langgraph/tests/test_pregel.py

import json
import operator
import time
import warnings
from collections import Counter
from concurrent.futures import ThreadPoolExecutor
from contextlib import contextmanager
from typing import (
    Annotated,
    Any,
    Dict,
    Generator,
    List,
    Literal,
    Optional,
    Sequence,
    TypedDict,
    Union,
)

import pytest
from langchain_core.runnables import (
    RunnableConfig,
    RunnableLambda,
    RunnableMap,
    RunnablePassthrough,
    RunnablePick,
)
from langsmith import traceable
from pytest_mock import MockerFixture
from syrupy import SnapshotAssertion

from langgraph.channels.binop import BinaryOperatorAggregate
from langgraph.channels.context import Context
from langgraph.channels.last_value import LastValue
from langgraph.channels.topic import Topic
from langgraph.checkpoint.sqlite import SqliteSaver
from langgraph.constants import Send
from langgraph.errors import InvalidUpdateError
from langgraph.graph import END, Graph
from langgraph.graph.graph import START
from langgraph.graph.message import MessageGraph, add_messages
from langgraph.graph.state import StateGraph
from langgraph.managed.few_shot import FewShotExamples
from langgraph.prebuilt.chat_agent_executor import (
    create_function_calling_executor,
    create_tool_calling_executor,
)
from langgraph.prebuilt.tool_node import ToolNode
from langgraph.pregel import Channel, GraphRecursionError, Pregel, StateSnapshot
from langgraph.pregel.retry import RetryPolicy
from langgraph.serde.base import SerializerProtocol
from langgraph.serde.jsonplus import JsonPlusSerializer
from tests.any_str import AnyStr
from tests.memory_assert import (
    MemorySaverAssertCheckpointMetadata,
    MemorySaverAssertImmutable,
    NoopSerializer,
)


def test_graph_validation() -> None:
    def logic(inp: str) -> str:
        return ""

    workflow = Graph()
    workflow.add_node("agent", logic)
    workflow.set_entry_point("agent")
    workflow.set_finish_point("agent")
    assert workflow.compile(), "valid graph"

    # Accept a dead-end
    workflow = Graph()
    workflow.add_node("agent", logic)
    workflow.set_entry_point("agent")
    workflow.compile()

    workflow = Graph()
    workflow.add_node("agent", logic)
    workflow.set_finish_point("agent")
    with pytest.raises(ValueError, match="not reachable"):
        workflow.compile()

    workflow = Graph()
    workflow.add_node("agent", logic)
    workflow.add_node("tools", logic)
    workflow.set_entry_point("agent")
    workflow.add_conditional_edges("agent", logic, {"continue": "tools", "exit": END})
    workflow.add_edge("tools", "agent")
    assert workflow.compile(), "valid graph"

    workflow = Graph()
    workflow.add_node("agent", logic)
    workflow.add_node("tools", logic)
    workflow.set_entry_point("tools")
    workflow.add_conditional_edges("agent", logic, {"continue": "tools", "exit": END})
    workflow.add_edge("tools", "agent")
    assert workflow.compile(), "valid graph"

    workflow = Graph()
    workflow.set_entry_point("tools")
    workflow.add_conditional_edges("agent", logic, {"continue": "tools", "exit": END})
    workflow.add_edge("tools", "agent")
    workflow.add_node("agent", logic)
    workflow.add_node("tools", logic)
    assert workflow.compile(), "valid graph"

    workflow = Graph()
    workflow.set_entry_point("tools")
    workflow.add_conditional_edges(
        "agent", logic, {"continue": "tools", "exit": END, "hmm": "extra"}
    )
    workflow.add_edge("tools", "agent")
    workflow.add_node("agent", logic)
    workflow.add_node("tools", logic)
    with pytest.raises(ValueError, match="unknown"):  # extra is not defined
        workflow.compile()

    workflow = Graph()
    workflow.set_entry_point("agent")
    workflow.add_conditional_edges("agent", logic, {"continue": "tools", "exit": END})
    workflow.add_edge("tools", "extra")
    workflow.add_node("agent", logic)
    workflow.add_node("tools", logic)
    with pytest.raises(ValueError, match="unknown"):  # extra is not defined
        workflow.compile()

    workflow = Graph()
    workflow.add_node("agent", logic)
    workflow.add_node("tools", logic)
    workflow.add_node("extra", logic)
    workflow.set_entry_point("agent")
    workflow.add_conditional_edges("agent", logic, {"continue": "tools", "exit": END})
    workflow.add_edge("tools", "agent")
    with pytest.raises(
        ValueError, match="Node `extra` is not reachable"
    ):  # extra is not reachable
        workflow.compile()

    workflow = Graph()
    workflow.add_node("agent", logic)
    workflow.add_node("tools", logic)
    workflow.add_node("extra", logic)
    workflow.set_entry_point("agent")
    workflow.add_conditional_edges("agent", logic)
    workflow.add_edge("tools", "agent")
    # Accept, even though extra is dead-end
    workflow.compile()

    class State(TypedDict):
        hello: str

    def node_a(state: State) -> State:
        # typo
        return {"hell": "world"}

    builder = StateGraph(State)
    builder.add_node("a", node_a)
    builder.set_entry_point("a")
    builder.set_finish_point("a")
    graph = builder.compile()
    with pytest.raises(InvalidUpdateError):
        graph.invoke({"hello": "there"})

    graph = StateGraph(State)
    graph.add_node("start", lambda x: x)
    graph.add_edge("__start__", "start")
    graph.add_edge("unknown", "start")
    graph.add_edge("start", "__end__")
    with pytest.raises(ValueError, match="Found edge starting at unknown node "):
        graph.compile()


def test_reducer_before_first_node() -> None:
    from langchain_core.messages import HumanMessage

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

    def node_a(state: State) -> State:
        assert state == {
            "hello": "there",
            "messages": [HumanMessage(content="hello", id=AnyStr())],
        }

    builder = StateGraph(State)
    builder.add_node("a", node_a)
    builder.set_entry_point("a")
    builder.set_finish_point("a")
    graph = builder.compile()
    assert graph.invoke({"hello": "there", "messages": "hello"}) == {
        "hello": "there",
        "messages": [HumanMessage(content="hello", id=AnyStr())],
    }

    class State(TypedDict):
        hello: str
        messages: Annotated[List[str], add_messages]

    def node_a(state: State) -> State:
        assert state == {
            "hello": "there",
            "messages": [HumanMessage(content="hello", id=AnyStr())],
        }

    builder = StateGraph(State)
    builder.add_node("a", node_a)
    builder.set_entry_point("a")
    builder.set_finish_point("a")
    graph = builder.compile()
    assert graph.invoke({"hello": "there", "messages": "hello"}) == {
        "hello": "there",
        "messages": [HumanMessage(content="hello", id=AnyStr())],
    }

    class State(TypedDict):
        hello: str
        messages: Annotated[Sequence[str], add_messages]

    def node_a(state: State) -> State:
        assert state == {
            "hello": "there",
            "messages": [HumanMessage(content="hello", id=AnyStr())],
        }

    builder = StateGraph(State)
    builder.add_node("a", node_a)
    builder.set_entry_point("a")
    builder.set_finish_point("a")
    graph = builder.compile()
    assert graph.invoke({"hello": "there", "messages": "hello"}) == {
        "hello": "there",
        "messages": [HumanMessage(content="hello", id=AnyStr())],
    }


def test_invoke_single_process_in_out(mocker: MockerFixture) -> None:
    add_one = mocker.Mock(side_effect=lambda x: x + 1)
    chain = Channel.subscribe_to("input") | add_one | Channel.write_to("output")

    app = Pregel(
        nodes={
            "one": chain,
        },
        channels={
            "input": LastValue(int),
            "output": LastValue(int),
        },
        input_channels="input",
        output_channels="output",
    )
    graph = Graph()
    graph.add_node("add_one", add_one)
    graph.set_entry_point("add_one")
    graph.set_finish_point("add_one")
    gapp = graph.compile()

    assert app.input_schema.schema() == {"title": "LangGraphInput", "type": "integer"}
    assert app.output_schema.schema() == {"title": "LangGraphOutput", "type": "integer"}
    with warnings.catch_warnings():
        warnings.simplefilter("error")  # raise warnings as errors
        assert app.config_schema().schema() == {
            "properties": {},
            "title": "LangGraphConfig",
            "type": "object",
        }
    assert app.invoke(2) == 3
    assert app.invoke(2, output_keys=["output"]) == {"output": 3}
    assert repr(app), "does not raise recursion error"

    assert gapp.invoke(2, debug=True) == 3


@pytest.mark.parametrize(
    "falsy_value",
    [None, False, 0, "", [], {}, set(), frozenset(), 0.0, 0j],
)
def test_invoke_single_process_in_out_falsy_values(falsy_value: Any) -> None:
    graph = Graph()
    graph.add_node("return_falsy_const", lambda *args, **kwargs: falsy_value)
    graph.set_entry_point("return_falsy_const")
    graph.set_finish_point("return_falsy_const")
    gapp = graph.compile()
    assert gapp.invoke(1) == falsy_value


def test_invoke_single_process_in_write_kwargs(mocker: MockerFixture) -> None:
    add_one = mocker.Mock(side_effect=lambda x: x + 1)
    chain = (
        Channel.subscribe_to("input")
        | add_one
        | Channel.write_to("output", fixed=5, output_plus_one=lambda x: x + 1)
    )

    app = Pregel(
        nodes={"one": chain},
        channels={
            "input": LastValue(int),
            "output": LastValue(int),
            "fixed": LastValue(int),
            "output_plus_one": LastValue(int),
        },
        output_channels=["output", "fixed", "output_plus_one"],
        input_channels="input",
    )

    assert app.input_schema.schema() == {"title": "LangGraphInput", "type": "integer"}
    assert app.output_schema.schema() == {
        "title": "LangGraphOutput",
        "type": "object",
        "properties": {
            "output": {"title": "Output", "type": "integer"},
            "fixed": {"title": "Fixed", "type": "integer"},
            "output_plus_one": {"title": "Output Plus One", "type": "integer"},
        },
    }
    assert app.invoke(2) == {"output": 3, "fixed": 5, "output_plus_one": 4}


def test_invoke_single_process_in_out_dict(mocker: MockerFixture) -> None:
    add_one = mocker.Mock(side_effect=lambda x: x + 1)
    chain = Channel.subscribe_to("input") | add_one | Channel.write_to("output")

    app = Pregel(
        nodes={"one": chain},
        channels={"input": LastValue(int), "output": LastValue(int)},
        input_channels="input",
        output_channels=["output"],
    )

    assert app.input_schema.schema() == {"title": "LangGraphInput", "type": "integer"}
    assert app.output_schema.schema() == {
        "title": "LangGraphOutput",
        "type": "object",
        "properties": {"output": {"title": "Output", "type": "integer"}},
    }
    assert app.invoke(2) == {"output": 3}


def test_invoke_single_process_in_dict_out_dict(mocker: MockerFixture) -> None:
    add_one = mocker.Mock(side_effect=lambda x: x + 1)
    chain = Channel.subscribe_to("input") | add_one | Channel.write_to("output")

