langgraph/docs/docs/tutorials/introduction.ipynb

🚀 LangGraph Quick Start

In this tutorial, we will build a support chatbot in LangGraph that can:

✅ Answer common questions by searching the web
✅ Maintain conversation state across calls
✅ Route complex queries to a human for review
✅ Use custom state to control its behavior
✅ Rewind and explore alternative conversation paths

We'll start with a basic chatbot and progressively add more sophisticated capabilities, introducing key LangGraph concepts along the way. Let’s dive in! 🌟

Setup

First, install the required packages and configure your environment:

In [1]:

%%capture --no-stderr
%pip install -U langgraph langsmith langchain_anthropic

In [2]:

import getpass
import os


def _set_env(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"{var}: ")


_set_env("ANTHROPIC_API_KEY")

ANTHROPIC_API_KEY:  ········

Set up LangSmith for LangGraph development

Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started here.

Part 1: Build a Basic Chatbot

We'll first create a simple chatbot using LangGraph. This chatbot will respond directly to user messages. Though simple, it will illustrate the core concepts of building with LangGraph. By the end of this section, you will have a built rudimentary chatbot.

Start by creating a StateGraph. A StateGraph object defines the structure of our chatbot as a "state machine". We'll add nodes to represent the llm and functions our chatbot can call and edges to specify how the bot should transition between these functions.

In [2]:

from typing import Annotated

from typing_extensions import TypedDict

from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages


class State(TypedDict):
    # Messages have the type "list". The `add_messages` function
    # in the annotation defines how this state key should be updated
    # (in this case, it appends messages to the list, rather than overwriting them)
    messages: Annotated[list, add_messages]


graph_builder = StateGraph(State)

Our graph can now handle two key tasks:

1. Each node can receive the current State as input and output an update to the state.
2. Updates to messages will be appended to the existing list rather than overwriting it, thanks to the prebuilt add_messages function used with the Annotated syntax.

---

!!! tip "Concept"

When defining a graph, the first step is to define its `State`. The `State` includes the graph's schema and [reducer functions](https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers) that handle state updates. In our example, `State` is a `TypedDict` with one key: `messages`. The [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages) reducer function is used to append new messages to the list instead of overwriting it. Keys without a reducer annotation will overwrite previous values. Learn more about state, reducers, and related concepts in [this guide](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages).

---

Next, add a "chatbot" node. Nodes represent units of work. They are typically regular python functions.

In [ ]:

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")


def chatbot(state: State):
    return {"messages": [llm.invoke(state["messages"])]}


# The first argument is the unique node name
# The second argument is the function or object that will be called whenever
# the node is used.
graph_builder.add_node("chatbot", chatbot)

Notice how the chatbot node function takes the current State as input and returns a dictionary containing an updated messages list under the key "messages". This is the basic pattern for all LangGraph node functions.

The add_messages function in our State will append the llm's response messages to whatever messages are already in the state.

Next, add an entry point. This tells our graph where to start its work each time we run it.

In [ ]:

graph_builder.add_edge(START, "chatbot")

Similarly, set a finish point. This instructs the graph "any time this node is run, you can exit."

In [ ]:

graph_builder.add_edge("chatbot", END)

Finally, we'll want to be able to run our graph. To do so, call "compile()" on the graph builder. This creates a "CompiledGraph" we can use invoke on our state.

In [6]:

graph = graph_builder.compile()

You can visualize the graph using the get_graph method and one of the "draw" methods, like draw_ascii or draw_png. The draw methods each require additional dependencies.

In [7]:

from IPython.display import Image, display

try:
    display(Image(graph.get_graph().draw_mermaid_png()))
except Exception:
    # This requires some extra dependencies and is optional
    pass

Now let's run the chatbot!

Tip: You can exit the chat loop at any time by typing "quit", "exit", or "q".

In [8]:

def stream_graph_updates(user_input: str):
    for event in graph.stream({"messages": [("user", user_input)]}):
        for value in event.values():
            print("Assistant:", value["messages"][-1].content)


while True:
    try:
        user_input = input("User: ")
        if user_input.lower() in ["quit", "exit", "q"]:
            print("Goodbye!")
            break

        stream_graph_updates(user_input)
    except:
        # fallback if input() is not available
        user_input = "What do you know about LangGraph?"
        print("User: " + user_input)
        stream_graph_updates(user_input)
        break

Assistant: LangGraph is a library designed to help build stateful multi-agent applications using language models. It provides tools for creating workflows and state machines to coordinate multiple AI agents or language model interactions. LangGraph is built on top of LangChain, leveraging its components while adding graph-based coordination capabilities. It's particularly useful for developing more complex, stateful AI applications that go beyond simple query-response interactions.
Goodbye!

Congratulations! You've built your first chatbot using LangGraph. This bot can engage in basic conversation by taking user input and generating responses using an LLM. You can inspect a LangSmith Trace for the call above at the provided link.

However, you may have noticed that the bot's knowledge is limited to what's in its training data. In the next part, we'll add a web search tool to expand the bot's knowledge and make it more capable.

Below is the full code for this section for your reference:

Full Code

from typing import Annotated

from langchain_anthropic import ChatAnthropic
from typing_extensions import TypedDict

from langgraph.graph import StateGraph
from langgraph.graph.message import add_messages


class State(TypedDict):
    messages: Annotated[list, add_messages]


graph_builder = StateGraph(State)


llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")


def chatbot(state: State):
    return {"messages": [llm.invoke(state["messages"])]}


# The first argument is the unique node name
# The second argument is the function or object that will be called whenever
# the node is used.
graph_builder.add_node("chatbot", chatbot)
graph_builder.set_entry_point("chatbot")
graph_builder.set_finish_point("chatbot")
graph = graph_builder.compile()

Part 2: 🛠️ Enhancing the Chatbot with Tools

To handle queries our chatbot can't answer "from memory", we'll integrate a web search tool. Our bot can use this tool to find relevant information and provide better responses.

Requirements

Before we start, make sure you have the necessary packages installed and API keys set up:

First, install the requirements to use the Tavily Search Engine, and set your TAVILY_API_KEY.

In [10]:

%%capture --no-stderr
%pip install -U tavily-python langchain_community

In [11]:

_set_env("TAVILY_API_KEY")

TAVILY_API_KEY:  ········

Next, define the tool:

In [11]:

from langchain_community.tools.tavily_search import TavilySearchResults

tool = TavilySearchResults(max_results=2)
tools = [tool]
tool.invoke("What's a 'node' in LangGraph?")

