Graph API 사용하기

from langchain.messages import AnyMessage
from typing_extensions import TypedDict

class State(TypedDict):
    messages: list[AnyMessage]
    extra_field: int

from langchain.messages import AIMessage

def node(state: State):
    messages = state["messages"]
    new_message = AIMessage("Hello!")
    return {"messages": messages + [new_message], "extra_field": 10}

from langgraph.graph import StateGraph

builder = StateGraph(State)
builder.add_node(node)
builder.set_entry_point("node")
graph = builder.compile()

from IPython.display import Image, display

display(Image(graph.get_graph().draw_mermaid_png()))

from langchain.messages import HumanMessage

result = graph.invoke({"messages": [HumanMessage("Hi")]})
result

{'messages': [HumanMessage(content='Hi'), AIMessage(content='Hello!')], 'extra_field': 10}

for message in result["messages"]:
    message.pretty_print()

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

Hi
================================== Ai Message ==================================

Hello!

from typing_extensions import Annotated

def add(left, right):
    """Can also import `add` from the `operator` built-in."""
    return left + right

class State(TypedDict):
    messages: Annotated[list[AnyMessage], add]  
    extra_field: int

def node(state: State):
    new_message = AIMessage("Hello!")
    return {"messages": [new_message], "extra_field": 10}  

from langgraph.graph import START

graph = StateGraph(State).add_node(node).add_edge(START, "node").compile()

result = graph.invoke({"messages": [HumanMessage("Hi")]})

for message in result["messages"]:
    message.pretty_print()

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

Hi
================================== Ai Message ==================================

Hello!

from langgraph.graph.message import add_messages

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

def node(state: State):
    new_message = AIMessage("Hello!")
    return {"messages": [new_message], "extra_field": 10}

graph = StateGraph(State).add_node(node).set_entry_point("node").compile()

input_message = {"role": "user", "content": "Hi"}  

result = graph.invoke({"messages": [input_message]})

for message in result["messages"]:
    message.pretty_print()

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

Hi
================================== Ai Message ==================================

Hello!

from langgraph.graph import MessagesState

class State(MessagesState):
    extra_field: int

from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict

# Define the schema for the input
class InputState(TypedDict):
    question: str

# Define the schema for the output
class OutputState(TypedDict):
    answer: str

# Define the overall schema, combining both input and output
class OverallState(InputState, OutputState):
    pass

# Define the node that processes the input and generates an answer
def answer_node(state: InputState):
    # Example answer and an extra key
    return {"answer": "bye", "question": state["question"]}

# Build the graph with input and output schemas specified
builder = StateGraph(OverallState, input_schema=InputState, output_schema=OutputState)
builder.add_node(answer_node)  # Add the answer node
builder.add_edge(START, "answer_node")  # Define the starting edge
builder.add_edge("answer_node", END)  # Define the ending edge
graph = builder.compile()  # Compile the graph

# Invoke the graph with an input and print the result
print(graph.invoke({"question": "hi"}))

{'answer': 'bye'}

from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict

# The overall state of the graph (this is the public state shared across nodes)
class OverallState(TypedDict):
    a: str

# Output from node_1 contains private data that is not part of the overall state
class Node1Output(TypedDict):
    private_data: str

# The private data is only shared between node_1 and node_2
def node_1(state: OverallState) -> Node1Output:
    output = {"private_data": "set by node_1"}
    print(f"Entered node `node_1`:\n\tInput: {state}.\n\tReturned: {output}")
    return output

# Node 2 input only requests the private data available after node_1
class Node2Input(TypedDict):
    private_data: str

def node_2(state: Node2Input) -> OverallState:
    output = {"a": "set by node_2"}
    print(f"Entered node `node_2`:\n\tInput: {state}.\n\tReturned: {output}")
    return output

# Node 3 only has access to the overall state (no access to private data from node_1)
def node_3(state: OverallState) -> OverallState:
    output = {"a": "set by node_3"}
    print(f"Entered node `node_3`:\n\tInput: {state}.\n\tReturned: {output}")
    return output