    app = Pregel(
        nodes={"one": chain},
        channels={"input": LastValue(int), "output": LastValue(int)},
        input_channels=["input"],
        output_channels=["output"],
    )

    assert app.input_schema.schema() == {
        "title": "LangGraphInput",
        "type": "object",
        "properties": {"input": {"title": "Input", "type": "integer"}},
    }
    assert app.output_schema.schema() == {
        "title": "LangGraphOutput",
        "type": "object",
        "properties": {"output": {"title": "Output", "type": "integer"}},
    }
    assert app.invoke({"input": 2}) == {"output": 3}


def test_invoke_two_processes_in_out(mocker: MockerFixture) -> None:
    add_one = mocker.Mock(side_effect=lambda x: x + 1)
    one = Channel.subscribe_to("input") | add_one | Channel.write_to("inbox")
    two = Channel.subscribe_to("inbox") | add_one | Channel.write_to("output")

    app = Pregel(
        nodes={"one": one, "two": two},
        channels={
            "inbox": LastValue(int),
            "output": LastValue(int),
            "input": LastValue(int),
        },
        input_channels="input",
        output_channels="output",
    )

    assert app.invoke(2) == 4

    assert app.invoke(2, input_keys="inbox") == 3

    with pytest.raises(GraphRecursionError):
        app.invoke(2, {"recursion_limit": 1})

    graph = Graph()
    graph.add_node("add_one", add_one)
    graph.add_node("add_one_more", add_one)
    graph.set_entry_point("add_one")
    graph.set_finish_point("add_one_more")
    graph.add_edge("add_one", "add_one_more")
    gapp = graph.compile()

    assert gapp.invoke(2) == 4

    for step, values in enumerate(gapp.stream(2), start=1):
        if step == 1:
            assert values == {
                "add_one": 3,
            }
        elif step == 2:
            assert values == {
                "add_one_more": 4,
            }
        else:
            assert 0, f"{step}:{values}"
    assert step == 2


def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> None:
    add_one = mocker.Mock(side_effect=lambda x: x + 1)
    one = Channel.subscribe_to("input") | add_one | Channel.write_to("inbox")
    two = Channel.subscribe_to("inbox") | add_one | Channel.write_to("output")

    memory = MemorySaverAssertImmutable()
    app = Pregel(
        nodes={"one": one, "two": two},
        channels={
            "inbox": LastValue(int),
            "output": LastValue(int),
            "input": LastValue(int),
        },
        input_channels="input",
        output_channels="output",
        checkpointer=memory,
        interrupt_after_nodes=["one"],
    )

    # start execution, stop at inbox
    assert app.invoke(2, {"configurable": {"thread_id": 1}}) is None

    # inbox == 3
    checkpoint = memory.get({"configurable": {"thread_id": 1}})
    assert checkpoint is not None
    assert checkpoint["channel_values"]["inbox"] == 3

    # resume execution, finish
    assert app.invoke(None, {"configurable": {"thread_id": 1}}) == 4

    # start execution again, stop at inbox
    assert app.invoke(20, {"configurable": {"thread_id": 1}}) is None

    # inbox == 21
    checkpoint = memory.get({"configurable": {"thread_id": 1}})
    assert checkpoint is not None
    assert checkpoint["channel_values"]["inbox"] == 21

    # send a new value in, interrupting the previous execution
    assert app.invoke(3, {"configurable": {"thread_id": 1}}) is None
    assert app.invoke(None, {"configurable": {"thread_id": 1}}) == 5

    # start execution again, stopping at inbox
    assert app.invoke(20, {"configurable": {"thread_id": 2}}) is None

    # inbox == 21
    snapshot = app.get_state({"configurable": {"thread_id": 2}})
    assert snapshot.values["inbox"] == 21
    assert snapshot.next == ("two",)

    # update the state, resume
    app.update_state({"configurable": {"thread_id": 2}}, 25, as_node="one")
    assert app.invoke(None, {"configurable": {"thread_id": 2}}) == 26

    # no pending tasks
    snapshot = app.get_state({"configurable": {"thread_id": 2}})
    assert snapshot.next == ()

    # list history
    assert [c for c in app.get_state_history({"configurable": {"thread_id": 1}})] == [
        StateSnapshot(
            values={"input": 2},
            next=("one",),
            config={
                "configurable": {
                    "thread_id": 1,
                    "thread_ts": AnyStr(),
                }
            },
            created_at=AnyStr(),
            metadata={"source": "input", "step": -1, "writes": 2},
            parent_config=None,
        ),
        StateSnapshot(
            values={"inbox": 3, "input": 2},
            next=("two",),
            config={
                "configurable": {
                    "thread_id": 1,
                    "thread_ts": AnyStr(),
                }
            },
            created_at=AnyStr(),
            metadata={"source": "loop", "step": 0, "writes": None},
            parent_config=None,
        ),
        StateSnapshot(
            values={"inbox": 3, "output": 4, "input": 2},
            next=(),
            config={
                "configurable": {
                    "thread_id": 1,
                    "thread_ts": AnyStr(),
                }
            },
            created_at=AnyStr(),
            metadata={"source": "loop", "step": 1, "writes": 4},
            parent_config=None,
        ),
        StateSnapshot(
            values={"inbox": 3, "output": 4, "input": 20},
            next=("one",),
            config={
                "configurable": {
                    "thread_id": 1,
                    "thread_ts": AnyStr(),
                }
            },
            created_at=AnyStr(),
            metadata={"source": "input", "step": 2, "writes": 20},
            parent_config=None,
        ),
        StateSnapshot(
            values={"inbox": 21, "output": 4, "input": 20},
            next=("two",),
            config={
                "configurable": {
                    "thread_id": 1,
                    "thread_ts": AnyStr(),
                }
            },
            created_at=AnyStr(),
            metadata={"source": "loop", "step": 3, "writes": None},
            parent_config=None,
        ),
        StateSnapshot(
            values={"inbox": 21, "output": 4, "input": 3},
            next=("one",),
            config={
                "configurable": {
                    "thread_id": 1,
                    "thread_ts": AnyStr(),
                }
            },
            created_at=AnyStr(),
            metadata={"source": "input", "step": 4, "writes": 3},
            parent_config=None,
        ),
        StateSnapshot(
            values={"inbox": 4, "output": 4, "input": 3},
            next=("two",),
            config={
                "configurable": {
                    "thread_id": 1,
                    "thread_ts": AnyStr(),
                }
            },
            created_at=AnyStr(),
            metadata={"source": "loop", "step": 5, "writes": None},
            parent_config=None,
        ),
        StateSnapshot(
            values={"inbox": 4, "output": 5, "input": 3},
            next=(),
            config={
                "configurable": {
                    "thread_id": 1,
                    "thread_ts": AnyStr(),
                }
            },
            created_at=AnyStr(),
            metadata={"source": "loop", "step": 6, "writes": 5},
            parent_config=None,
        ),
    ]


def test_invoke_two_processes_in_dict_out(mocker: MockerFixture) -> None:
    add_one = mocker.Mock(side_effect=lambda x: x + 1)
    one = Channel.subscribe_to("input") | add_one | Channel.write_to("inbox")
    two = (
        Channel.subscribe_to("inbox")
        | RunnableLambda(add_one).batch
        | RunnablePassthrough(lambda _: time.sleep(0.1))
        | Channel.write_to("output").batch
    )

    app = Pregel(
        nodes={"one": one, "two": two},
        channels={
            "inbox": Topic(int),
            "output": LastValue(int),
            "input": LastValue(int),
        },
        input_channels=["input", "inbox"],
        stream_channels=["output", "inbox"],
        output_channels=["output"],
    )

    # [12 + 1, 2 + 1 + 1]
    assert [
        *app.stream(
            {"input": 2, "inbox": 12}, output_keys="output", stream_mode="updates"
        )
    ] == [
        {"two": 13},
        {"two": 4},
    ]
    assert [*app.stream({"input": 2, "inbox": 12}, output_keys="output")] == [
        13,
        4,
    ]

    assert [*app.stream({"input": 2, "inbox": 12}, stream_mode="updates")] == [
        {"one": {"inbox": 3}},
        {"two": {"output": 13}},
        {"two": {"output": 4}},
    ]
    assert [*app.stream({"input": 2, "inbox": 12})] == [
        {"inbox": [3], "output": 13},
        {"inbox": [], "output": 4},
    ]
    assert [*app.stream({"input": 2, "inbox": 12}, stream_mode="debug")] == [
        {
            "type": "task",
            "timestamp": AnyStr(),
            "step": 0,
            "payload": {
                "id": "2687f72c-e3a8-5f6f-9afa-047cbf24e923",
                "name": "one",
                "input": 2,
                "triggers": ["input"],
            },
        },
        {
            "type": "task",
            "timestamp": AnyStr(),
            "step": 0,
            "payload": {
                "id": "18f52f6a-828d-58a1-a501-53cc0c7af33e",
                "name": "two",
                "input": [12],
                "triggers": ["inbox"],
            },
        },
        {
            "type": "task_result",
            "timestamp": AnyStr(),
            "step": 0,
            "payload": {
                "id": "2687f72c-e3a8-5f6f-9afa-047cbf24e923",
                "name": "one",
                "result": [("inbox", 3)],
            },
        },
        {
            "type": "task_result",
            "timestamp": AnyStr(),
            "step": 0,
            "payload": {
                "id": "18f52f6a-828d-58a1-a501-53cc0c7af33e",
                "name": "two",
                "result": [("output", 13)],
            },
        },
        {
            "type": "task",
            "timestamp": AnyStr(),
            "step": 1,
            "payload": {
                "id": "871d6e74-7bb3-565f-a4fe-cef4b8f19b62",
                "name": "two",
                "input": [3],
                "triggers": ["inbox"],
            },
        },
        {
            "type": "task_result",
            "timestamp": AnyStr(),
            "step": 1,
            "payload": {
                "id": "871d6e74-7bb3-565f-a4fe-cef4b8f19b62",
                "name": "two",
                "result": [("output", 4)],
            },
        },
    ]


def test_batch_two_processes_in_out() -> None:
    def add_one_with_delay(inp: int) -> int:
        time.sleep(inp / 10)
        return inp + 1

    one = Channel.subscribe_to("input") | add_one_with_delay | Channel.write_to("one")
    two = Channel.subscribe_to("one") | add_one_with_delay | Channel.write_to("output")

    app = Pregel(
        nodes={"one": one, "two": two},
        channels={
            "one": LastValue(int),
            "output": LastValue(int),
            "input": LastValue(int),
        },
        input_channels="input",
        output_channels="output",
    )

    assert app.batch([3, 2, 1, 3, 5]) == [5, 4, 3, 5, 7]
    assert app.batch([3, 2, 1, 3, 5], output_keys=["output"]) == [
        {"output": 5},
        {"output": 4},
        {"output": 3},
        {"output": 5},
        {"output": 7},
    ]

    graph = Graph()
    graph.add_node("add_one", add_one_with_delay)
    graph.add_node("add_one_more", add_one_with_delay)
    graph.set_entry_point("add_one")
    graph.set_finish_point("add_one_more")
    graph.add_edge("add_one", "add_one_more")
    gapp = graph.compile()

    assert gapp.batch([3, 2, 1, 3, 5]) == [5, 4, 3, 5, 7]