Out [11]:

[{'url': 'https://medium.com/@cplog/introduction-to-langgraph-a-beginners-guide-14f9be027141',
  'content': 'Nodes: Nodes are the building blocks of your LangGraph. Each node represents a function or a computation step. You define nodes to perform specific tasks, such as processing input, making ...'},
 {'url': 'https://saksheepatil05.medium.com/demystifying-langgraph-a-beginner-friendly-dive-into-langgraph-concepts-5ffe890ddac0',
  'content': 'Nodes (Tasks): Nodes are like the workstations on the assembly line. Each node performs a specific task on the product. In LangGraph, nodes are Python functions that take the current state, do some work, and return an updated state. Next, we define the nodes, each representing a task in our sandwich-making process.'}]

The results are page summaries our chat bot can use to answer questions.

Next, we'll start defining our graph. The following is all the same as in Part 1, except we have added bind_tools on our LLM. This lets the LLM know the correct JSON format to use if it wants to use our search engine.

In [ ]:

from typing import Annotated

from langchain_anthropic import ChatAnthropic
from typing_extensions import TypedDict

from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages


class State(TypedDict):
    messages: Annotated[list, add_messages]


graph_builder = StateGraph(State)


llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
# Modification: tell the LLM which tools it can call
llm_with_tools = llm.bind_tools(tools)


def chatbot(state: State):
    return {"messages": [llm_with_tools.invoke(state["messages"])]}


graph_builder.add_node("chatbot", chatbot)

Next we need to create a function to actually run the tools if they are called. We'll do this by adding the tools to a new node.

Below, we implement a BasicToolNode that checks the most recent message in the state and calls tools if the message contains tool_calls. It relies on the LLM's tool_calling support, which is available in Anthropic, OpenAI, Google Gemini, and a number of other LLM providers.

We will later replace this with LangGraph's prebuilt ToolNode to speed things up, but building it ourselves first is instructive.

In [ ]:

import json

from langchain_core.messages import ToolMessage


class BasicToolNode:
    """A node that runs the tools requested in the last AIMessage."""

    def __init__(self, tools: list) -> None:
        self.tools_by_name = {tool.name: tool for tool in tools}

    def __call__(self, inputs: dict):
        if messages := inputs.get("messages", []):
            message = messages[-1]
        else:
            raise ValueError("No message found in input")
        outputs = []
        for tool_call in message.tool_calls:
            tool_result = self.tools_by_name[tool_call["name"]].invoke(
                tool_call["args"]
            )
            outputs.append(
                ToolMessage(
                    content=json.dumps(tool_result),
                    name=tool_call["name"],
                    tool_call_id=tool_call["id"],
                )
            )
        return {"messages": outputs}


tool_node = BasicToolNode(tools=[tool])
graph_builder.add_node("tools", tool_node)

With the tool node added, we can define the conditional_edges.

Recall that edges route the control flow from one node to the next. Conditional edges usually contain "if" statements to route to different nodes depending on the current graph state. These functions receive the current graph state and return a string or list of strings indicating which node(s) to call next.

Below, call define a router function called route_tools, that checks for tool_calls in the chatbot's output. Provide this function to the graph by calling add_conditional_edges, which tells the graph that whenever the chatbot node completes to check this function to see where to go next.

The condition will route to tools if tool calls are present and END if not.

Later, we will replace this with the prebuilt tools_condition to be more concise, but implementing it ourselves first makes things more clear.

In [14]:

from typing import Literal


def route_tools(
    state: State,
):
    """
    Use in the conditional_edge to route to the ToolNode if the last message
    has tool calls. Otherwise, route to the end.
    """
    if isinstance(state, list):
        ai_message = state[-1]
    elif messages := state.get("messages", []):
        ai_message = messages[-1]
    else:
        raise ValueError(f"No messages found in input state to tool_edge: {state}")
    if hasattr(ai_message, "tool_calls") and len(ai_message.tool_calls) > 0:
        return "tools"
    return END


# The `tools_condition` function returns "tools" if the chatbot asks to use a tool, and "END" if
# it is fine directly responding. This conditional routing defines the main agent loop.
graph_builder.add_conditional_edges(
    "chatbot",
    route_tools,
    # The following dictionary lets you tell the graph to interpret the condition's outputs as a specific node
    # It defaults to the identity function, but if you
    # want to use a node named something else apart from "tools",
    # You can update the value of the dictionary to something else
    # e.g., "tools": "my_tools"
    {"tools": "tools", END: END},
)
# Any time a tool is called, we return to the chatbot to decide the next step
graph_builder.add_edge("tools", "chatbot")
graph_builder.add_edge(START, "chatbot")
graph = graph_builder.compile()

Notice that conditional edges start from a single node. This tells the graph "any time the 'chatbot' node runs, either go to 'tools' if it calls a tool, or end the loop if it responds directly.

Like the prebuilt tools_condition, our function returns the END string if no tool calls are made. When the graph transitions to END, it has no more tasks to complete and ceases execution. Because the condition can return END, we don't need to explicitly set a finish_point this time. Our graph already has a way to finish!

Let's visualize the graph we've built. The following function has some additional dependencies to run that are unimportant for this tutorial.

In [15]:

from IPython.display import Image, display

try:
    display(Image(graph.get_graph().draw_mermaid_png()))
except Exception:
    # This requires some extra dependencies and is optional
    pass

Now we can ask the bot questions outside its training data.

In [16]:

while True:
    try:
        user_input = input("User: ")
        if user_input.lower() in ["quit", "exit", "q"]:
            print("Goodbye!")
            break

        stream_graph_updates(user_input)
    except:
        # fallback if input() is not available
        user_input = "What do you know about LangGraph?"
        print("User: " + user_input)
        stream_graph_updates(user_input)
        break

Assistant: [{'text': "To provide you with accurate and up-to-date information about LangGraph, I'll need to search for the latest details. Let me do that for you.", 'type': 'text'}, {'id': 'toolu_01Q588CszHaSvvP2MxRq9zRD', 'input': {'query': 'LangGraph AI tool information'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Assistant: [{"url": "https://www.langchain.com/langgraph", "content": "LangGraph sets the foundation for how we can build and scale AI workloads \u2014 from conversational agents, complex task automation, to custom LLM-backed experiences that 'just work'. The next chapter in building complex production-ready features with LLMs is agentic, and with LangGraph and LangSmith, LangChain delivers an out-of-the-box solution ..."}, {"url": "https://github.com/langchain-ai/langgraph", "content": "Overview. LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures ..."}]
Assistant: Based on the search results, I can provide you with information about LangGraph:

1. Purpose:
   LangGraph is a library designed for building stateful, multi-actor applications with Large Language Models (LLMs). It's particularly useful for creating agent and multi-agent workflows.

