Quickstart ↗

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


    model = init_chat_model(
        "claude-sonnet-4-5-20250929",
        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.

<span class="callout-start" data-callout-type="tip"></span>
  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.
<span class="callout-end"></span>

```python
    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.

```python
    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.

```python
    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.

```python
    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 the [`StateGraph`](https://reference.langchain.com/python/langgraph/graph/state/StateGraph) class and compiled using the [`compile`](https://reference.langchain.com/python/langgraph/graph/state/StateGraph/compile) method.

```python
    # 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()
    ```

<span class="callout-start" data-callout-type="tip"></span>
  To learn how to trace your agent with LangSmith, see the [LangSmith documentation](/langsmith/trace-with-langgraph).
<span class="callout-end"></span>

Congratulations! You've built your first agent using the LangGraph Graph API.

<Accordion title="Full code example">
  ```python
      # 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-4-5-20250929",
          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: 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
          }


      # Step 4: Define tool node

      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}

      # 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()

      ```
</Accordion>
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Use the Functional API"></span>
## 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.

```python
    from langchain.tools import tool
    from langchain.chat_models import init_chat_model


    model = init_chat_model(
        "claude-sonnet-4-5-20250929",
        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.

<span class="callout-start" data-callout-type="tip"></span>
  The [`@task`](https://reference.langchain.com/python/langgraph/func/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.
<span class="callout-end"></span>

```python
    @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.

```python
    @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`](https://reference.langchain.com/python/langgraph/func/entrypoint) function.

<span class="callout-start" data-callout-type="note"></span>
  In the Functional API, instead of defining nodes and edges explicitly, you write standard control flow logic (loops, conditionals) within a single function.
<span class="callout-end"></span>

```python
    @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.")]
    for chunk in agent.stream(messages, stream_mode="updates"):
        print(chunk)
        print("\n")
    ```

<span class="callout-start" data-callout-type="tip"></span>
  To learn how to trace your agent with LangSmith, see the [LangSmith documentation](/langsmith/trace-with-langgraph).
<span class="callout-end"></span>

Congratulations! You've built your first agent using the LangGraph Functional API.

<Accordion title="Full code example" icon="code">
  ```python
      # 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-4-5-20250929",
          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.")]
      for chunk in agent.stream(messages, stream_mode="updates"):
          print(chunk)
          print("\n")
      ```
</Accordion>
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

***

<span class="callout-start" data-callout-type="note"></span>
  [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/langgraph/quickstart.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).
<span class="callout-end"></span>

<span class="callout-start" data-callout-type="note"></span>
  [Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
<span class="callout-end"></span>