def test_invoke_many_processes_in_out(mocker: MockerFixture) -> None:
    test_size = 100
    add_one = mocker.Mock(side_effect=lambda x: x + 1)

    nodes = {"-1": Channel.subscribe_to("input") | add_one | Channel.write_to("-1")}
    for i in range(test_size - 2):
        nodes[str(i)] = (
            Channel.subscribe_to(str(i - 1)) | add_one | Channel.write_to(str(i))
        )
    nodes["last"] = Channel.subscribe_to(str(i)) | add_one | Channel.write_to("output")

    app = Pregel(
        nodes=nodes,
        channels={str(i): LastValue(int) for i in range(-1, test_size - 2)}
        | {"input": LastValue(int), "output": LastValue(int)},
        input_channels="input",
        output_channels="output",
    )

    for _ in range(10):
        assert app.invoke(2, {"recursion_limit": test_size}) == 2 + test_size

    with ThreadPoolExecutor() as executor:
        assert [
            *executor.map(app.invoke, [2] * 10, [{"recursion_limit": test_size}] * 10)
        ] == [2 + test_size] * 10


def test_batch_many_processes_in_out(mocker: MockerFixture) -> None:
    test_size = 100
    add_one = mocker.Mock(side_effect=lambda x: x + 1)

    nodes = {"-1": Channel.subscribe_to("input") | add_one | Channel.write_to("-1")}
    for i in range(test_size - 2):
        nodes[str(i)] = (
            Channel.subscribe_to(str(i - 1)) | add_one | Channel.write_to(str(i))
        )
    nodes["last"] = Channel.subscribe_to(str(i)) | add_one | Channel.write_to("output")

    app = Pregel(
        nodes=nodes,
        channels={str(i): LastValue(int) for i in range(-1, test_size - 2)}
        | {"input": LastValue(int), "output": LastValue(int)},
        input_channels="input",
        output_channels="output",
    )

    for _ in range(3):
        assert app.batch([2, 1, 3, 4, 5], {"recursion_limit": test_size}) == [
            2 + test_size,
            1 + test_size,
            3 + test_size,
            4 + test_size,
            5 + test_size,
        ]

    with ThreadPoolExecutor() as executor:
        assert [
            *executor.map(
                app.batch, [[2, 1, 3, 4, 5]] * 3, [{"recursion_limit": test_size}] * 3
            )
        ] == [
            [2 + test_size, 1 + test_size, 3 + test_size, 4 + test_size, 5 + test_size]
        ] * 3


def test_invoke_two_processes_two_in_two_out_invalid(mocker: MockerFixture) -> None:
    add_one = mocker.Mock(side_effect=lambda x: x + 1)

    one = Channel.subscribe_to("input") | add_one | Channel.write_to("output")
    two = Channel.subscribe_to("input") | add_one | Channel.write_to("output")

    app = Pregel(
        nodes={"one": one, "two": two},
        channels={"output": LastValue(int), "input": LastValue(int)},
        input_channels="input",
        output_channels="output",
    )

    with pytest.raises(InvalidUpdateError):
        # LastValue channels can only be updated once per iteration
        app.invoke(2)


def test_invoke_two_processes_two_in_two_out_valid(mocker: MockerFixture) -> None:
    add_one = mocker.Mock(side_effect=lambda x: x + 1)

    one = Channel.subscribe_to("input") | add_one | Channel.write_to("output")
    two = Channel.subscribe_to("input") | add_one | Channel.write_to("output")

    app = Pregel(
        nodes={"one": one, "two": two},
        channels={
            "input": LastValue(int),
            "output": Topic(int),
        },
        input_channels="input",
        output_channels="output",
    )

    # An Inbox channel accumulates updates into a sequence
    assert app.invoke(2) == [3, 3]


def test_invoke_checkpoint(mocker: MockerFixture) -> None:
    add_one = mocker.Mock(side_effect=lambda x: x["total"] + x["input"])
    errored_once = False

    def raise_if_above_10(input: int) -> int:
        nonlocal errored_once
        if input > 4:
            if errored_once:
                pass
            else:
                errored_once = True
                raise ConnectionError("I will be retried")
        if input > 10:
            raise ValueError("Input is too large")
        return input

    one = (
        Channel.subscribe_to(["input"]).join(["total"])
        | add_one
        | Channel.write_to("output", "total")
        | raise_if_above_10
    )

    memory = MemorySaverAssertImmutable()

    app = Pregel(
        nodes={"one": one},
        channels={
            "total": BinaryOperatorAggregate(int, operator.add),
            "input": LastValue(int),
            "output": LastValue(int),
        },
        input_channels="input",
        output_channels="output",
        checkpointer=memory,
        retry_policy=RetryPolicy(),
    )

    # total starts out as 0, so output is 0+2=2
    assert app.invoke(2, {"configurable": {"thread_id": "1"}}) == 2
    checkpoint = memory.get({"configurable": {"thread_id": "1"}})
    assert checkpoint is not None
    assert checkpoint["channel_values"].get("total") == 2
    # total is now 2, so output is 2+3=5
    assert app.invoke(3, {"configurable": {"thread_id": "1"}}) == 5
    assert errored_once, "errored and retried"
    checkpoint = memory.get({"configurable": {"thread_id": "1"}})
    assert checkpoint is not None
    assert checkpoint["channel_values"].get("total") == 7
    # total is now 2+5=7, so output would be 7+4=11, but raises ValueError
    with pytest.raises(ValueError):
        app.invoke(4, {"configurable": {"thread_id": "1"}})
    # checkpoint is not updated
    checkpoint = memory.get({"configurable": {"thread_id": "1"}})
    assert checkpoint is not None
    assert checkpoint["channel_values"].get("total") == 7
    # on a new thread, total starts out as 0, so output is 0+5=5
    assert app.invoke(5, {"configurable": {"thread_id": "2"}}) == 5
    checkpoint = memory.get({"configurable": {"thread_id": "1"}})
    assert checkpoint is not None
    assert checkpoint["channel_values"].get("total") == 7
    checkpoint = memory.get({"configurable": {"thread_id": "2"}})
    assert checkpoint is not None
    assert checkpoint["channel_values"].get("total") == 5


def test_invoke_checkpoint_sqlite(mocker: MockerFixture) -> None:
    adder = mocker.Mock(side_effect=lambda x: x["total"] + x["input"])

    def raise_if_above_10(input: int) -> int:
        if input > 10:
            raise ValueError("Input is too large")
        return input

    one = (
        Channel.subscribe_to(["input"]).join(["total"])
        | adder
        | Channel.write_to("output", "total")
        | raise_if_above_10
    )

    with SqliteSaver.from_conn_string(":memory:") as memory:
        app = Pregel(
            nodes={"one": one},
            channels={
                "total": BinaryOperatorAggregate(int, operator.add),
                "input": LastValue(int),
                "output": LastValue(int),
            },
            input_channels="input",
            output_channels="output",
            checkpointer=memory,
        )

        thread_1 = {"configurable": {"thread_id": "1"}}
        # total starts out as 0, so output is 0+2=2
        assert app.invoke(2, thread_1, debug=1) == 2
        state = app.get_state(thread_1)
        assert state is not None
        assert state.values.get("total") == 2
        assert state.next == ()
        assert state.config["configurable"]["thread_ts"] == memory.get(thread_1)["id"]
        # total is now 2, so output is 2+3=5
        assert app.invoke(3, thread_1) == 5
        state = app.get_state(thread_1)
        assert state is not None
        assert state.values.get("total") == 7
        assert state.config["configurable"]["thread_ts"] == memory.get(thread_1)["id"]
        # total is now 2+5=7, so output would be 7+4=11, but raises ValueError
        with pytest.raises(ValueError):
            app.invoke(4, thread_1)
        # checkpoint is updated with new input
        state = app.get_state(thread_1)
        assert state is not None
        assert state.values.get("total") == 7
        assert state.next == ("one",)
        """we checkpoint inputs and it failed on "one", so the next node is one"""
        # we can recover from error by sending new inputs
        assert app.invoke(2, thread_1) == 9
        state = app.get_state(thread_1)
        assert state is not None
        assert state.values.get("total") == 16, "total is now 7+9=16"
        assert state.next == ()

        thread_2 = {"configurable": {"thread_id": "2"}}
        # on a new thread, total starts out as 0, so output is 0+5=5
        assert app.invoke(5, thread_2, debug=True) == 5
        state = app.get_state({"configurable": {"thread_id": "1"}})
        assert state is not None
        assert state.values.get("total") == 16
        assert state.next == (), "checkpoint of other thread not touched"
        state = app.get_state(thread_2)
        assert state is not None
        assert state.values.get("total") == 5
        assert state.next == ()

        assert len(list(app.get_state_history(thread_1, limit=1))) == 1
        # list all checkpoints for thread 1
        thread_1_history = [c for c in app.get_state_history(thread_1)]
        # there are 7 checkpoints
        assert len(thread_1_history) == 7
        assert Counter(c.metadata["source"] for c in thread_1_history) == {
            "input": 4,
            "loop": 3,
        }
        # sorted descending
        assert (
            thread_1_history[0].config["configurable"]["thread_ts"]
            > thread_1_history[1].config["configurable"]["thread_ts"]
        )
        # cursor pagination
        cursored = list(
            app.get_state_history(thread_1, limit=1, before=thread_1_history[0].config)
        )
        assert len(cursored) == 1
        assert cursored[0].config == thread_1_history[1].config
        # the last checkpoint
        assert thread_1_history[0].values["total"] == 16
        # the first "loop" checkpoint
        assert thread_1_history[-2].values["total"] == 2
        # can get each checkpoint using aget with config
        assert (
            memory.get(thread_1_history[0].config)["id"]
            == thread_1_history[0].config["configurable"]["thread_ts"]
        )
        assert (
            memory.get(thread_1_history[1].config)["id"]
            == thread_1_history[1].config["configurable"]["thread_ts"]
        )

        thread_1_next_config = app.update_state(thread_1_history[1].config, 10)
        # update creates a new checkpoint
        assert (
            thread_1_next_config["configurable"]["thread_ts"]
            > thread_1_history[0].config["configurable"]["thread_ts"]
        )
        # update makes new checkpoint child of the previous one
        assert (
            app.get_state(thread_1_next_config).parent_config
            == thread_1_history[1].config
        )
        # 1 more checkpoint in history
        assert len(list(app.get_state_history(thread_1))) == 8
        assert Counter(
            c.metadata["source"] for c in app.get_state_history(thread_1)
        ) == {
            "update": 1,
            "input": 4,
            "loop": 3,
        }
        # the latest checkpoint is the updated one
        assert app.get_state(thread_1) == app.get_state(thread_1_next_config)


def test_invoke_two_processes_two_in_join_two_out(mocker: MockerFixture) -> None:
    add_one = mocker.Mock(side_effect=lambda x: x + 1)
    add_10_each = mocker.Mock(side_effect=lambda x: sorted(y + 10 for y in x))

    one = Channel.subscribe_to("input") | add_one | Channel.write_to("inbox")
    chain_three = Channel.subscribe_to("input") | add_one | Channel.write_to("inbox")
    chain_four = (
        Channel.subscribe_to("inbox") | add_10_each | Channel.write_to("output")
    )

    app = Pregel(
        nodes={
            "one": one,
            "chain_three": chain_three,
            "chain_four": chain_four,
        },
        channels={
            "inbox": Topic(int),
            "output": LastValue(int),
            "input": LastValue(int),
        },
        input_channels="input",
        output_channels="output",
    )