2. Developer:
   LangGraph is developed by LangChain, a company known for its tools and frameworks in the AI and LLM space.

3. Key Features:
   - Cycles: LangGraph allows the definition of flows that involve cycles, which is essential for most agentic architectures.
   - Controllability: It offers enhanced control over the application flow.
   - Persistence: The library provides ways to maintain state and persistence in LLM-based applications.

4. Use Cases:
   LangGraph can be used for various applications, including:
   - Conversational agents
   - Complex task automation
   - Custom LLM-backed experiences

5. Integration:
   LangGraph works in conjunction with LangSmith, another tool by LangChain, to provide an out-of-the-box solution for building complex, production-ready features with LLMs.

6. Significance:
   LangGraph is described as setting the foundation for building and scaling AI workloads. It's positioned as a key tool in the next chapter of LLM-based application development, particularly in the realm of agentic AI.

7. Availability:
   LangGraph is open-source and available on GitHub, which suggests that developers can access and contribute to its codebase.

8. Comparison to Other Frameworks:
   LangGraph is noted to offer unique benefits compared to other LLM frameworks, particularly in its ability to handle cycles, provide controllability, and maintain persistence.

LangGraph appears to be a significant tool in the evolving landscape of LLM-based application development, offering developers new ways to create more complex, stateful, and interactive AI systems.
Goodbye!

Congrats! You've created a conversational agent in langgraph that can use a search engine to retrieve updated information when needed. Now it can handle a wider range of user queries. To inspect all the steps your agent just took, check out this LangSmith trace.

Our chatbot still can't remember past interactions on its own, limiting its ability to have coherent, multi-turn conversations. In the next part, we'll add memory to address this.

The full code for the graph we've created in this section is reproduced below, replacing our BasicToolNode for the prebuilt ToolNode, and our route_tools condition with the prebuilt tools_condition

Full Code

from typing import Annotated

from langchain_anthropic import ChatAnthropic
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.messages import BaseMessage
from typing_extensions import TypedDict

from langgraph.graph import StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition


class State(TypedDict):
    messages: Annotated[list, add_messages]


graph_builder = StateGraph(State)


tool = TavilySearchResults(max_results=2)
tools = [tool]
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
llm_with_tools = llm.bind_tools(tools)


def chatbot(state: State):
    return {"messages": [llm_with_tools.invoke(state["messages"])]}


graph_builder.add_node("chatbot", chatbot)

tool_node = ToolNode(tools=[tool])
graph_builder.add_node("tools", tool_node)

graph_builder.add_conditional_edges(
    "chatbot",
    tools_condition,
)
# Any time a tool is called, we return to the chatbot to decide the next step
graph_builder.add_edge("tools", "chatbot")
graph_builder.set_entry_point("chatbot")
graph = graph_builder.compile()

Part 3: Adding Memory to the Chatbot

Our chatbot can now use tools to answer user questions, but it doesn't remember the context of previous interactions. This limits its ability to have coherent, multi-turn conversations.

LangGraph solves this problem through persistent checkpointing. If you provide a checkpointer when compiling the graph and a thread_id when calling your graph, LangGraph automatically saves the state after each step. When you invoke the graph again using the same thread_id, the graph loads its saved state, allowing the chatbot to pick up where it left off.

We will see later that checkpointing is much more powerful than simple chat memory - it lets you save and resume complex state at any time for error recovery, human-in-the-loop workflows, time travel interactions, and more. But before we get too ahead of ourselves, let's add checkpointing to enable multi-turn conversations.

To get started, create a MemorySaver checkpointer.

In [18]:

from langgraph.checkpoint.memory import MemorySaver

memory = MemorySaver()

Notice we're using an in-memory checkpointer. This is convenient for our tutorial (it saves it all in-memory). In a production application, you would likely change this to use SqliteSaver or PostgresSaver and connect to your own DB.

Next define the graph. Now that you've already built your own BasicToolNode, we'll replace it with LangGraph's prebuilt ToolNode and tools_condition, since these do some nice things like parallel API execution. Apart from that, the following is all copied from Part 2.

In [ ]:

from typing import Annotated

from langchain_anthropic import ChatAnthropic
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.messages import BaseMessage
from typing_extensions import TypedDict

from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition


class State(TypedDict):
    messages: Annotated[list, add_messages]


graph_builder = StateGraph(State)


tool = TavilySearchResults(max_results=2)
tools = [tool]
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
llm_with_tools = llm.bind_tools(tools)


def chatbot(state: State):
    return {"messages": [llm_with_tools.invoke(state["messages"])]}


graph_builder.add_node("chatbot", chatbot)

tool_node = ToolNode(tools=[tool])
graph_builder.add_node("tools", tool_node)

graph_builder.add_conditional_edges(
    "chatbot",
    tools_condition,
)
# Any time a tool is called, we return to the chatbot to decide the next step
graph_builder.add_edge("tools", "chatbot")
graph_builder.add_edge(START, "chatbot")

Finally, compile the graph with the provided checkpointer.

In [20]:

graph = graph_builder.compile(checkpointer=memory)

Notice the connectivity of the graph hasn't changed since Part 2. All we are doing is checkpointing the State as the graph works through each node.

In [21]:

from IPython.display import Image, display

try:
    display(Image(graph.get_graph().draw_mermaid_png()))
except Exception:
    # This requires some extra dependencies and is optional
    pass

Now you can interact with your bot! First, pick a thread to use as the key for this conversation.

In [22]:

config = {"configurable": {"thread_id": "1"}}

Next, call your chat bot.

In [23]:

user_input = "Hi there! My name is Will."

# The config is the **second positional argument** to stream() or invoke()!
events = graph.stream(
    {"messages": [("user", user_input)]}, config, stream_mode="values"
)
for event in events:
    event["messages"][-1].pretty_print()

================================ Human Message =================================

Hi there! My name is Will.
================================== Ai Message ==================================

Hello Will! It's nice to meet you. How can I assist you today? Is there anything specific you'd like to know or discuss?

Note: The config was provided as the second positional argument when calling our graph. It importantly is not nested within the graph inputs ({'messages': []}).

Let's ask a followup: see if it remembers your name.

In [24]:

user_input = "Remember my name?"

# The config is the **second positional argument** to stream() or invoke()!
events = graph.stream(
    {"messages": [("user", user_input)]}, config, stream_mode="values"
)
for event in events:
    event["messages"][-1].pretty_print()

================================ Human Message =================================

Remember my name?
================================== Ai Message ==================================

Of course, I remember your name, Will. I always try to pay attention to important details that users share with me. Is there anything else you'd like to talk about or any questions you have? I'm here to help with a wide range of topics or tasks.