# Connect nodes in a sequence
# node_2 accepts private data from node_1, whereas
# node_3 does not see the private data.
builder = StateGraph(OverallState).add_sequence([node_1, node_2, node_3])
builder.add_edge(START, "node_1")
graph = builder.compile()

# Invoke the graph with the initial state
response = graph.invoke(
    {
        "a": "set at start",
    }
)

print()
print(f"Output of graph invocation: {response}")

Entered node `node_1`:
    ut: {'a': 'set at start'}.
    urned: {'private_data': 'set by node_1'}
Entered node `node_2`:
    ut: {'private_data': 'set by node_1'}.
    urned: {'a': 'set by node_2'}
Entered node `node_3`:
    ut: {'a': 'set by node_2'}.
    urned: {'a': 'set by node_3'}

Output of graph invocation: {'a': 'set by node_3'}

from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
from pydantic import BaseModel

# The overall state of the graph (this is the public state shared across nodes)
class OverallState(BaseModel):
    a: str

def node(state: OverallState):
    return {"a": "goodbye"}

# Build the state graph
builder = StateGraph(OverallState)
builder.add_node(node)  # node_1 is the first node
builder.add_edge(START, "node")  # Start the graph with node_1
builder.add_edge("node", END)  # End the graph after node_1
graph = builder.compile()

# Test the graph with a valid input
graph.invoke({"a": "hello"})

try:
    graph.invoke({"a": 123})  # Should be a string
except Exception as e:
    print("An exception was raised because `a` is an integer rather than a string.")
    print(e)

An exception was raised because `a` is an integer rather than a string.
1 validation error for OverallState
a
  Input should be a valid string [type=string_type, input_value=123, input_type=int]
    For further information visit https://errors.pydantic.dev/2.9/v/string_type

from langgraph.graph import END, StateGraph, START
from langgraph.runtime import Runtime
from typing_extensions import TypedDict

# 1. Specify config schema
class ContextSchema(TypedDict):
    my_runtime_value: str

# 2. Define a graph that accesses the config in a node
class State(TypedDict):
    my_state_value: str

def node(state: State, runtime: Runtime[ContextSchema]):  
    if runtime.context["my_runtime_value"] == "a":  
        return {"my_state_value": 1}
    elif runtime.context["my_runtime_value"] == "b":  
        return {"my_state_value": 2}
    else:
        raise ValueError("Unknown values.")

builder = StateGraph(State, context_schema=ContextSchema)  
builder.add_node(node)
builder.add_edge(START, "node")
builder.add_edge("node", END)

graph = builder.compile()

# 3. Pass in configuration at runtime:
print(graph.invoke({}, context={"my_runtime_value": "a"}))  
print(graph.invoke({}, context={"my_runtime_value": "b"}))  

{'my_state_value': 1}
{'my_state_value': 2}

from langgraph.types import RetryPolicy

builder.add_node(
    "node_name",
    node_function,
    retry_policy=RetryPolicy(),
)

from langgraph.types import CachePolicy

builder.add_node(
    "node_name",
    node_function,
    cache_policy=CachePolicy(ttl=120),
)

from langgraph.cache.memory import InMemoryCache

graph = builder.compile(cache=InMemoryCache())

from langgraph.graph import START, StateGraph

builder = StateGraph(State)

# Add nodes
builder.add_node(step_1)
builder.add_node(step_2)
builder.add_node(step_3)

# Add edges
builder.add_edge(START, "step_1")
builder.add_edge("step_1", "step_2")
builder.add_edge("step_2", "step_3")

builder = StateGraph(State).add_sequence([step_1, step_2, step_3])
builder.add_edge(START, "step_1")

import operator
from typing import Annotated, Any
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END

class State(TypedDict):
    # The operator.add reducer fn makes this append-only
    aggregate: Annotated[list, operator.add]

def a(state: State):
    print(f'Adding "A" to {state["aggregate"]}')
    return {"aggregate": ["A"]}