    # Then invoke app
    # We get a single array result as chain_four waits for all publishers to finish
    # before operating on all elements published to topic_two as an array
    for _ in range(100):
        assert app.invoke(2) == [13, 13]

    with ThreadPoolExecutor() as executor:
        assert [*executor.map(app.invoke, [2] * 100)] == [[13, 13]] * 100


def test_invoke_join_then_call_other_pregel(mocker: MockerFixture) -> None:
    add_one = mocker.Mock(side_effect=lambda x: x + 1)
    add_10_each = mocker.Mock(side_effect=lambda x: [y + 10 for y in x])

    inner_app = Pregel(
        nodes={
            "one": Channel.subscribe_to("input") | add_one | Channel.write_to("output")
        },
        channels={
            "output": LastValue(int),
            "input": LastValue(int),
        },
        input_channels="input",
        output_channels="output",
    )

    one = (
        Channel.subscribe_to("input")
        | add_10_each
        | Channel.write_to("inbox_one").map()
    )
    two = (
        Channel.subscribe_to("inbox_one")
        | inner_app.map()
        | sorted
        | Channel.write_to("outbox_one")
    )
    chain_three = Channel.subscribe_to("outbox_one") | sum | Channel.write_to("output")

    app = Pregel(
        nodes={
            "one": one,
            "two": two,
            "chain_three": chain_three,
        },
        channels={
            "inbox_one": Topic(int),
            "outbox_one": LastValue(int),
            "output": LastValue(int),
            "input": LastValue(int),
        },
        input_channels="input",
        output_channels="output",
    )

    for _ in range(10):
        assert app.invoke([2, 3]) == 27

    with ThreadPoolExecutor() as executor:
        assert [*executor.map(app.invoke, [[2, 3]] * 10)] == [27] * 10


def test_invoke_two_processes_one_in_two_out(mocker: MockerFixture) -> None:
    add_one = mocker.Mock(side_effect=lambda x: x + 1)

    one = (
        Channel.subscribe_to("input")
        | add_one
        | Channel.write_to(output=RunnablePassthrough(), between=RunnablePassthrough())
    )
    two = Channel.subscribe_to("between") | add_one | Channel.write_to("output")

    app = Pregel(
        nodes={"one": one, "two": two},
        channels={
            "input": LastValue(int),
            "between": LastValue(int),
            "output": LastValue(int),
        },
        stream_channels=["output", "between"],
        input_channels="input",
        output_channels="output",
    )

    assert [c for c in app.stream(2, stream_mode="updates")] == [
        {"one": {"between": 3, "output": 3}},
        {"two": {"output": 4}},
    ]
    assert [c for c in app.stream(2)] == [
        {"between": 3, "output": 3},
        {"between": 3, "output": 4},
    ]


def test_invoke_two_processes_no_out(mocker: MockerFixture) -> None:
    add_one = mocker.Mock(side_effect=lambda x: x + 1)
    one = Channel.subscribe_to("input") | add_one | Channel.write_to("between")
    two = Channel.subscribe_to("between") | add_one

    app = Pregel(
        nodes={"one": one, "two": two},
        channels={
            "input": LastValue(int),
            "between": LastValue(int),
            "output": LastValue(int),
        },
        input_channels="input",
        output_channels="output",
    )

    # It finishes executing (once no more messages being published)
    # but returns nothing, as nothing was published to OUT topic
    assert app.invoke(2) is None


def test_invoke_two_processes_no_in(mocker: MockerFixture) -> None:
    add_one = mocker.Mock(side_effect=lambda x: x + 1)

    one = Channel.subscribe_to("between") | add_one | Channel.write_to("output")
    two = Channel.subscribe_to("between") | add_one

    with pytest.raises(ValueError):
        Pregel(nodes={"one": one, "two": two})


def test_channel_enter_exit_timing(mocker: MockerFixture) -> None:
    setup = mocker.Mock()
    cleanup = mocker.Mock()

    @contextmanager
    def an_int() -> Generator[int, None, None]:
        setup()
        try:
            yield 5
        finally:
            cleanup()

    add_one = mocker.Mock(side_effect=lambda x: x + 1)
    one = Channel.subscribe_to("input") | add_one | Channel.write_to("inbox")
    two = (
        Channel.subscribe_to("inbox")
        | RunnableLambda(add_one).batch
        | Channel.write_to("output").batch
    )

    app = Pregel(
        nodes={"one": one, "two": two},
        channels={
            "inbox": Topic(int),
            "ctx": Context(an_int, typ=int),
            "output": LastValue(int),
            "input": LastValue(int),
        },
        input_channels="input",
        output_channels=["inbox", "output"],
        stream_channels=["inbox", "output"],
    )

    assert setup.call_count == 0
    assert cleanup.call_count == 0
    for i, chunk in enumerate(app.stream(2)):
        assert setup.call_count == 1, "Expected setup to be called once"
        assert cleanup.call_count == 0, "Expected cleanup to not be called yet"
        if i == 0:
            assert chunk == {"inbox": [3]}
        elif i == 1:
            assert chunk == {"inbox": [], "output": 4}
        else:
            assert False, "Expected only two chunks"
    assert cleanup.call_count == 1, "Expected cleanup to be called once"


def test_conditional_graph(snapshot: SnapshotAssertion) -> None:
    from copy import deepcopy

    from langchain_core.agents import AgentAction, AgentFinish
    from langchain_core.language_models.fake import FakeStreamingListLLM
    from langchain_core.prompts import PromptTemplate
    from langchain_core.runnables import RunnablePassthrough
    from langchain_core.tools import tool

    # Assemble the tools
    @tool()
    def search_api(query: str) -> str:
        """Searches the API for the query."""
        return f"result for {query}"

    tools = [search_api]

    # Construct the agent
    prompt = PromptTemplate.from_template("Hello!")

    llm = FakeStreamingListLLM(
        responses=[
            "tool:search_api:query",
            "tool:search_api:another",
            "finish:answer",
        ]
    )

    def agent_parser(input: str) -> Union[AgentAction, AgentFinish]:
        if input.startswith("finish"):
            _, answer = input.split(":")
            return AgentFinish(return_values={"answer": answer}, log=input)
        else:
            _, tool_name, tool_input = input.split(":")
            return AgentAction(tool=tool_name, tool_input=tool_input, log=input)

    agent = RunnablePassthrough.assign(agent_outcome=prompt | llm | agent_parser)

    # Define tool execution logic
    def execute_tools(data: dict) -> dict:
        agent_action: AgentAction = data.pop("agent_outcome")
        observation = {t.name: t for t in tools}[agent_action.tool].invoke(
            agent_action.tool_input
        )
        if data.get("intermediate_steps") is None:
            data["intermediate_steps"] = []
        data["intermediate_steps"].append((agent_action, observation))
        return data

    # Define decision-making logic
    def should_continue(data: dict) -> str:
        # Logic to decide whether to continue in the loop or exit
        if isinstance(data["agent_outcome"], AgentFinish):
            return "exit"
        else:
            return "continue"

    # Define a new graph
    workflow = Graph()

    workflow.add_node("agent", agent)
    workflow.add_node("tools", execute_tools)

    workflow.set_entry_point("agent")

    workflow.add_conditional_edges(
        "agent", should_continue, {"continue": "tools", "exit": END}
    )

    workflow.add_edge("tools", "agent")

    app = workflow.compile()

    assert json.dumps(app.get_graph().to_json(), indent=2) == snapshot
    assert app.get_graph().draw_mermaid(with_styles=False) == snapshot
    assert json.dumps(app.get_graph(xray=True).to_json(), indent=2) == snapshot
    assert app.get_graph(xray=True).draw_mermaid(with_styles=False) == snapshot

    assert app.invoke({"input": "what is weather in sf"}) == {
        "input": "what is weather in sf",
        "intermediate_steps": [
            (
                AgentAction(
                    tool="search_api",
                    tool_input="query",
                    log="tool:search_api:query",
                ),
                "result for query",
            ),
            (
                AgentAction(
                    tool="search_api",
                    tool_input="another",
                    log="tool:search_api:another",
                ),
                "result for another",
            ),
        ],
        "agent_outcome": AgentFinish(
            return_values={"answer": "answer"}, log="finish:answer"
        ),
    }

    # deepcopy because the nodes mutate the data
    assert [deepcopy(c) for c in app.stream({"input": "what is weather in sf"})] == [
        {
            "agent": {
                "input": "what is weather in sf",
                "agent_outcome": AgentAction(
                    tool="search_api", tool_input="query", log="tool:search_api:query"
                ),
            }
        },
        {
            "tools": {
                "input": "what is weather in sf",
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:query",
                        ),
                        "result for query",
                    )
                ],
            }
        },
        {
            "agent": {
                "input": "what is weather in sf",
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:query",
                        ),
                        "result for query",
                    )
                ],
                "agent_outcome": AgentAction(
                    tool="search_api",
                    tool_input="another",
                    log="tool:search_api:another",
                ),
            }
        },
        {
            "tools": {
                "input": "what is weather in sf",
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:query",
                        ),
                        "result for query",
                    ),
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="another",
                            log="tool:search_api:another",
                        ),
                        "result for another",
                    ),
                ],
            }
        },
        {
            "agent": {
                "input": "what is weather in sf",
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:query",
                        ),
                        "result for query",
                    ),
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="another",
                            log="tool:search_api:another",
                        ),
                        "result for another",
                    ),
                ],
                "agent_outcome": AgentFinish(
                    return_values={"answer": "answer"}, log="finish:answer"
                ),
            }
        },
    ]

    # test state get/update methods with interrupt_after

    app_w_interrupt = workflow.compile(
        checkpointer=MemorySaverAssertImmutable(),
        interrupt_after=["agent"],
    )
    config = {"configurable": {"thread_id": "1"}}

    assert [
        c for c in app_w_interrupt.stream({"input": "what is weather in sf"}, config)
    ] == [
        {
            "agent": {
                "input": "what is weather in sf",
                "agent_outcome": AgentAction(
                    tool="search_api", tool_input="query", log="tool:search_api:query"
                ),
            }
        }
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "agent": {
                "input": "what is weather in sf",
                "agent_outcome": AgentAction(
                    tool="search_api", tool_input="query", log="tool:search_api:query"
                ),
            },
        },
        next=("tools",),
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        metadata={
            "source": "loop",
            "step": 0,
            "writes": {
                "agent": {
                    "input": "what is weather in sf",
                    "agent_outcome": AgentAction(
                        tool="search_api",
                        tool_input="query",
                        log="tool:search_api:query",
                    ),
                },
            },
        },
    )
    assert (
        app_w_interrupt.checkpointer.get_tuple(config).config["configurable"][
            "thread_ts"
        ]
        is not None
    )

    app_w_interrupt.update_state(
        config,
        {
            "agent_outcome": AgentAction(
                tool="search_api",
                tool_input="query",
                log="tool:search_api:a different query",
            ),
            "input": "what is weather in sf",
        },
    )