Notice that we aren't using an external list for memory: it's all handled by the checkpointer! You can inspect the full execution in this LangSmith trace to see what's going on.

Don't believe me? Try this using a different config.

In [25]:

# The only difference is we change the `thread_id` here to "2" instead of "1"
events = graph.stream(
    {"messages": [("user", user_input)]},
    {"configurable": {"thread_id": "2"}},
    stream_mode="values",
)
for event in events:
    event["messages"][-1].pretty_print()

================================ Human Message =================================

Remember my name?
================================== Ai Message ==================================

I apologize, but I don't have any previous context or memory of your name. As an AI assistant, I don't retain information from past conversations. Each interaction starts fresh. Could you please tell me your name so I can address you properly in this conversation?

Notice that the only change we've made is to modify the thread_id in the config. See this call's LangSmith trace for comparison.

By now, we have made a few checkpoints across two different threads. But what goes into a checkpoint? To inspect a graph's state for a given config at any time, call get_state(config).

In [26]:

snapshot = graph.get_state(config)
snapshot

Out [26]:

StateSnapshot(values={'messages': [HumanMessage(content='Hi there! My name is Will.', additional_kwargs={}, response_metadata={}, id='8c1ca919-c553-4ebf-95d4-b59a2d61e078'), AIMessage(content="Hello Will! It's nice to meet you. How can I assist you today? Is there anything specific you'd like to know or discuss?", additional_kwargs={}, response_metadata={'id': 'msg_01WTQebPhNwmMrmmWojJ9KXJ', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 405, 'output_tokens': 32}}, id='run-58587b77-8c82-41e6-8a90-d62c444a261d-0', usage_metadata={'input_tokens': 405, 'output_tokens': 32, 'total_tokens': 437}), HumanMessage(content='Remember my name?', additional_kwargs={}, response_metadata={}, id='daba7df6-ad75-4d6b-8057-745881cea1ca'), AIMessage(content="Of course, I remember your name, Will. I always try to pay attention to important details that users share with me. Is there anything else you'd like to talk about or any questions you have? I'm here to help with a wide range of topics or tasks.", additional_kwargs={}, response_metadata={'id': 'msg_01E41KitY74HpENRgXx94vag', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 444, 'output_tokens': 58}}, id='run-ffeaae5c-4d2d-4ddb-bd59-5d5cbf2a5af8-0', usage_metadata={'input_tokens': 444, 'output_tokens': 58, 'total_tokens': 502})]}, next=(), config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef7d06e-93e0-6acc-8004-f2ac846575d2'}}, metadata={'source': 'loop', 'writes': {'chatbot': {'messages': [AIMessage(content="Of course, I remember your name, Will. I always try to pay attention to important details that users share with me. Is there anything else you'd like to talk about or any questions you have? I'm here to help with a wide range of topics or tasks.", additional_kwargs={}, response_metadata={'id': 'msg_01E41KitY74HpENRgXx94vag', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 444, 'output_tokens': 58}}, id='run-ffeaae5c-4d2d-4ddb-bd59-5d5cbf2a5af8-0', usage_metadata={'input_tokens': 444, 'output_tokens': 58, 'total_tokens': 502})]}}, 'step': 4, 'parents': {}}, created_at='2024-09-27T19:30:10.820758+00:00', parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef7d06e-859f-6206-8003-e1bd3c264b8f'}}, tasks=())

In [27]:

snapshot.next  # (since the graph ended this turn, `next` is empty. If you fetch a state from within a graph invocation, next tells which node will execute next)

Out [27]:

()

The snapshot above contains the current state values, corresponding config, and the next node to process. In our case, the graph has reached an END state, so next is empty.

Congratulations! Your chatbot can now maintain conversation state across sessions thanks to LangGraph's checkpointing system. This opens up exciting possibilities for more natural, contextual interactions. LangGraph's checkpointing even handles arbitrarily complex graph states, which is much more expressive and powerful than simple chat memory.

In the next part, we'll introduce human oversight to our bot to handle situations where it may need guidance or verification before proceeding.

Check out the code snippet below to review our graph from this section.

Full Code

from typing import Annotated

from langchain_anthropic import ChatAnthropic
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.messages import BaseMessage
from typing_extensions import TypedDict

from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode


class State(TypedDict):
    messages: Annotated[list, add_messages]


graph_builder = StateGraph(State)


tool = TavilySearchResults(max_results=2)
tools = [tool]
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
llm_with_tools = llm.bind_tools(tools)


def chatbot(state: State):
    return {"messages": [llm_with_tools.invoke(state["messages"])]}


graph_builder.add_node("chatbot", chatbot)

tool_node = ToolNode(tools=[tool])
graph_builder.add_node("tools", tool_node)

graph_builder.add_conditional_edges(
    "chatbot",
    tools_condition,
)
graph_builder.add_edge("tools", "chatbot")
graph_builder.set_entry_point("chatbot")
graph = graph_builder.compile(checkpointer=memory)

Part 4: Human-in-the-loop

Agents can be unreliable and may need human input to successfully accomplish tasks. Similarly, for some actions, you may want to require human approval before running to ensure that everything is running as intended.

LangGraph supports human-in-the-loop workflows in a number of ways. In this section, we will use LangGraph's interrupt_before functionality to always break the tool node.

First, start from our existing code. The following is copied from Part 3.

In [ ]:

from typing import Annotated

from langchain_anthropic import ChatAnthropic
from langchain_community.tools.tavily_search import TavilySearchResults
from typing_extensions import TypedDict

from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import StateGraph, START
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition

memory = MemorySaver()


class State(TypedDict):
    messages: Annotated[list, add_messages]


graph_builder = StateGraph(State)


tool = TavilySearchResults(max_results=2)
tools = [tool]
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
llm_with_tools = llm.bind_tools(tools)


def chatbot(state: State):
    return {"messages": [llm_with_tools.invoke(state["messages"])]}


graph_builder.add_node("chatbot", chatbot)

tool_node = ToolNode(tools=[tool])
graph_builder.add_node("tools", tool_node)

graph_builder.add_conditional_edges(
    "chatbot",
    tools_condition,
)
graph_builder.add_edge("tools", "chatbot")
graph_builder.add_edge(START, "chatbot")

Now, compile the graph, specifying to interrupt_before the tools node.

