> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-ramonn-1789138246-3410f5f.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

This quickstart demonstrates how to build a calculator agent using the LangGraph Graph API or the Functional API.

<Prompt description="Build the LangGraph calculator quickstart" icon="sparkles" actions={["copy"]}>
  Build a LangGraph calculator agent in this working directory by following the LangGraph quickstart.

  ## Step 1: Read the guide

  Detect whether this project uses Python or TypeScript/JavaScript. Fetch and follow the matching page; treat it as the source of truth for package names, model strings, and code:

  * Python: [https://docs.langchain.com/oss/python/langgraph/quickstart.md](https://docs.langchain.com/oss/python/langgraph/quickstart.md)
  * TypeScript: [https://docs.langchain.com/oss/javascript/langgraph/quickstart.md](https://docs.langchain.com/oss/javascript/langgraph/quickstart.md)

  Ask the user whether to use the Graph API or the Functional API. If they have no preference, use the Graph API path.

  ## Step 2: Install dependencies

  Install the packages required by the chosen path with the package manager already used in this project.

  ## Step 3: Configure model credentials

  This quickstart uses Anthropic by default. Check whether `ANTHROPIC_API_KEY` is set. If it is not, ask the user to create a key and set it in the shell or a `.env` file, then stop and wait for confirmation. Do not invent, hardcode, or commit API keys. If the user prefers another chat model provider from the integrations docs, adapt the example accordingly after confirming.

  ## Step 4: Implement the calculator agent

  Implement the selected quickstart path end to end: tools for add, multiply, and divide; the agent graph or functional workflow; and a sample invocation that exercises tool calling. Print the final result so the user can verify the run.

  ## Rules

  * Stay scoped to this quickstart. Do not add deployment, evals, or unrelated frameworks unless the user asks.
  * Prefer the APIs and structure shown on the fetched guide over inventing a different agent pattern.
  * Ask rather than guess when a secret, API choice, or project convention is unclear.
</Prompt>

<Tip>
  **Using an AI coding assistant?**

  * Install the [LangChain Docs MCP servers](/use-these-docs) to give your agent access to up-to-date LangChain documentation and examples.

      <Prompt description="Connect LangChain docs MCP servers" icon="plug" actions={["copy"]}>
        Connect both LangChain documentation MCP servers to my coding agent so it can look up current LangChain, LangGraph, and LangSmith docs and API reference.

        Servers to add:

        * `docs-langchain`: [https://docs.langchain.com/mcp](https://docs.langchain.com/mcp)
        * `reference-langchain`: [https://reference.langchain.com/mcp](https://reference.langchain.com/mcp)

        Detect which agent or editor I am using (Claude Code, Cursor, Codex CLI, Claude Desktop, Deep Agents Code, VS Code, Antigravity, or another MCP-compatible client). Use the matching setup from [https://docs.langchain.com/use-these-docs.md](https://docs.langchain.com/use-these-docs.md):

        * Claude Code: `claude mcp add --transport http` for each server (project scope by default; use `--scope user` only if I ask for global access).
        * Codex CLI: `codex mcp add` with each server URL.
        * Cursor, Deep Agents Code, VS Code, or Antigravity: merge both entries into the MCP settings JSON using the field names shown on that page for my client.
        * Claude Desktop: add both URLs under Settings > Connectors.

        Do not invent alternate MCP URLs. After configuring, confirm both servers are listed and reachable.
      </Prompt>
  * Install [LangChain Skills](https://github.com/langchain-ai/langchain-skills) to improve your agent's performance on LangChain ecosystem tasks.

      <Prompt description="Install LangChain Skills" icon="puzzle" actions={["copy"]}>
        Install LangChain Skills for my coding agent so it can perform better on LangChain, LangGraph, and Deep Agents tasks.

        Use the Agent Skills installer from [https://github.com/langchain-ai/langchain-skills](https://github.com/langchain-ai/langchain-skills):

        ```bash theme={null}
        npx skills add langchain-ai/langchain-skills --skill '*' --yes
        ```

        If I ask for a global install instead, use:

        ```bash theme={null}
        npx skills add langchain-ai/langchain-skills --skill '*' --yes --global
        ```

        Detect which agent or editor I am using. If I use Claude Code and prefer the plugin path, follow the marketplace install from that repository README (`/plugin marketplace add` then `/plugin install`). Do not invent alternate skill package names or install URLs. After installing, confirm the skills are available to the agent.
      </Prompt>
</Tip>

* [Use the Graph API](#use-the-graph-api) if you prefer to define your agent as a graph of nodes and edges.
* [Use the Functional API](#use-the-functional-api) if you prefer to define your agent as a single function.

For conceptual information, see [Graph API overview](/oss/python/langgraph/graph-api) and [Functional API overview](/oss/python/langgraph/functional-api).

<Info>
  For this example, you will need to set up a [Claude (Anthropic)](https://www.anthropic.com/) account and get an API key. Then, set the `ANTHROPIC_API_KEY` environment variable in your terminal. See [chat model integrations](/oss/python/integrations/chat) for all available providers. If you use [LangSmith Gateway](/langsmith/llm-gateway), you can [bring your own provider keys](/langsmith/llm-gateway-quickstart) or use [Gateway Credits](/langsmith/llm-gateway-credits) to access models without a provider key.
</Info>

<Tabs>
  <Tab title="Use the Graph 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.

    ```python theme={null}
    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.

    <Tip>
      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.
    </Tip>

    ```python theme={null}
    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 theme={null}
    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 theme={null}
    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 theme={null}
    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 theme={null}
    # 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()
    ```

    <Tip>
      Trace and debug your agent with [LangSmith](https://smith.langchain.com?utm_source=docs\&utm_medium=cta\&utm_campaign=langsmith-signup\&utm_content=oss-langgraph-quickstart). Follow the [tracing quickstart](/langsmith/trace-with-langgraph) to get set up. When ready for production, see [Deploy](/langsmith/deployment) for hosting options.

      We recommend you also set up [LangSmith Engine](/langsmith/engine) which monitors your traces, detects issues, and proposes fixes.
    </Tip>

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

    <Accordion title="Full code example">
      ```python theme={null}
      # 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()

      ```
    </Accordion>
  </Tab>

  <Tab title="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.

    ```python theme={null}
    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.

    <Tip>
      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.
    </Tip>

    ```python theme={null}
    @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 theme={null}
    @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.

    <Note>
      In the Functional API, instead of defining nodes and edges explicitly, you write standard control flow logic (loops, conditionals) within a single function.
    </Note>

    ```python theme={null}
    @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")
    ```

    <Tip>
      Trace and debug your agent with [LangSmith](https://smith.langchain.com?utm_source=docs\&utm_medium=cta\&utm_campaign=langsmith-signup\&utm_content=oss-langgraph-quickstart). Follow the [tracing quickstart](/langsmith/trace-with-langgraph) to get set up. When ready for production, see [Deploy](/langsmith/deployment) for hosting options.

      We recommend you also set up [LangSmith Engine](/langsmith/engine) which monitors your traces, detects issues, and proposes fixes.
    </Tip>

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

    <Accordion title="Full code example" icon="code">
      ```python theme={null}
      # 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")
      ```
    </Accordion>
  </Tab>
</Tabs>

***

<div className="source-links">
  <Callout icon="terminal-2">
    [Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
  </Callout>

  <Callout icon="edit">
    [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).
  </Callout>
</div>