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "agent": {
                "agent_outcome": AgentAction(
                    tool="search_api",
                    tool_input="query",
                    log="tool:search_api:a different query",
                ),
                "input": "what is weather in sf",
            },
        },
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 1,
            "writes": {
                "agent": {
                    "agent_outcome": AgentAction(
                        tool="search_api",
                        tool_input="query",
                        log="tool:search_api:a different query",
                    ),
                    "input": "what is weather in sf",
                },
            },
        },
    )

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {
            "tools": {
                "input": "what is weather in sf",
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:a different query",
                        ),
                        "result for query",
                    )
                ],
            }
        },
        {
            "agent": {
                "input": "what is weather in sf",
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:a different query",
                        ),
                        "result for query",
                    )
                ],
                "agent_outcome": AgentAction(
                    tool="search_api",
                    tool_input="another",
                    log="tool:search_api:another",
                ),
            }
        },
    ]

    app_w_interrupt.update_state(
        config,
        {
            "input": "what is weather in sf",
            "intermediate_steps": [
                (
                    AgentAction(
                        tool="search_api",
                        tool_input="query",
                        log="tool:search_api:a different query",
                    ),
                    "result for query",
                )
            ],
            "agent_outcome": AgentFinish(
                return_values={"answer": "a really nice answer"},
                log="finish:a really nice answer",
            ),
        },
    )

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "agent": {
                "input": "what is weather in sf",
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:a different query",
                        ),
                        "result for query",
                    )
                ],
                "agent_outcome": AgentFinish(
                    return_values={"answer": "a really nice answer"},
                    log="finish:a really nice answer",
                ),
            },
        },
        next=(),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 4,
            "writes": {
                "agent": {
                    "input": "what is weather in sf",
                    "intermediate_steps": [
                        (
                            AgentAction(
                                tool="search_api",
                                tool_input="query",
                                log="tool:search_api:a different query",
                            ),
                            "result for query",
                        )
                    ],
                    "agent_outcome": AgentFinish(
                        return_values={"answer": "a really nice answer"},
                        log="finish:a really nice answer",
                    ),
                }
            },
        },
    )

    # test state get/update methods with interrupt_before

    app_w_interrupt = workflow.compile(
        checkpointer=MemorySaverAssertImmutable(),
        interrupt_before=["tools"],
    )
    config = {"configurable": {"thread_id": "2"}}
    llm.i = 0  # reset the llm

    assert [
        c for c in app_w_interrupt.stream({"input": "what is weather in sf"}, config)
    ] == [
        {
            "agent": {
                "input": "what is weather in sf",
                "agent_outcome": AgentAction(
                    tool="search_api", tool_input="query", log="tool:search_api:query"
                ),
            }
        }
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "agent": {
                "input": "what is weather in sf",
                "agent_outcome": AgentAction(
                    tool="search_api", tool_input="query", log="tool:search_api:query"
                ),
            },
        },
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "loop",
            "step": 0,
            "writes": {
                "agent": {
                    "input": "what is weather in sf",
                    "agent_outcome": AgentAction(
                        tool="search_api",
                        tool_input="query",
                        log="tool:search_api:query",
                    ),
                }
            },
        },
    )

    app_w_interrupt.update_state(
        config,
        {
            "agent_outcome": AgentAction(
                tool="search_api",
                tool_input="query",
                log="tool:search_api:a different query",
            ),
            "input": "what is weather in sf",
        },
    )

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "agent": {
                "agent_outcome": AgentAction(
                    tool="search_api",
                    tool_input="query",
                    log="tool:search_api:a different query",
                ),
                "input": "what is weather in sf",
            },
        },
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 1,
            "writes": {
                "agent": {
                    "agent_outcome": AgentAction(
                        tool="search_api",
                        tool_input="query",
                        log="tool:search_api:a different query",
                    ),
                    "input": "what is weather in sf",
                }
            },
        },
    )

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {
            "tools": {
                "input": "what is weather in sf",
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:a different query",
                        ),
                        "result for query",
                    )
                ],
            }
        },
        {
            "agent": {
                "input": "what is weather in sf",
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:a different query",
                        ),
                        "result for query",
                    )
                ],
                "agent_outcome": AgentAction(
                    tool="search_api",
                    tool_input="another",
                    log="tool:search_api:another",
                ),
            }
        },
    ]

    app_w_interrupt.update_state(
        config,
        {
            "input": "what is weather in sf",
            "intermediate_steps": [
                (
                    AgentAction(
                        tool="search_api",
                        tool_input="query",
                        log="tool:search_api:a different query",
                    ),
                    "result for query",
                )
            ],
            "agent_outcome": AgentFinish(
                return_values={"answer": "a really nice answer"},
                log="finish:a really nice answer",
            ),
        },
    )

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "agent": {
                "input": "what is weather in sf",
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:a different query",
                        ),
                        "result for query",
                    )
                ],
                "agent_outcome": AgentFinish(
                    return_values={"answer": "a really nice answer"},
                    log="finish:a really nice answer",
                ),
            },
        },
        next=(),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 4,
            "writes": {
                "agent": {
                    "input": "what is weather in sf",
                    "intermediate_steps": [
                        (
                            AgentAction(
                                tool="search_api",
                                tool_input="query",
                                log="tool:search_api:a different query",
                            ),
                            "result for query",
                        )
                    ],
                    "agent_outcome": AgentFinish(
                        return_values={"answer": "a really nice answer"},
                        log="finish:a really nice answer",
                    ),
                }
            },
        },
    )

    # test re-invoke to continue with interrupt_before

    app_w_interrupt = workflow.compile(
        checkpointer=MemorySaverAssertImmutable(),
        interrupt_before=["tools"],
    )
    config = {"configurable": {"thread_id": "2"}}
    llm.i = 0  # reset the llm

    assert [
        c for c in app_w_interrupt.stream({"input": "what is weather in sf"}, config)
    ] == [
        {
            "agent": {
                "input": "what is weather in sf",
                "agent_outcome": AgentAction(
                    tool="search_api", tool_input="query", log="tool:search_api:query"
                ),
            }
        }
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "agent": {
                "input": "what is weather in sf",
                "agent_outcome": AgentAction(
                    tool="search_api", tool_input="query", log="tool:search_api:query"
                ),
            },
        },
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "loop",
            "step": 0,
            "writes": {
                "agent": {
                    "input": "what is weather in sf",
                    "agent_outcome": AgentAction(
                        tool="search_api",
                        tool_input="query",
                        log="tool:search_api:query",
                    ),
                }
            },
        },
    )

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {
            "tools": {
                "input": "what is weather in sf",
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:query",
                        ),
                        "result for query",
                    )
                ],
            }
        },
        {
            "agent": {
                "input": "what is weather in sf",
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:query",
                        ),
                        "result for query",
                    )
                ],
                "agent_outcome": AgentAction(
                    tool="search_api",
                    tool_input="another",
                    log="tool:search_api:another",
                ),
            }
        },
    ]

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {
            "tools": {
                "input": "what is weather in sf",
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:query",
                        ),
                        "result for query",
                    ),
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="another",
                            log="tool:search_api:another",
                        ),
                        "result for another",
                    ),
                ],
            }
        },
        {
            "agent": {
                "input": "what is weather in sf",
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:query",
                        ),
                        "result for query",
                    ),
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="another",
                            log="tool:search_api:another",
                        ),
                        "result for another",
                    ),
                ],
                "agent_outcome": AgentFinish(
                    return_values={"answer": "answer"}, log="finish:answer"
                ),
            }
        },
    ]


def test_conditional_entrypoint_graph(snapshot: SnapshotAssertion) -> None:
    def left(data: str) -> str:
        return data + "->left"

    def right(data: str) -> str:
        return data + "->right"

    def should_start(data: str) -> str:
        # Logic to decide where to start
        if len(data) > 10:
            return "go-right"
        else:
            return "go-left"

    # Define a new graph
    workflow = Graph()

    workflow.add_node("left", left)
    workflow.add_node("right", right)

    workflow.set_conditional_entry_point(
        should_start, {"go-left": "left", "go-right": "right"}
    )

    workflow.add_conditional_edges("left", lambda data: END, {END: END})
    workflow.add_edge("right", END)

    app = workflow.compile()

    assert app.get_input_schema().schema_json() == snapshot
    assert app.get_output_schema().schema_json() == snapshot
    assert json.dumps(app.get_graph().to_json(), indent=2) == snapshot
    assert app.get_graph().draw_mermaid(with_styles=False) == snapshot

    assert (
        app.invoke("what is weather in sf", debug=True)
        == "what is weather in sf->right"
    )

    assert [*app.stream("what is weather in sf")] == [
        {"right": "what is weather in sf->right"},
    ]


def test_conditional_entrypoint_to_multiple_state_graph(
    snapshot: SnapshotAssertion,
) -> None:
    class OverallState(TypedDict):
        locations: list[str]
        results: Annotated[list[str], operator.add]

    def get_weather(state: OverallState) -> OverallState:
        location = state["location"]
        weather = "sunny" if len(location) > 2 else "cloudy"
        return {"results": [f"It's {weather} in {location}"]}

    def continue_to_weather(state: OverallState) -> list[Send]:
        return [
            Send("get_weather", {"location": location})
            for location in state["locations"]
        ]

    workflow = StateGraph(OverallState)

    workflow.add_node("get_weather", get_weather)
    workflow.add_edge("get_weather", END)
    workflow.set_conditional_entry_point(continue_to_weather)

    app = workflow.compile()

    assert app.get_input_schema().schema_json() == snapshot
    assert app.get_output_schema().schema_json() == snapshot
    assert json.dumps(app.get_graph().to_json(), indent=2) == snapshot
    assert app.get_graph().draw_mermaid(with_styles=False) == snapshot

    assert app.invoke({"locations": ["sf", "nyc"]}, debug=True) == {
        "locations": ["sf", "nyc"],
        "results": ["It's cloudy in sf", "It's sunny in nyc"],
    }

    assert [*app.stream({"locations": ["sf", "nyc"]}, stream_mode="values")][-1] == {
        "locations": ["sf", "nyc"],
        "results": ["It's cloudy in sf", "It's sunny in nyc"],
    }


def test_conditional_state_graph(snapshot: SnapshotAssertion) -> None:
    from langchain_core.agents import AgentAction, AgentFinish
    from langchain_core.language_models.fake import FakeStreamingListLLM
    from langchain_core.prompts import PromptTemplate
    from langchain_core.tools import tool

    class AgentState(TypedDict, total=False):
        input: str
        agent_outcome: Optional[Union[AgentAction, AgentFinish]]
        intermediate_steps: Annotated[list[tuple[AgentAction, str]], operator.add]

    # Assemble the tools
    @tool()
    def search_api(query: str) -> str:
        """Searches the API for the query."""
        return f"result for {query}"

    tools = [search_api]