In [29]:

graph = graph_builder.compile(
    checkpointer=memory,
    # This is new!
    interrupt_before=["tools"],
    # Note: can also interrupt __after__ tools, if desired.
    # interrupt_after=["tools"]
)

In [30]:

user_input = "I'm learning LangGraph. Could you do some research on it for me?"
config = {"configurable": {"thread_id": "1"}}
# The config is the **second positional argument** to stream() or invoke()!
events = graph.stream(
    {"messages": [("user", user_input)]}, config, stream_mode="values"
)
for event in events:
    if "messages" in event:
        event["messages"][-1].pretty_print()

================================ Human Message =================================

I'm learning LangGraph. Could you do some research on it for me?
================================== Ai Message ==================================

[{'text': "Certainly! I'd be happy to research LangGraph for you. To get the most up-to-date and comprehensive information, I'll use the Tavily search engine to look this up. Let me do that for you now.", 'type': 'text'}, {'id': 'toolu_01R4ZFcb5hohpiVZwr88Bxhc', 'input': {'query': 'LangGraph framework for building language model applications'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Tool Calls:
  tavily_search_results_json (toolu_01R4ZFcb5hohpiVZwr88Bxhc)
 Call ID: toolu_01R4ZFcb5hohpiVZwr88Bxhc
  Args:
    query: LangGraph framework for building language model applications

Let's inspect the graph state to confirm it worked.

In [31]:

snapshot = graph.get_state(config)
snapshot.next

Out [31]:

('tools',)

Notice that unlike last time, the "next" node is set to 'tools'. We've interrupted here! Let's check the tool invocation.

In [32]:

existing_message = snapshot.values["messages"][-1]
existing_message.tool_calls

Out [32]:

[{'name': 'tavily_search_results_json',
  'args': {'query': 'LangGraph framework for building language model applications'},
  'id': 'toolu_01R4ZFcb5hohpiVZwr88Bxhc',
  'type': 'tool_call'}]

This query seems reasonable. Nothing to filter here. The simplest thing the human can do is just let the graph continue executing. Let's do that below.

Next, continue the graph! Passing in None will just let the graph continue where it left off, without adding anything new to the state.

In [33]:

# `None` will append nothing new to the current state, letting it resume as if it had never been interrupted
events = graph.stream(None, config, stream_mode="values")
for event in events:
    if "messages" in event:
        event["messages"][-1].pretty_print()

================================== Ai Message ==================================

[{'text': "Certainly! I'd be happy to research LangGraph for you. To get the most up-to-date and comprehensive information, I'll use the Tavily search engine to look this up. Let me do that for you now.", 'type': 'text'}, {'id': 'toolu_01R4ZFcb5hohpiVZwr88Bxhc', 'input': {'query': 'LangGraph framework for building language model applications'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Tool Calls:
  tavily_search_results_json (toolu_01R4ZFcb5hohpiVZwr88Bxhc)
 Call ID: toolu_01R4ZFcb5hohpiVZwr88Bxhc
  Args:
    query: LangGraph framework for building language model applications
================================= Tool Message =================================
Name: tavily_search_results_json

[{"url": "https://towardsdatascience.com/from-basics-to-advanced-exploring-langgraph-e8c1cf4db787", "content": "LangChain is one of the leading frameworks for building applications powered by Lardge Language Models. With the LangChain Expression Language (LCEL), defining and executing step-by-step action sequences — also known as chains — becomes much simpler. In more technical terms, LangChain allows us to create DAGs (directed acyclic graphs). As LLM applications, particularly LLM agents, have ..."}, {"url": "https://github.com/langchain-ai/langgraph", "content": "Overview. LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures ..."}]
================================== Ai Message ==================================

Thank you for your patience. I've found some valuable information about LangGraph for you. Let me summarize the key points:

1. LangGraph is a library for building stateful, multi-actor applications with Large Language Models (LLMs).

2. It is particularly useful for creating agent and multi-agent workflows.

3. LangGraph is built on top of LangChain, which is one of the leading frameworks for building LLM-powered applications.

4. Key benefits of LangGraph compared to other LLM frameworks include:
   a) Cycles: It allows you to define flows that involve cycles, which is essential for most agent architectures.
   b) Controllability: Offers more control over the application flow.
   c) Persistence: Provides ways to maintain state across interactions.

5. LangGraph works well with the LangChain Expression Language (LCEL), which simplifies the process of defining and executing step-by-step action sequences (chains).

6. In technical terms, LangGraph enables the creation of Directed Acyclic Graphs (DAGs) for LLM applications.

7. It's particularly useful for building more complex LLM agents and multi-agent systems.

LangGraph seems to be an advanced tool that builds upon LangChain to provide more sophisticated capabilities for creating stateful and multi-actor LLM applications. It's especially valuable if you're looking to create complex agent systems or applications that require maintaining state across interactions.

Is there any specific aspect of LangGraph you'd like to know more about? I'd be happy to dive deeper into any particular area of interest.

Review this call's LangSmith trace to see the exact work that was done in the above call. Notice that the state is loaded in the first step so that your chatbot can continue where it left off.

Congrats! You've used an interrupt to add human-in-the-loop execution to your chatbot, allowing for human oversight and intervention when needed. This opens up the potential UIs you can create with your AI systems. Since we have already added a checkpointer, the graph can be paused indefinitely and resumed at any time as if nothing had happened.

Next, we'll explore how to further customize the bot's behavior using custom state updates.

Below is a copy of the code you used in this section. The only difference between this and the previous parts is the addition of the interrupt_before argument.

Full Code

from typing import Annotated

from langchain_anthropic import ChatAnthropic
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.messages import BaseMessage
from typing_extensions import TypedDict

from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition


class State(TypedDict):
    messages: Annotated[list, add_messages]


graph_builder = StateGraph(State)


tool = TavilySearchResults(max_results=2)
tools = [tool]
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
llm_with_tools = llm.bind_tools(tools)


def chatbot(state: State):
    return {"messages": [llm_with_tools.invoke(state["messages"])]}


graph_builder.add_node("chatbot", chatbot)

tool_node = ToolNode(tools=[tool])
graph_builder.add_node("tools", tool_node)

graph_builder.add_conditional_edges(
    "chatbot",
    tools_condition,
)
graph_builder.add_edge("tools", "chatbot")
graph_builder.set_entry_point("chatbot")

memory = MemorySaver()
graph = graph_builder.compile(
    checkpointer=memory,
    # This is new!
    interrupt_before=["tools"],
    # Note: can also interrupt __after__ actions, if desired.
    # interrupt_after=["tools"]
)

Part 5: Manually Updating the State

In the previous section, we showed how to interrupt a graph so that a human could inspect its actions. This lets the human read the state, but if they want to change their agent's course, they'll need to have write access.

