = call_llm(messages).result()
while True:
if not model_response.tool_calls:
break
# Execute tools
tool_result_futures = [
call_tool(tool_call) for tool_call in model_response.tool_calls
]
tool_results = [fut.result() for fut in tool_result_futures]
messages = add_messages(messages, [model_response, *tool_results])
model_response = call_llm(messages).result()
messages = add_messages(messages, model_response)
return messages
# Invoke
messages = [HumanMessage(content="Add 3 and 4.")]
stream = agent.stream_events(messages, version="v3")
for snapshot in stream.values:
print(snapshot)
print("\n")
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