    # Construct the agent
    prompt = PromptTemplate.from_template("Hello!")

    llm = FakeStreamingListLLM(
        responses=[
            "tool:search_api:query",
            "tool:search_api:another",
            "finish:answer",
        ]
    )

    def agent_parser(input: str) -> dict[str, Union[AgentAction, AgentFinish]]:
        if input.startswith("finish"):
            _, answer = input.split(":")
            return {
                "agent_outcome": AgentFinish(
                    return_values={"answer": answer}, log=input
                )
            }
        else:
            _, tool_name, tool_input = input.split(":")
            return {
                "agent_outcome": AgentAction(
                    tool=tool_name, tool_input=tool_input, log=input
                )
            }

    agent = prompt | llm | agent_parser

    # Define tool execution logic
    def execute_tools(data: AgentState) -> dict:
        agent_action: AgentAction = data.pop("agent_outcome")
        observation = {t.name: t for t in tools}[agent_action.tool].invoke(
            agent_action.tool_input
        )
        return {"intermediate_steps": [(agent_action, observation)]}

    # Define decision-making logic
    def should_continue(data: AgentState) -> str:
        # Logic to decide whether to continue in the loop or exit
        if isinstance(data["agent_outcome"], AgentFinish):
            return "exit"
        else:
            return "continue"

    # Define a new graph
    workflow = StateGraph(AgentState)

    workflow.add_node("agent", agent)
    workflow.add_node("tools", execute_tools)

    workflow.set_entry_point("agent")

    workflow.add_conditional_edges(
        "agent", should_continue, {"continue": "tools", "exit": END}
    )

    workflow.add_edge("tools", "agent")

    app = workflow.compile()

    assert app.get_input_schema().schema_json() == snapshot
    assert app.get_output_schema().schema_json() == snapshot
    assert json.dumps(app.get_graph().to_json(), indent=2) == snapshot
    assert app.get_graph().draw_mermaid(with_styles=False) == snapshot

    assert app.invoke({"input": "what is weather in sf"}) == {
        "input": "what is weather in sf",
        "intermediate_steps": [
            (
                AgentAction(
                    tool="search_api",
                    tool_input="query",
                    log="tool:search_api:query",
                ),
                "result for query",
            ),
            (
                AgentAction(
                    tool="search_api",
                    tool_input="another",
                    log="tool:search_api:another",
                ),
                "result for another",
            ),
        ],
        "agent_outcome": AgentFinish(
            return_values={"answer": "answer"}, log="finish:answer"
        ),
    }

    assert [*app.stream({"input": "what is weather in sf"})] == [
        {
            "agent": {
                "agent_outcome": AgentAction(
                    tool="search_api", tool_input="query", log="tool:search_api:query"
                ),
            }
        },
        {
            "tools": {
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:query",
                        ),
                        "result for query",
                    )
                ],
            }
        },
        {
            "agent": {
                "agent_outcome": AgentAction(
                    tool="search_api",
                    tool_input="another",
                    log="tool:search_api:another",
                ),
            }
        },
        {
            "tools": {
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="another",
                            log="tool:search_api:another",
                        ),
                        "result for another",
                    ),
                ],
            }
        },
        {
            "agent": {
                "agent_outcome": AgentFinish(
                    return_values={"answer": "answer"}, log="finish:answer"
                ),
            }
        },
    ]

    # test state get/update methods with interrupt_after

    app_w_interrupt = workflow.compile(
        checkpointer=MemorySaverAssertImmutable(),
        interrupt_after=["agent"],
    )
    config = {"configurable": {"thread_id": "1"}}

    assert [
        c for c in app_w_interrupt.stream({"input": "what is weather in sf"}, config)
    ] == [
        {
            "agent": {
                "agent_outcome": AgentAction(
                    tool="search_api", tool_input="query", log="tool:search_api:query"
                ),
            }
        },
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "input": "what is weather in sf",
            "agent_outcome": AgentAction(
                tool="search_api", tool_input="query", log="tool:search_api:query"
            ),
            "intermediate_steps": [],
        },
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "loop",
            "step": 1,
            "writes": {
                "agent": {
                    "agent_outcome": AgentAction(
                        tool="search_api",
                        tool_input="query",
                        log="tool:search_api:query",
                    ),
                }
            },
        },
    )

    app_w_interrupt.update_state(
        config,
        {
            "agent_outcome": AgentAction(
                tool="search_api",
                tool_input="query",
                log="tool:search_api:a different query",
            )
        },
    )

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "input": "what is weather in sf",
            "agent_outcome": AgentAction(
                tool="search_api",
                tool_input="query",
                log="tool:search_api:a different query",
            ),
            "intermediate_steps": [],
        },
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 2,
            "writes": {
                "agent": {
                    "agent_outcome": AgentAction(
                        tool="search_api",
                        tool_input="query",
                        log="tool:search_api:a different query",
                    )
                },
            },
        },
    )

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {
            "tools": {
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:a different query",
                        ),
                        "result for query",
                    )
                ],
            }
        },
        {
            "agent": {
                "agent_outcome": AgentAction(
                    tool="search_api",
                    tool_input="another",
                    log="tool:search_api:another",
                ),
            }
        },
    ]

    app_w_interrupt.update_state(
        config,
        {
            "agent_outcome": AgentFinish(
                return_values={"answer": "a really nice answer"},
                log="finish:a really nice answer",
            )
        },
    )

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "input": "what is weather in sf",
            "agent_outcome": AgentFinish(
                return_values={"answer": "a really nice answer"},
                log="finish:a really nice answer",
            ),
            "intermediate_steps": [
                (
                    AgentAction(
                        tool="search_api",
                        tool_input="query",
                        log="tool:search_api:a different query",
                    ),
                    "result for query",
                )
            ],
        },
        next=(),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 5,
            "writes": {
                "agent": {
                    "agent_outcome": AgentFinish(
                        return_values={"answer": "a really nice answer"},
                        log="finish:a really nice answer",
                    )
                }
            },
        },
    )

    # test state get/update methods with interrupt_before

    app_w_interrupt = workflow.compile(
        checkpointer=MemorySaverAssertImmutable(),
        interrupt_before=["tools"],
        debug=True,
    )
    config = {"configurable": {"thread_id": "2"}}
    llm.i = 0  # reset the llm

    assert [
        c for c in app_w_interrupt.stream({"input": "what is weather in sf"}, config)
    ] == [
        {
            "agent": {
                "agent_outcome": AgentAction(
                    tool="search_api", tool_input="query", log="tool:search_api:query"
                ),
            }
        },
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "input": "what is weather in sf",
            "agent_outcome": AgentAction(
                tool="search_api", tool_input="query", log="tool:search_api:query"
            ),
            "intermediate_steps": [],
        },
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "loop",
            "step": 1,
            "writes": {
                "agent": {
                    "agent_outcome": AgentAction(
                        tool="search_api",
                        tool_input="query",
                        log="tool:search_api:query",
                    ),
                }
            },
        },
    )

    app_w_interrupt.update_state(
        config,
        {
            "agent_outcome": AgentAction(
                tool="search_api",
                tool_input="query",
                log="tool:search_api:a different query",
            )
        },
    )

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "input": "what is weather in sf",
            "agent_outcome": AgentAction(
                tool="search_api",
                tool_input="query",
                log="tool:search_api:a different query",
            ),
            "intermediate_steps": [],
        },
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 2,
            "writes": {
                "agent": {
                    "agent_outcome": AgentAction(
                        tool="search_api",
                        tool_input="query",
                        log="tool:search_api:a different query",
                    )
                }
            },
        },
    )

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {
            "tools": {
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:a different query",
                        ),
                        "result for query",
                    )
                ],
            }
        },
        {
            "agent": {
                "agent_outcome": AgentAction(
                    tool="search_api",
                    tool_input="another",
                    log="tool:search_api:another",
                ),
            }
        },
    ]

    app_w_interrupt.update_state(
        config,
        {
            "agent_outcome": AgentFinish(
                return_values={"answer": "a really nice answer"},
                log="finish:a really nice answer",
            )
        },
    )

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "input": "what is weather in sf",
            "agent_outcome": AgentFinish(
                return_values={"answer": "a really nice answer"},
                log="finish:a really nice answer",
            ),
            "intermediate_steps": [
                (
                    AgentAction(
                        tool="search_api",
                        tool_input="query",
                        log="tool:search_api:a different query",
                    ),
                    "result for query",
                )
            ],
        },
        next=(),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 5,
            "writes": {
                "agent": {
                    "agent_outcome": AgentFinish(
                        return_values={"answer": "a really nice answer"},
                        log="finish:a really nice answer",
                    )
                }
            },
        },
    )

    # test w interrupt before all
    app_w_interrupt = workflow.compile(
        checkpointer=MemorySaverAssertImmutable(),
        interrupt_before="*",
        debug=True,
    )
    config = {"configurable": {"thread_id": "3"}}
    llm.i = 0  # reset the llm

    assert [
        c for c in app_w_interrupt.stream({"input": "what is weather in sf"}, config)
    ] == []

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "input": "what is weather in sf",
            "intermediate_steps": [],
        },
        next=("agent",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={"source": "loop", "step": 0, "writes": None},
    )

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {
            "agent": {
                "agent_outcome": AgentAction(
                    tool="search_api", tool_input="query", log="tool:search_api:query"
                ),
            }
        },
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "input": "what is weather in sf",
            "agent_outcome": AgentAction(
                tool="search_api", tool_input="query", log="tool:search_api:query"
            ),
            "intermediate_steps": [],
        },
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "loop",
            "step": 1,
            "writes": {
                "agent": {
                    "agent_outcome": AgentAction(
                        tool="search_api",
                        tool_input="query",
                        log="tool:search_api:query",
                    ),
                }
            },
        },
    )

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {
            "tools": {
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:query",
                        ),
                        "result for query",
                    )
                ],
            }
        },
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "input": "what is weather in sf",
            "agent_outcome": AgentAction(
                tool="search_api", tool_input="query", log="tool:search_api:query"
            ),
            "intermediate_steps": [
                (
                    AgentAction(
                        tool="search_api",
                        tool_input="query",
                        log="tool:search_api:query",
                    ),
                    "result for query",
                )
            ],
        },
        next=("agent",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "loop",
            "step": 2,
            "writes": {
                "tools": {
                    "intermediate_steps": [
                        (
                            AgentAction(
                                tool="search_api",
                                tool_input="query",
                                log="tool:search_api:query",
                            ),
                            "result for query",
                        )
                    ],
                }
            },
        },
    )

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {
            "agent": {
                "agent_outcome": AgentAction(
                    tool="search_api",
                    tool_input="another",
                    log="tool:search_api:another",
                ),
            }
        },
    ]

    # test w interrupt after all
    app_w_interrupt = workflow.compile(
        checkpointer=MemorySaverAssertImmutable(),
        interrupt_after="*",
    )
    config = {"configurable": {"thread_id": "4"}}
    llm.i = 0  # reset the llm

    assert [
        c for c in app_w_interrupt.stream({"input": "what is weather in sf"}, config)
    ] == [
        {
            "agent": {
                "agent_outcome": AgentAction(
                    tool="search_api", tool_input="query", log="tool:search_api:query"
                ),
            }
        },
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "input": "what is weather in sf",
            "agent_outcome": AgentAction(
                tool="search_api", tool_input="query", log="tool:search_api:query"
            ),
            "intermediate_steps": [],
        },
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "loop",
            "step": 1,
            "writes": {
                "agent": {
                    "agent_outcome": AgentAction(
                        tool="search_api",
                        tool_input="query",
                        log="tool:search_api:query",
                    ),
                }
            },
        },
    )