def b(state: State):
    print(f'Adding "B" to {state["aggregate"]}')
    return {"aggregate": ["B"]}

def c(state: State):
    print(f'Adding "C" to {state["aggregate"]}')
    return {"aggregate": ["C"]}

def d(state: State):
    print(f'Adding "D" to {state["aggregate"]}')
    return {"aggregate": ["D"]}

builder = StateGraph(State)
builder.add_node(a)
builder.add_node(b)
builder.add_node(c)
builder.add_node(d)
builder.add_edge(START, "a")
builder.add_edge("a", "b")
builder.add_edge("a", "c")
builder.add_edge("b", "d")
builder.add_edge("c", "d")
builder.add_edge("d", END)
graph = builder.compile()

from IPython.display import Image, display

display(Image(graph.get_graph().draw_mermaid_png()))

graph.invoke({"aggregate": []}, {"configurable": {"thread_id": "foo"}})

Adding "A" to []
Adding "B" to ['A']
Adding "C" to ['A']
Adding "D" to ['A', 'B', 'C']

import operator
from typing import Annotated, Any
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END

class State(TypedDict):
    # The operator.add reducer fn makes this append-only
    aggregate: Annotated[list, operator.add]

def a(state: State):
    print(f'Adding "A" to {state["aggregate"]}')
    return {"aggregate": ["A"]}

def b(state: State):
    print(f'Adding "B" to {state["aggregate"]}')
    return {"aggregate": ["B"]}

def b_2(state: State):
    print(f'Adding "B_2" to {state["aggregate"]}')
    return {"aggregate": ["B_2"]}

def c(state: State):
    print(f'Adding "C" to {state["aggregate"]}')
    return {"aggregate": ["C"]}

def d(state: State):
    print(f'Adding "D" to {state["aggregate"]}')
    return {"aggregate": ["D"]}

builder = StateGraph(State)
builder.add_node(a)
builder.add_node(b)
builder.add_node(b_2)
builder.add_node(c)
builder.add_node(d, defer=True)  
builder.add_edge(START, "a")
builder.add_edge("a", "b")
builder.add_edge("a", "c")
builder.add_edge("b", "b_2")
builder.add_edge("b_2", "d")
builder.add_edge("c", "d")
builder.add_edge("d", END)
graph = builder.compile()

from IPython.display import Image, display

display(Image(graph.get_graph().draw_mermaid_png()))

graph.invoke({"aggregate": []})

Adding "A" to []
Adding "B" to ['A']
Adding "C" to ['A']
Adding "B_2" to ['A', 'B', 'C']
Adding "D" to ['A', 'B', 'C', 'B_2']

import operator
from typing import Annotated, Literal, Sequence
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END

class State(TypedDict):
    aggregate: Annotated[list, operator.add]
    # Add a key to the state. We will set this key to determine
    # how we branch.
    which: str

def a(state: State):
    print(f'Adding "A" to {state["aggregate"]}')
    return {"aggregate": ["A"], "which": "c"}  

def b(state: State):
    print(f'Adding "B" to {state["aggregate"]}')
    return {"aggregate": ["B"]}

def c(state: State):
    print(f'Adding "C" to {state["aggregate"]}')
    return {"aggregate": ["C"]}

builder = StateGraph(State)
builder.add_node(a)
builder.add_node(b)
builder.add_node(c)
builder.add_edge(START, "a")
builder.add_edge("b", END)
builder.add_edge("c", END)

def conditional_edge(state: State) -> Literal["b", "c"]:
    # Fill in arbitrary logic here that uses the state
    # to determine the next node
    return state["which"]

builder.add_conditional_edges("a", conditional_edge)  

graph = builder.compile()

from IPython.display import Image, display

display(Image(graph.get_graph().draw_mermaid_png()))

result = graph.invoke({"aggregate": []})
print(result)