Thankfully, LangGraph lets you manually update state! Updating the state lets you control the agent's trajectory by modifying its actions (even modifying the past!). This capability is particularly useful when you want to correct the agent's mistakes, explore alternative paths, or guide the agent towards a specific goal.

We'll show how to update a checkpointed state below. As before, first, define your graph. We'll reuse the exact same graph as before.

In [17]:

from typing import Annotated

from langchain_anthropic import ChatAnthropic
from langchain_community.tools.tavily_search import TavilySearchResults
from typing_extensions import TypedDict

from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import StateGraph, START
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition


class State(TypedDict):
    messages: Annotated[list, add_messages]


graph_builder = StateGraph(State)


tool = TavilySearchResults(max_results=2)
tools = [tool]
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
llm_with_tools = llm.bind_tools(tools)


def chatbot(state: State):
    return {"messages": [llm_with_tools.invoke(state["messages"])]}


graph_builder.add_node("chatbot", chatbot)

tool_node = ToolNode(tools=[tool])
graph_builder.add_node("tools", tool_node)

graph_builder.add_conditional_edges(
    "chatbot",
    tools_condition,
)
graph_builder.add_edge("tools", "chatbot")
graph_builder.add_edge(START, "chatbot")
memory = MemorySaver()
graph = graph_builder.compile(
    checkpointer=memory,
    # This is new!
    interrupt_before=["tools"],
    # Note: can also interrupt **after** actions, if desired.
    # interrupt_after=["tools"]
)

user_input = "I'm learning LangGraph. Could you do some research on it for me?"
config = {"configurable": {"thread_id": "1"}}
# The config is the **second positional argument** to stream() or invoke()!
events = graph.stream({"messages": [("user", user_input)]}, config)
for event in events:
    if "messages" in event:
        event["messages"][-1].pretty_print()

In [35]:

snapshot = graph.get_state(config)
existing_message = snapshot.values["messages"][-1]
existing_message.pretty_print()

================================== Ai Message ==================================

[{'text': "Certainly! I'd be happy to research LangGraph for you. To get the most up-to-date and comprehensive information, I'll use the Tavily search engine to look this up. Let me do that for you now.", 'type': 'text'}, {'id': 'toolu_018YcbFR37CG8RRXnavH5fxZ', 'input': {'query': 'LangGraph: what is it, how is it used in AI development'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Tool Calls:
  tavily_search_results_json (toolu_018YcbFR37CG8RRXnavH5fxZ)
 Call ID: toolu_018YcbFR37CG8RRXnavH5fxZ
  Args:
    query: LangGraph: what is it, how is it used in AI development

So far, all of this is an exact repeat of the previous section. The LLM just requested to use the search engine tool and our graph was interrupted. If we proceed as before, the tool will be called to search the web.

But what if the user wants to intercede? What if we think the chat bot doesn't need to use the tool?

Let's directly provide the correct response!

In [36]:

from langchain_core.messages import AIMessage, ToolMessage

answer = (
    "LangGraph is a library for building stateful, multi-actor applications with LLMs."
)
new_messages = [
    # The LLM API expects some ToolMessage to match its tool call. We'll satisfy that here.
    ToolMessage(content=answer, tool_call_id=existing_message.tool_calls[0]["id"]),
    # And then directly "put words in the LLM's mouth" by populating its response.
    AIMessage(content=answer),
]

new_messages[-1].pretty_print()
graph.update_state(
    # Which state to update
    config,
    # The updated values to provide. The messages in our `State` are "append-only", meaning this will be appended
    # to the existing state. We will review how to update existing messages in the next section!
    {"messages": new_messages},
)

print("\n\nLast 2 messages;")
print(graph.get_state(config).values["messages"][-2:])

================================== Ai Message ==================================

LangGraph is a library for building stateful, multi-actor applications with LLMs.


Last 2 messages;
[ToolMessage(content='LangGraph is a library for building stateful, multi-actor applications with LLMs.', id='675f7618-367f-44b7-b80e-2834afb02ac5', tool_call_id='toolu_018YcbFR37CG8RRXnavH5fxZ'), AIMessage(content='LangGraph is a library for building stateful, multi-actor applications with LLMs.', additional_kwargs={}, response_metadata={}, id='35fd5682-0c2a-4200-b192-71c59ac6d412')]

Now the graph is complete, since we've provided the final response message! Since state updates simulate a graph step, they even generate corresponding traces. Inspec the LangSmith trace of the update_state call above to see what's going on.

Notice that our new messages are appended to the messages already in the state. Remember how we defined the State type?

class State(TypedDict):
    messages: Annotated[list, add_messages]

We annotated messages with the pre-built add_messages function. This instructs the graph to always append values to the existing list, rather than overwriting the list directly. The same logic is applied here, so the messages we passed to update_state were appended in the same way!

The update_state function operates as if it were one of the nodes in your graph! By default, the update operation uses the node that was last executed, but you can manually specify it below. Let's add an update and tell the graph to treat it as if it came from the "chatbot".

In [19]:

graph.update_state(
    config,
    {"messages": [AIMessage(content="I'm an AI expert!")]},
    # Which node for this function to act as. It will automatically continue
    # processing as if this node just ran.
    as_node="chatbot",
)

Out [19]:

{'configurable': {'thread_id': '1',
  'checkpoint_ns': '',
  'checkpoint_id': '1ef7d134-3958-6412-8002-3f4b4112062f'}}

Check out the LangSmith trace for this update call at the provided link. Notice from the trace that the graph continues into the tools_condition edge. We just told the graph to treat the update as_node="chatbot". If we follow the diagram below and start from the chatbot node, we naturally end up in the tools_condition edge and then __end__ since our updated message lacks tool calls.

In [38]:

from IPython.display import Image, display

try:
    display(Image(graph.get_graph().draw_mermaid_png()))
except Exception:
    # This requires some extra dependencies and is optional
    pass

Inspect the current state as before to confirm the checkpoint reflects our manual updates.

In [39]:

snapshot = graph.get_state(config)
print(snapshot.values["messages"][-3:])
print(snapshot.next)

[ToolMessage(content='LangGraph is a library for building stateful, multi-actor applications with LLMs.', id='675f7618-367f-44b7-b80e-2834afb02ac5', tool_call_id='toolu_018YcbFR37CG8RRXnavH5fxZ'), AIMessage(content='LangGraph is a library for building stateful, multi-actor applications with LLMs.', additional_kwargs={}, response_metadata={}, id='35fd5682-0c2a-4200-b192-71c59ac6d412'), AIMessage(content="I'm an AI expert!", additional_kwargs={}, response_metadata={}, id='288e2f74-f1cb-4082-8c3c-af4695c83117')]
()

Notice: that we've continued to add AI messages to the state. Since we are acting as the chatbot and responding with an AIMessage that doesn't contain tool_calls, the graph knows that it has entered a finished state (next is empty).