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {
            "tools": {
                "intermediate_steps": [
                    (
                        AgentAction(
                            tool="search_api",
                            tool_input="query",
                            log="tool:search_api:query",
                        ),
                        "result for query",
                    )
                ],
            }
        },
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "input": "what is weather in sf",
            "agent_outcome": AgentAction(
                tool="search_api", tool_input="query", log="tool:search_api:query"
            ),
            "intermediate_steps": [
                (
                    AgentAction(
                        tool="search_api",
                        tool_input="query",
                        log="tool:search_api:query",
                    ),
                    "result for query",
                )
            ],
        },
        next=("agent",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "loop",
            "step": 2,
            "writes": {
                "tools": {
                    "intermediate_steps": [
                        (
                            AgentAction(
                                tool="search_api",
                                tool_input="query",
                                log="tool:search_api:query",
                            ),
                            "result for query",
                        )
                    ],
                }
            },
        },
    )

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {
            "agent": {
                "agent_outcome": AgentAction(
                    tool="search_api",
                    tool_input="another",
                    log="tool:search_api:another",
                ),
            }
        },
    ]


def test_state_graph_w_config(snapshot: SnapshotAssertion) -> None:
    from langchain_core.agents import AgentAction, AgentFinish
    from langchain_core.language_models.fake import FakeStreamingListLLM
    from langchain_core.prompts import PromptTemplate
    from langchain_core.tools import tool

    class AgentState(TypedDict, total=False):
        input: str
        agent_outcome: Optional[Union[AgentAction, AgentFinish]]
        intermediate_steps: Annotated[list[tuple[AgentAction, str]], operator.add]

    class Config(TypedDict, total=False):
        tools: list[str]

    # Assemble the tools
    @tool()
    def search_api(query: str) -> str:
        """Searches the API for the query."""
        return f"result for {query}"

    tools = [search_api]

    # Construct the agent
    prompt = PromptTemplate.from_template("Hello!")

    llm = FakeStreamingListLLM(
        responses=[
            "tool:search_api:query",
            "tool:search_api:another",
            "finish:answer",
        ]
    )

    def agent_parser(input: str) -> dict[str, Union[AgentAction, AgentFinish]]:
        if input.startswith("finish"):
            _, answer = input.split(":")
            return {
                "agent_outcome": AgentFinish(
                    return_values={"answer": answer}, log=input
                )
            }
        else:
            _, tool_name, tool_input = input.split(":")
            return {
                "agent_outcome": AgentAction(
                    tool=tool_name, tool_input=tool_input, log=input
                )
            }

    agent = prompt | llm | agent_parser

    # Define tool execution logic
    def execute_tools(data: AgentState) -> dict:
        agent_action: AgentAction = data.pop("agent_outcome")
        observation = {t.name: t for t in tools}[agent_action.tool].invoke(
            agent_action.tool_input
        )
        return {"intermediate_steps": [(agent_action, observation)]}

    # Define decision-making logic
    def should_continue(data: AgentState) -> str:
        # Logic to decide whether to continue in the loop or exit
        if isinstance(data["agent_outcome"], AgentFinish):
            return "exit"
        else:
            return "continue"

    # Define a new graph
    workflow = StateGraph(AgentState, Config)

    workflow.add_node("agent", agent)
    workflow.add_node("tools", execute_tools)

    workflow.set_entry_point("agent")

    workflow.add_conditional_edges(
        "agent", should_continue, {"continue": "tools", "exit": END}
    )

    workflow.add_edge("tools", "agent")

    app = workflow.compile()

    assert app.config_schema().schema_json() == snapshot


def test_state_graph_few_shot() -> None:
    from langchain_core.language_models.fake_chat_models import (
        FakeMessagesListChatModel,
    )
    from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, ToolMessage
    from langchain_core.prompts import ChatPromptTemplate
    from langchain_core.tools import tool

    def filter_by_source(config: RunnableConfig) -> Dict[str, Any]:
        """This function is a trivial example that demonstrates that passing
        a Callable to metadata_filter works as expected.
        """
        return {"source": "loop"}

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

    class AgentState(BaseState):
        examples: Annotated[
            Sequence[BaseState],
            FewShotExamples[BaseState].configure(k=1, metadata_filter=filter_by_source),
        ]

    # Assemble the tools
    @tool()
    def search_api(query: str) -> str:
        """Searches the API for the query."""
        return f"result for {query}"

    tools = [search_api]

    prompt = ChatPromptTemplate.from_messages(
        [
            (
                "system",
                """You are a nice assistant.
Some examples of past conversations:
{examples}""",
            ),
            ("placeholder", "{messages}"),
        ]
    )

    model = FakeMessagesListChatModel(
        responses=[
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "query"},
                    },
                ],
            ),
            AIMessage(content="answer"),
        ]
    )

    def agent(state: AgentState, config: RunnableConfig) -> AgentState:
        # begin: testing code
        assert state["examples"] == config["configurable"]["expected_examples"]
        # end: testing code
        formatted = prompt.invoke(state)
        response = model.invoke(formatted)
        return {"messages": response}

    # Define decision-making logic
    def should_continue(data: AgentState) -> str:
        # Logic to decide whether to continue in the loop or exit
        if not data["messages"][-1].tool_calls:
            return "exit"
        else:
            return "continue"

    # Define a new graph
    workflow = StateGraph(AgentState)

    workflow.add_node("agent", agent)
    workflow.add_node("tools", ToolNode(tools))
    workflow.set_entry_point("agent")
    workflow.add_conditional_edges(
        "agent", should_continue, {"continue": "tools", "exit": END}
    )
    workflow.add_edge("tools", "agent")

    with SqliteSaver.from_conn_string(":memory:") as saver:
        app = workflow.compile(checkpointer=saver)

        first_messages = [
            HumanMessage(content="what is weather in sf", id=AnyStr()),
            AIMessage(
                content="",
                id=AnyStr(),
                tool_calls=[
                    {
                        "name": "search_api",
                        "args": {"query": "query"},
                        "id": "tool_call123",
                    }
                ],
            ),
            ToolMessage(
                content="result for query",
                name="search_api",
                id=AnyStr(),
                tool_call_id="tool_call123",
            ),
            AIMessage(content="answer", id=AnyStr()),
        ]
        assert app.invoke(
            {"messages": "what is weather in sf"},
            {
                "configurable": {
                    "thread_id": "1",
                    "expected_examples": [],
                },
            },
        ) == {"messages": first_messages}

        # get first checkpoint
        chkpnt_tuple_1 = saver.get_tuple({"configurable": {"thread_id": "1"}})
        config = chkpnt_tuple_1.config
        checkpoint = chkpnt_tuple_1.checkpoint
        metadata = chkpnt_tuple_1.metadata

        # not needed in application code, only for testing
        hiscored = list(saver.list(None, filter={"score": 1}))
        assert hiscored == []

        # mark as "good"
        metadata["score"] = 1
        saver.put(config, checkpoint, metadata)

        # not needed in application code, only for testing
        hiscored = list(saver.list(None, filter={"score": 1}))
        assert len(hiscored) == 1
        assert hiscored[0].checkpoint["channel_values"]["messages"] == first_messages

        second_messages = [
            HumanMessage(content="what is weather in la", id=AnyStr()),
            AIMessage(
                content="",
                id=AnyStr(),
                tool_calls=[
                    {
                        "name": "search_api",
                        "args": {"query": "query"},
                        "id": "tool_call123",
                    }
                ],
            ),
            ToolMessage(
                content="result for query",
                name="search_api",
                id=AnyStr(),
                tool_call_id="tool_call123",
            ),
            AIMessage(content="answer", id=AnyStr()),
        ]
        assert app.invoke(
            {"messages": "what is weather in la"},
            {
                "configurable": {
                    "thread_id": "2",
                    # below is only for testing purposes, not part of few shot api
                    "expected_examples": [{"messages": first_messages}],
                }
            },
        ) == {"messages": second_messages}

        # get first checkpoint
        chkpnt_tuple_2 = saver.get_tuple({"configurable": {"thread_id": "2"}})
        config = chkpnt_tuple_2.config
        checkpoint = chkpnt_tuple_2.checkpoint
        metadata = chkpnt_tuple_2.metadata

        # not needed in application code, only for testing
        hiscored = list(saver.list(None, filter={"score": 1}))
        assert len(hiscored) == 1

        # mark as "good"
        metadata["score"] = 1
        saver.put(config, checkpoint, metadata)

        hiscored = list(saver.list(None, filter={"score": 1}))
        assert len(hiscored) == 2

        assert app.invoke(
            {"messages": "what is weather in ny"},
            {
                "configurable": {
                    "thread_id": "3",
                    # below is only for testing purposes, not part of few shot api
                    "expected_examples": [{"messages": second_messages}],
                }
            },
        ) == {
            "messages": [
                HumanMessage(content="what is weather in ny", id=AnyStr()),
                AIMessage(
                    content="",
                    id=AnyStr(),
                    tool_calls=[
                        {
                            "name": "search_api",
                            "args": {"query": "query"},
                            "id": "tool_call123",
                        }
                    ],
                ),
                ToolMessage(
                    content="result for query",
                    name="search_api",
                    id=AnyStr(),
                    tool_call_id="tool_call123",
                ),
                AIMessage(content="answer", id=AnyStr()),
            ]
        }


def test_conditional_entrypoint_graph_state(snapshot: SnapshotAssertion) -> None:
    class AgentState(TypedDict, total=False):
        input: str
        output: str
        steps: Annotated[list[str], operator.add]

    def left(data: AgentState) -> AgentState:
        return {"output": data["input"] + "->left"}

    def right(data: AgentState) -> AgentState:
        return {"output": data["input"] + "->right"}

    def should_start(data: AgentState) -> str:
        assert data["steps"] == [], "Expected input to be read from the state"
        # Logic to decide where to start
        if len(data["input"]) > 10:
            return "go-right"
        else:
            return "go-left"

    # Define a new graph
    workflow = StateGraph(AgentState)

    workflow.add_node("left", left)
    workflow.add_node("right", right)

    workflow.set_conditional_entry_point(
        should_start, {"go-left": "left", "go-right": "right"}
    )

    workflow.add_conditional_edges("left", lambda data: END, {END: END})
    workflow.add_edge("right", END)

    app = workflow.compile()

    assert app.get_input_schema().schema_json() == snapshot
    assert app.get_output_schema().schema_json() == snapshot
    assert json.dumps(app.get_graph().to_json(), indent=2) == snapshot
    assert app.get_graph().draw_mermaid(with_styles=False) == snapshot

    assert app.invoke({"input": "what is weather in sf"}) == {
        "input": "what is weather in sf",
        "output": "what is weather in sf->right",
        "steps": [],
    }

    assert [*app.stream({"input": "what is weather in sf"})] == [
        {"right": {"output": "what is weather in sf->right"}},
    ]


def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
    from langchain_core.language_models.fake_chat_models import (
        FakeMessagesListChatModel,
    )
    from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
    from langchain_core.tools import tool

    class FakeFuntionChatModel(FakeMessagesListChatModel):
        def bind_tools(self, functions: list):
            return self