Adding "A" to []
Adding "C" to ['A']
{'aggregate': ['A', 'C'], 'which': 'c'}

from langgraph.graph import StateGraph, START, END
from langgraph.types import Send
from typing_extensions import TypedDict, Annotated
import operator

class OverallState(TypedDict):
    topic: str
    subjects: list[str]
    jokes: Annotated[list[str], operator.add]
    best_selected_joke: str

def generate_topics(state: OverallState):
    return {"subjects": ["lions", "elephants", "penguins"]}

def generate_joke(state: OverallState):
    joke_map = {
        "lions": "Why don't lions like fast food? Because they can't catch it!",
        "elephants": "Why don't elephants use computers? They're afraid of the mouse!",
        "penguins": "Why don't penguins like talking to strangers at parties? Because they find it hard to break the ice."
    }
    return {"jokes": [joke_map[state["subject"]]]}

def continue_to_jokes(state: OverallState):
    return [Send("generate_joke", {"subject": s}) for s in state["subjects"]]

def best_joke(state: OverallState):
    return {"best_selected_joke": "penguins"}

builder = StateGraph(OverallState)
builder.add_node("generate_topics", generate_topics)
builder.add_node("generate_joke", generate_joke)
builder.add_node("best_joke", best_joke)
builder.add_edge(START, "generate_topics")
builder.add_conditional_edges("generate_topics", continue_to_jokes, ["generate_joke"])
builder.add_edge("generate_joke", "best_joke")
builder.add_edge("best_joke", END)
graph = builder.compile()

from IPython.display import Image, display

display(Image(graph.get_graph().draw_mermaid_png()))

# Call the graph: here we call it to generate a list of jokes
for step in graph.stream({"topic": "animals"}):
    print(step)

{'generate_topics': {'subjects': ['lions', 'elephants', 'penguins']}}
{'generate_joke': {'jokes': ["Why don't lions like fast food? Because they can't catch it!"]}}
{'generate_joke': {'jokes': ["Why don't elephants use computers? They're afraid of the mouse!"]}}
{'generate_joke': {'jokes': ['Why don't penguins like talking to strangers at parties? Because they find it hard to break the ice.']}}
{'best_joke': {'best_selected_joke': 'penguins'}}

builder = StateGraph(State)
builder.add_node(a)
builder.add_node(b)

def route(state: State) -> Literal["b", END]:
    if termination_condition(state):
        return END
    else:
        return "b"

builder.add_edge(START, "a")
builder.add_conditional_edges("a", route)
builder.add_edge("b", "a")
graph = builder.compile()

from langgraph.errors import GraphRecursionError

try:
    graph.invoke(inputs, {"recursion_limit": 3})
except GraphRecursionError:
    print("Recursion Error")

import operator
from typing import Annotated, Literal
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END

class State(TypedDict):
    # The operator.add reducer fn makes this append-only
    aggregate: Annotated[list, operator.add]

def a(state: State):
    print(f'Node A sees {state["aggregate"]}')
    return {"aggregate": ["A"]}

def b(state: State):
    print(f'Node B sees {state["aggregate"]}')
    return {"aggregate": ["B"]}

# Define nodes
builder = StateGraph(State)
builder.add_node(a)
builder.add_node(b)

# Define edges
def route(state: State) -> Literal["b", END]:
    if len(state["aggregate"]) < 7:
        return "b"
    else:
        return END

builder.add_edge(START, "a")
builder.add_conditional_edges("a", route)
builder.add_edge("b", "a")
graph = builder.compile()

from IPython.display import Image, display

display(Image(graph.get_graph().draw_mermaid_png()))

graph.invoke({"aggregate": []})

Node A sees []
Node B sees ['A']
Node A sees ['A', 'B']
Node B sees ['A', 'B', 'A']
Node A sees ['A', 'B', 'A', 'B']
Node B sees ['A', 'B', 'A', 'B', 'A']
Node A sees ['A', 'B', 'A', 'B', 'A', 'B']

from langgraph.errors import GraphRecursionError

try:
    graph.invoke({"aggregate": []}, {"recursion_limit": 4})
except GraphRecursionError:
    print("Recursion Error")