What if you want to overwrite existing messages?

The add_messages function we used to annotate our graph's State above controls how updates are made to the messages key. This function looks at any message IDs in the new messages list. If the ID matches a message in the existing state, add_messages overwrites the existing message with the new content.

As an example, let's update the tool invocation to make sure we get good results from our search engine! First, start a new thread:

In [40]:

user_input = "I'm learning LangGraph. Could you do some research on it for me?"
config = {"configurable": {"thread_id": "2"}}  # we'll use thread_id = 2 here
events = graph.stream(
    {"messages": [("user", user_input)]}, config, stream_mode="values"
)
for event in events:
    if "messages" in event:
        event["messages"][-1].pretty_print()

================================ Human Message =================================

I'm learning LangGraph. Could you do some research on it for me?
================================== Ai Message ==================================

[{'text': "Certainly! I'd be happy to research LangGraph for you. To get the most up-to-date and accurate information, I'll use the Tavily search engine to look this up. Let me do that for you now.", 'type': 'text'}, {'id': 'toolu_01TfAeisrpx4ddgJpoAxqrVh', 'input': {'query': 'LangGraph framework for language models'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Tool Calls:
  tavily_search_results_json (toolu_01TfAeisrpx4ddgJpoAxqrVh)
 Call ID: toolu_01TfAeisrpx4ddgJpoAxqrVh
  Args:
    query: LangGraph framework for language models

Next, let's update the tool invocation for our agent. Maybe we want to search for human-in-the-loop workflows in particular.

In [41]:

from langchain_core.messages import AIMessage

snapshot = graph.get_state(config)
existing_message = snapshot.values["messages"][-1]
print("Original")
print("Message ID", existing_message.id)
print(existing_message.tool_calls[0])
new_tool_call = existing_message.tool_calls[0].copy()
new_tool_call["args"]["query"] = "LangGraph human-in-the-loop workflow"
new_message = AIMessage(
    content=existing_message.content,
    tool_calls=[new_tool_call],
    # Important! The ID is how LangGraph knows to REPLACE the message in the state rather than APPEND this messages
    id=existing_message.id,
)

print("Updated")
print(new_message.tool_calls[0])
print("Message ID", new_message.id)
graph.update_state(config, {"messages": [new_message]})

print("\n\nTool calls")
graph.get_state(config).values["messages"][-1].tool_calls

Out [41]:

Original
Message ID run-342f3f54-356b-4cc1-b747-573f6aa31054-0
{'name': 'tavily_search_results_json', 'args': {'query': 'LangGraph framework for language models'}, 'id': 'toolu_01TfAeisrpx4ddgJpoAxqrVh', 'type': 'tool_call'}
Updated
{'name': 'tavily_search_results_json', 'args': {'query': 'LangGraph human-in-the-loop workflow'}, 'id': 'toolu_01TfAeisrpx4ddgJpoAxqrVh', 'type': 'tool_call'}
Message ID run-342f3f54-356b-4cc1-b747-573f6aa31054-0


Tool calls

[{'name': 'tavily_search_results_json',
  'args': {'query': 'LangGraph human-in-the-loop workflow'},
  'id': 'toolu_01TfAeisrpx4ddgJpoAxqrVh',
  'type': 'tool_call'}]

Notice that we've modified the AI's tool invocation to search for "LangGraph human-in-the-loop workflow" instead of the simple "LangGraph".

Check out the LangSmith trace to see the state update call - you can see our new message has successfully updated the previous AI message.

Resume the graph by streaming with an input of None and the existing config.

In [42]:

events = graph.stream(None, config, stream_mode="values")
for event in events:
    if "messages" in event:
        event["messages"][-1].pretty_print()

================================== Ai Message ==================================

[{'text': "Certainly! I'd be happy to research LangGraph for you. To get the most up-to-date and accurate information, I'll use the Tavily search engine to look this up. Let me do that for you now.", 'type': 'text'}, {'id': 'toolu_01TfAeisrpx4ddgJpoAxqrVh', 'input': {'query': 'LangGraph framework for language models'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Tool Calls:
  tavily_search_results_json (toolu_01TfAeisrpx4ddgJpoAxqrVh)
 Call ID: toolu_01TfAeisrpx4ddgJpoAxqrVh
  Args:
    query: LangGraph human-in-the-loop workflow
================================= Tool Message =================================
Name: tavily_search_results_json

[{"url": "https://www.youtube.com/watch?v=9BPCV5TYPmg", "content": "In this video, I'll show you how to handle persistence with LangGraph, enabling a unique Human-in-the-Loop workflow. This approach allows a human to grant an..."}, {"url": "https://medium.com/@kbdhunga/implementing-human-in-the-loop-with-langgraph-ccfde023385c", "content": "Implementing a Human-in-the-Loop (HIL) framework in LangGraph with the Streamlit app provides a robust mechanism for user engagement and decision-making. By incorporating breakpoints and ..."}]
================================== Ai Message ==================================

Thank you for your patience. I've found some information about LangGraph, particularly focusing on its human-in-the-loop workflow capabilities. Let me summarize what I've learned for you:

1. LangGraph Overview:
   LangGraph is a framework for building stateful, multi-actor applications with Large Language Models (LLMs). It's particularly useful for creating complex, interactive AI systems.

2. Human-in-the-Loop (HIL) Workflow:
   One of the key features of LangGraph is its support for human-in-the-loop workflows. This means that it allows for human intervention and decision-making within AI-driven processes.

3. Persistence Handling:
   LangGraph offers capabilities for handling persistence, which is crucial for maintaining state across interactions in a workflow.

4. Implementation with Streamlit:
   There are examples of implementing LangGraph's human-in-the-loop functionality using Streamlit, a popular Python library for creating web apps. This combination allows for the creation of interactive user interfaces for AI applications.

5. Breakpoints and User Engagement:
   LangGraph allows the incorporation of breakpoints in the workflow. These breakpoints are points where the system can pause and wait for human input or decision-making, enhancing user engagement and control over the AI process.

6. Decision-Making Mechanism:
   The human-in-the-loop framework in LangGraph provides a robust mechanism for integrating user decision-making into AI workflows. This is particularly useful in scenarios where human judgment or expertise is needed to guide or validate AI actions.