Node A sees []
Node B sees ['A']
Node C sees ['A', 'B']
Node D sees ['A', 'B']
Node A sees ['A', 'B', 'C', 'D']
Recursion Error

from langchain.chat_models import init_chat_model
from langgraph.graph import MessagesState, StateGraph

async def node(state: MessagesState):  
    new_message = await llm.ainvoke(state["messages"])  
    return {"messages": [new_message]}

builder = StateGraph(MessagesState).add_node(node).set_entry_point("node")
graph = builder.compile()

input_message = {"role": "user", "content": "Hello"}
result = await graph.ainvoke({"messages": [input_message]})  

def my_node(state: State) -> Command[Literal["my_other_node"]]:
    return Command(
        # state update
        update={"foo": "bar"},
        # control flow
        goto="my_other_node"
    )

import random
from typing_extensions import TypedDict, Literal
from langgraph.graph import StateGraph, START
from langgraph.types import Command

# Define graph state
class State(TypedDict):
    foo: str

# Define the nodes

def node_a(state: State) -> Command[Literal["node_b", "node_c"]]:
    print("Called A")
    value = random.choice(["b", "c"])
    # this is a replacement for a conditional edge function
    if value == "b":
        goto = "node_b"
    else:
        goto = "node_c"

    # note how Command allows you to BOTH update the graph state AND route to the next node
    return Command(
        # this is the state update
        update={"foo": value},
        # this is a replacement for an edge
        goto=goto,
    )

def node_b(state: State):
    print("Called B")
    return {"foo": state["foo"] + "b"}

def node_c(state: State):
    print("Called C")
    return {"foo": state["foo"] + "c"}

builder = StateGraph(State)
builder.add_edge(START, "node_a")
builder.add_node(node_a)
builder.add_node(node_b)
builder.add_node(node_c)
# NOTE: there are no edges between nodes A, B and C!

graph = builder.compile()

from IPython.display import display, Image

display(Image(graph.get_graph().draw_mermaid_png()))

graph.invoke({"foo": ""})

Called A
Called C

def my_node(state: State) -> Command[Literal["my_other_node"]]:
    return Command(
        update={"foo": "bar"},
        goto="other_subgraph",  # where `other_subgraph` is a node in the parent graph
        graph=Command.PARENT
    )

import operator
from typing_extensions import Annotated

class State(TypedDict):
    # NOTE: we define a reducer here
    foo: Annotated[str, operator.add]  

def node_a(state: State):
    print("Called A")
    value = random.choice(["a", "b"])
    # this is a replacement for a conditional edge function
    if value == "a":
        goto = "node_b"
    else:
        goto = "node_c"

    # note how Command allows you to BOTH update the graph state AND route to the next node
    return Command(
        update={"foo": value},
        goto=goto,
        # this tells LangGraph to navigate to node_b or node_c in the parent graph
        # NOTE: this will navigate to the closest parent graph relative to the subgraph
        graph=Command.PARENT,  
    )

subgraph = StateGraph(State).add_node(node_a).add_edge(START, "node_a").compile()

def node_b(state: State):
    print("Called B")
    # NOTE: since we've defined a reducer, we don't need to manually append
    # new characters to existing 'foo' value. instead, reducer will append these
    # automatically (via operator.add)
    return {"foo": "b"}  

def node_c(state: State):
    print("Called C")
    return {"foo": "c"}  

builder = StateGraph(State)
builder.add_edge(START, "subgraph")
builder.add_node("subgraph", subgraph)
builder.add_node(node_b)
builder.add_node(node_c)

graph = builder.compile()

graph.invoke({"foo": ""})

Called A
Called C

@tool
def lookup_user_info(tool_call_id: Annotated[str, InjectedToolCallId], config: RunnableConfig):
    """Use this to look up user information to better assist them with their questions."""
    user_info = get_user_info(config.get("configurable", {}).get("user_id"))
    return Command(
        update={
            # update the state keys
            "user_info": user_info,
            # update the message history
            "messages": [ToolMessage("Successfully looked up user information", tool_call_id=tool_call_id)]
        }
    )

import random
from typing import Annotated, Literal
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]

class MyNode:
    def __init__(self, name: str):
        self.name = name
    def __call__(self, state: State):
        return {"messages": [("assistant", f"Called node {self.name}")]}

def route(state) -> Literal["entry_node", END]:
    if len(state["messages"]) > 10:
        return END
    return "entry_node"

def add_fractal_nodes(builder, current_node, level, max_level):
    if level > max_level:
        return
    # Number of nodes to create at this level
    num_nodes = random.randint(1, 3)  # Adjust randomness as needed
    for i in range(num_nodes):
        nm = ["A", "B", "C"][i]
        node_name = f"node_{current_node}_{nm}"
        builder.add_node(node_name, MyNode(node_name))
        builder.add_edge(current_node, node_name)
        # Recursively add more nodes
        r = random.random()
        if r > 0.2 and level + 1 < max_level:
            add_fractal_nodes(builder, node_name, level + 1, max_level)
        elif r > 0.05:
            builder.add_conditional_edges(node_name, route, node_name)
        else:
            # End
            builder.add_edge(node_name, END)

def build_fractal_graph(max_level: int):
    builder = StateGraph(State)
    entry_point = "entry_node"
    builder.add_node(entry_point, MyNode(entry_point))
    builder.add_edge(START, entry_point)
    add_fractal_nodes(builder, entry_point, 1, max_level)
    # Optional: set a finish point if required
    builder.add_edge(entry_point, END)  # or any specific node
    return builder.compile()

app = build_fractal_graph(3)

print(app.get_graph().draw_mermaid())

%%{init: {'flowchart': {'curve': 'linear'}}}%%
graph TD;
    tart__([<p>__start__</p>]):::first
    ry_node(entry_node)
    e_entry_node_A(node_entry_node_A)
    e_entry_node_B(node_entry_node_B)
    e_node_entry_node_B_A(node_node_entry_node_B_A)
    e_node_entry_node_B_B(node_node_entry_node_B_B)
    e_node_entry_node_B_C(node_node_entry_node_B_C)
    nd__([<p>__end__</p>]):::last
    tart__ --> entry_node;
    ry_node --> __end__;
    ry_node --> node_entry_node_A;
    ry_node --> node_entry_node_B;
    e_entry_node_B --> node_node_entry_node_B_A;
    e_entry_node_B --> node_node_entry_node_B_B;
    e_entry_node_B --> node_node_entry_node_B_C;
    e_entry_node_A -.-> entry_node;
    e_entry_node_A -.-> __end__;
    e_node_entry_node_B_A -.-> entry_node;
    e_node_entry_node_B_A -.-> __end__;
    e_node_entry_node_B_B -.-> entry_node;
    e_node_entry_node_B_B -.-> __end__;
    e_node_entry_node_B_C -.-> entry_node;
    e_node_entry_node_B_C -.-> __end__;
    ssDef default fill:#f2f0ff,line-height:1.2
    ssDef first fill-opacity:0
    ssDef last fill:#bfb6fc

from IPython.display import Image, display
from langchain_core.runnables.graph import CurveStyle, MermaidDrawMethod, NodeStyles

display(Image(app.get_graph().draw_mermaid_png()))

import nest_asyncio

nest_asyncio.apply()  # Required for Jupyter Notebook to run async functions

display(
    Image(
        app.get_graph().draw_mermaid_png(
            curve_style=CurveStyle.LINEAR,
            node_colors=NodeStyles(first="#ffdfba", last="#baffc9", default="#fad7de"),
            wrap_label_n_words=9,
            output_file_path=None,
            draw_method=MermaidDrawMethod.PYPPETEER,
            background_color="white",
            padding=10,
        )
    )
)

try:
    display(Image(app.get_graph().draw_png()))
except ImportError:
    print(
        "You likely need to install dependencies for pygraphviz, see more here https://github.com/pygraphviz/pygraphviz/blob/main/INSTALL.txt"
    )