7. Flexibility and Customization:
   From the information available, it seems that LangGraph offers flexibility in how human-in-the-loop processes are implemented, allowing developers to customize the interaction points and the nature of human involvement based on their specific use case.

LangGraph appears to be a powerful tool for developers looking to create more interactive and controllable AI applications, especially those that benefit from human oversight or input at crucial stages of the process.

Would you like me to research any specific aspect of LangGraph in more detail, or do you have any questions about what I've found so far?

Check out the trace to see the tool call and later LLM response. Notice that now the graph queries the search engine using our updated query term - we were able to manually override the LLM's search here!

All of this is reflected in the graph's checkpointed memory, meaning if we continue the conversation, it will recall all the modified state.

In [43]:

events = graph.stream(
    {
        "messages": (
            "user",
            "Remember what I'm learning about?",
        )
    },
    config,
    stream_mode="values",
)
for event in events:
    if "messages" in event:
        event["messages"][-1].pretty_print()

================================ Human Message =================================

Remember what I'm learning about?
================================== Ai Message ==================================

I apologize for my oversight. You're absolutely right to remind me. You mentioned that you're learning LangGraph. Thank you for bringing that back into focus. 

Since you're in the process of learning LangGraph, it would be helpful to know more about your current level of understanding and what specific aspects of LangGraph you're most interested in or finding challenging. This way, I can provide more targeted information or explanations that align with your learning journey.

Are there any particular areas of LangGraph you'd like to explore further? For example:

1. Basic concepts and architecture of LangGraph
2. Setting up and getting started with LangGraph
3. Implementing specific features like the human-in-the-loop workflow
4. Best practices for using LangGraph in projects
5. Comparisons with other similar frameworks

Or if you have any specific questions about what you've learned so far, I'd be happy to help clarify or expand on those topics. Please let me know what would be most useful for your learning process.

Congratulations! You've used interrupt_before and update_state to manually modify the state as a part of a human-in-the-loop workflow. Interruptions and state modifications let you control how the agent behaves. Combined with persistent checkpointing, it means you can pause an action and resume at any point. Your user doesn't have to be available when the graph interrupts!

The graph code for this section is identical to previous ones. The key snippets to remember are to add .compile(..., interrupt_before=[...]) (or interrupt_after) if you want to explicitly pause the graph whenever it reaches a node. Then you can use update_state to modify the checkpoint and control how the graph should proceed.

Part 6: Customizing State

So far, we've relied on a simple state (it's just a list of messages!). You can go far with this simple state, but if you want to define complex behavior without relying on the message list, you can add additional fields to the state. In this section, we will extend our chat bot with a new node to illustrate this.

In the examples above, we involved a human deterministically: the graph always interrupted whenever a tool was invoked. Suppose we wanted our chat bot to have the choice of relying on a human.

One way to do this is to create a passthrough "human" node, before which the graph will always stop. We will only execute this node if the LLM invokes a "human" tool. For our convenience, we will include an "ask_human" flag in our graph state that we will flip if the LLM calls this tool.

Below, define this new graph, with an updated State

In [54]:

from typing import Annotated

from langchain_anthropic import ChatAnthropic
from langchain_community.tools.tavily_search import TavilySearchResults
from typing_extensions import TypedDict

from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import StateGraph, START
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition


class State(TypedDict):
    messages: Annotated[list, add_messages]
    # This flag is new
    ask_human: bool

Next, define a schema to show the model to let it decide to request assistance.

Using Pydantic with LangChain

This notebook uses Pydantic v2 BaseModel, which requires langchain-core >= 0.3. Using langchain-core < 0.3 will result in errors due to mixing of Pydantic v1 and v2 BaseModels.

In [55]:

from pydantic import BaseModel


class RequestAssistance(BaseModel):
    """Escalate the conversation to an expert. Use this if you are unable to assist directly or if the user requires support beyond your permissions.

    To use this function, relay the user's 'request' so the expert can provide the right guidance.
    """

    request: str

Next, define the chatbot node. The primary modification here is flip the ask_human flag if we see that the chat bot has invoked the RequestAssistance flag.

In [56]:

tool = TavilySearchResults(max_results=2)
tools = [tool]
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
# We can bind the llm to a tool definition, a pydantic model, or a json schema
llm_with_tools = llm.bind_tools(tools + [RequestAssistance])


def chatbot(state: State):
    response = llm_with_tools.invoke(state["messages"])
    ask_human = False
    if (
        response.tool_calls
        and response.tool_calls[0]["name"] == RequestAssistance.__name__
    ):
        ask_human = True
    return {"messages": [response], "ask_human": ask_human}

Next, create the graph builder and add the chatbot and tools nodes to the graph, same as before.

In [ ]:

graph_builder = StateGraph(State)

graph_builder.add_node("chatbot", chatbot)
graph_builder.add_node("tools", ToolNode(tools=[tool]))

Next, create the "human" node. This node function is mostly a placeholder in our graph that will trigger an interrupt. If the human does not manually update the state during the interrupt, it inserts a tool message so the LLM knows the user was requested but didn't respond. This node also unsets the ask_human flag so the graph knows not to revisit the node unless further requests are made.

In [ ]:

from langchain_core.messages import AIMessage, ToolMessage


def create_response(response: str, ai_message: AIMessage):
    return ToolMessage(
        content=response,
        tool_call_id=ai_message.tool_calls[0]["id"],
    )


def human_node(state: State):
    new_messages = []
    if not isinstance(state["messages"][-1], ToolMessage):
        # Typically, the user will have updated the state during the interrupt.
        # If they choose not to, we will include a placeholder ToolMessage to
        # let the LLM continue.
        new_messages.append(
            create_response("No response from human.", state["messages"][-1])
        )
    return {
        # Append the new messages
        "messages": new_messages,
        # Unset the flag
        "ask_human": False,
    }


graph_builder.add_node("human", human_node)

Next, define the conditional logic. The select_next_node will route to the human node if the flag is set. Otherwise, it lets the prebuilt tools_condition function choose the next node.

Recall that the tools_condition function simply checks to see if the chatbot has responded with any tool_calls in its response message. If so, it routes to the action node. Otherwise, it ends the graph.

In [ ]:

def select_next_node(state: State):
    if state["ask_human"]:
        return "human"
    # Otherwise, we can route as before
    return tools_condition(state)


graph_builder.add_conditional_edges(
    "chatbot",
    select_next_node,
    {"human": "human", "tools": "tools", END: END},
)

Warning:

Output truncated. This notebook contains too many cells to display efficiently.