    @tool()
    def search_api(query: str) -> str:
        """Searches the API for the query."""
        return f"result for {query}"

    tools = [search_api]

    model = FakeFuntionChatModel(
        responses=[
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "query"},
                    },
                ],
            ),
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call234",
                        "name": "search_api",
                        "args": {"query": "another"},
                    },
                    {
                        "id": "tool_call567",
                        "name": "search_api",
                        "args": {"query": "a third one"},
                    },
                ],
            ),
            AIMessage(content="answer"),
        ]
    )

    app = create_tool_calling_executor(model, tools)

    assert app.get_input_schema().schema_json() == snapshot
    assert app.get_output_schema().schema_json() == snapshot
    assert json.dumps(app.get_graph().to_json(), indent=2) == snapshot
    assert app.get_graph().draw_mermaid(with_styles=False) == snapshot

    assert app.invoke(
        {"messages": [HumanMessage(content="what is weather in sf")]}
    ) == {
        "messages": [
            HumanMessage(content="what is weather in sf", id=AnyStr()),
            AIMessage(
                id=AnyStr(),
                content="",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "query"},
                    },
                ],
            ),
            ToolMessage(
                content="result for query",
                name="search_api",
                tool_call_id="tool_call123",
                id=AnyStr(),
            ),
            AIMessage(
                id=AnyStr(),
                content="",
                tool_calls=[
                    {
                        "id": "tool_call234",
                        "name": "search_api",
                        "args": {"query": "another"},
                    },
                    {
                        "id": "tool_call567",
                        "name": "search_api",
                        "args": {"query": "a third one"},
                    },
                ],
            ),
            ToolMessage(
                content="result for another",
                name="search_api",
                tool_call_id="tool_call234",
                id=AnyStr(),
            ),
            ToolMessage(
                content="result for a third one",
                name="search_api",
                tool_call_id="tool_call567",
                id=AnyStr(),
            ),
            AIMessage(content="answer", id=AnyStr()),
        ]
    }

    assert app.invoke(
        {"messages": [HumanMessage(content="what is weather in sf")]},
        {"recursion_limit": 2},
        debug=True,
    ) == {
        "messages": [
            HumanMessage(content="what is weather in sf", id=AnyStr()),
            AIMessage(
                content="Sorry, need more steps to process this request.", id=AnyStr()
            ),
        ]
    }

    model.i = 0  # reset the model

    assert app.invoke(
        {"messages": [HumanMessage(content="what is weather in sf")]},
        stream_mode="updates",
    ) == [
        {
            "agent": {
                "messages": [
                    AIMessage(
                        content="",
                        tool_calls=[
                            {
                                "id": "tool_call123",
                                "name": "search_api",
                                "args": {"query": "query"},
                            },
                        ],
                        id=AnyStr(),
                    )
                ]
            }
        },
        {
            "tools": {
                "messages": [
                    ToolMessage(
                        content="result for query",
                        name="search_api",
                        tool_call_id="tool_call123",
                        id=AnyStr(),
                    )
                ]
            }
        },
        {
            "agent": {
                "messages": [
                    AIMessage(
                        content="",
                        tool_calls=[
                            {
                                "id": "tool_call234",
                                "name": "search_api",
                                "args": {"query": "another"},
                            },
                            {
                                "id": "tool_call567",
                                "name": "search_api",
                                "args": {"query": "a third one"},
                            },
                        ],
                        id=AnyStr(),
                    )
                ]
            }
        },
        {
            "tools": {
                "messages": [
                    ToolMessage(
                        content="result for another",
                        name="search_api",
                        tool_call_id="tool_call234",
                        id=AnyStr(),
                    ),
                    ToolMessage(
                        content="result for a third one",
                        name="search_api",
                        tool_call_id="tool_call567",
                        id=AnyStr(),
                    ),
                ]
            }
        },
        {
            "agent": {
                "messages": [
                    AIMessage(
                        content="answer",
                        id=AnyStr(),
                    )
                ]
            }
        },
    ]

    assert [
        *app.stream({"messages": [HumanMessage(content="what is weather in sf")]})
    ] == [
        {
            "agent": {
                "messages": [
                    AIMessage(
                        id=AnyStr(),
                        content="",
                        tool_calls=[
                            {
                                "id": "tool_call123",
                                "name": "search_api",
                                "args": {"query": "query"},
                            },
                        ],
                    )
                ]
            }
        },
        {
            "tools": {
                "messages": [
                    ToolMessage(
                        content="result for query",
                        name="search_api",
                        tool_call_id="tool_call123",
                        id=AnyStr(),
                    )
                ]
            }
        },
        {
            "agent": {
                "messages": [
                    AIMessage(
                        id=AnyStr(),
                        content="",
                        tool_calls=[
                            {
                                "id": "tool_call234",
                                "name": "search_api",
                                "args": {"query": "another"},
                            },
                            {
                                "id": "tool_call567",
                                "name": "search_api",
                                "args": {"query": "a third one"},
                            },
                        ],
                    )
                ]
            }
        },
        {
            "tools": {
                "messages": [
                    ToolMessage(
                        content="result for another",
                        name="search_api",
                        tool_call_id="tool_call234",
                        id=AnyStr(),
                    ),
                    ToolMessage(
                        content="result for a third one",
                        name="search_api",
                        tool_call_id="tool_call567",
                        id=AnyStr(),
                    ),
                ]
            }
        },
        {"agent": {"messages": [AIMessage(content="answer", id=AnyStr())]}},
    ]


def test_prebuilt_chat(snapshot: SnapshotAssertion) -> None:
    from langchain_core.language_models.fake_chat_models import (
        FakeMessagesListChatModel,
    )
    from langchain_core.messages import AIMessage, FunctionMessage, HumanMessage
    from langchain_core.tools import tool

    class FakeFuntionChatModel(FakeMessagesListChatModel):
        def bind_functions(self, functions: list):
            return self

    @tool()
    def search_api(query: str) -> str:
        """Searches the API for the query."""
        return f"result for {query}"

    tools = [search_api]

    app = create_function_calling_executor(
        FakeFuntionChatModel(
            responses=[
                AIMessage(
                    content="",
                    additional_kwargs={
                        "function_call": {
                            "name": "search_api",
                            "arguments": json.dumps("query"),
                        }
                    },
                ),
                AIMessage(
                    content="",
                    additional_kwargs={
                        "function_call": {
                            "name": "search_api",
                            "arguments": json.dumps("another"),
                        }
                    },
                ),
                AIMessage(content="answer"),
            ]
        ),
        tools,
    )

    assert app.get_input_schema().schema_json() == snapshot
    assert app.get_output_schema().schema_json() == snapshot
    assert json.dumps(app.get_graph().to_json(), indent=2) == snapshot
    assert app.get_graph().draw_mermaid(with_styles=False) == snapshot

    assert app.invoke(
        {"messages": [HumanMessage(content="what is weather in sf")]}
    ) == {
        "messages": [
            HumanMessage(content="what is weather in sf", id=AnyStr()),
            AIMessage(
                id=AnyStr(),
                content="",
                additional_kwargs={
                    "function_call": {"name": "search_api", "arguments": '"query"'}
                },
            ),
            FunctionMessage(content="result for query", name="search_api", id=AnyStr()),
            AIMessage(
                id=AnyStr(),
                content="",
                additional_kwargs={
                    "function_call": {"name": "search_api", "arguments": '"another"'}
                },
            ),
            FunctionMessage(
                content="result for another", name="search_api", id=AnyStr()
            ),
            AIMessage(content="answer", id=AnyStr()),
        ]
    }

    assert [
        *app.stream({"messages": [HumanMessage(content="what is weather in sf")]})
    ] == [
        {
            "agent": {
                "messages": [
                    AIMessage(
                        id=AnyStr(),
                        content="",
                        additional_kwargs={
                            "function_call": {
                                "name": "search_api",
                                "arguments": '"query"',
                            }
                        },
                    )
                ]
            }
        },
        {
            "tools": {
                "messages": [
                    FunctionMessage(
                        content="result for query", name="search_api", id=AnyStr()
                    )
                ]
            }
        },
        {
            "agent": {
                "messages": [
                    AIMessage(
                        id=AnyStr(),
                        content="",
                        additional_kwargs={
                            "function_call": {
                                "name": "search_api",
                                "arguments": '"another"',
                            }
                        },
                    )
                ]
            }
        },
        {
            "tools": {
                "messages": [
                    FunctionMessage(
                        content="result for another", name="search_api", id=AnyStr()
                    )
                ]
            }
        },
        {"agent": {"messages": [AIMessage(content="answer", id=AnyStr())]}},
    ]


@pytest.mark.parametrize("serde", [NoopSerializer(), JsonPlusSerializer()])
def test_state_graph_packets(serde: SerializerProtocol) -> None:
    from langchain_core.language_models.fake_chat_models import (
        FakeMessagesListChatModel,
    )
    from langchain_core.messages import (
        AIMessage,
        BaseMessage,
        HumanMessage,
        ToolCall,
        ToolMessage,
    )
    from langchain_core.tools import tool

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

    @tool()
    def search_api(query: str) -> str:
        """Searches the API for the query."""
        return f"result for {query}"

    tools = [search_api]
    tools_by_name = {t.name: t for t in tools}

    model = FakeMessagesListChatModel(
        responses=[
            AIMessage(
                id="ai1",
                content="",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "query"},
                    },
                ],
            ),
            AIMessage(
                id="ai2",
                content="",
                tool_calls=[
                    {
                        "id": "tool_call234",
                        "name": "search_api",
                        "args": {"query": "another", "idx": 0},
                    },
                    {
                        "id": "tool_call567",
                        "name": "search_api",
                        "args": {"query": "a third one", "idx": 1},
                    },
                ],
            ),
            AIMessage(id="ai3", content="answer"),
        ]
    )

    def agent(data: AgentState) -> AgentState:
        return {
            "messages": model.invoke(data["messages"]),
            "something_extra": "hi there",
        }

    # Define decision-making logic
    def should_continue(data: AgentState) -> str:
        assert (
            data["something_extra"] == "hi there"
        ), "nodes can pass extra data to their cond edges, which isn't saved in state"
        # Logic to decide whether to continue in the loop or exit
        if tool_calls := data["messages"][-1].tool_calls:
            return [Send("tools", tool_call) for tool_call in tool_calls]
        else:
            return END

    def tools_node(tool_call: ToolCall, config: RunnableConfig) -> AgentState:
        time.sleep(tool_call["args"].get("idx", 0) / 10)
        output = tools_by_name[tool_call["name"]].invoke(tool_call["args"], config)
        return {
            "messages": ToolMessage(
                content=output, name=tool_call["name"], tool_call_id=tool_call["id"]
            )
        }

    # Define a new graph
    workflow = StateGraph(AgentState)

    # Define the two nodes we will cycle between
    workflow.add_node("agent", agent)
    workflow.add_node("tools", tools_node)

    # Set the entrypoint as `agent`
    # This means that this node is the first one called
    workflow.set_entry_point("agent")

    # We now add a conditional edge
    workflow.add_conditional_edges("agent", should_continue)

    # We now add a normal edge from `tools` to `agent`.
    # This means that after `tools` is called, `agent` node is called next.
    workflow.add_edge("tools", "agent")

    # Finally, we compile it!
    # This compiles it into a LangChain Runnable,
    # meaning you can use it as you would any other runnable
    app = workflow.compile()

    assert app.invoke({"messages": HumanMessage(content="what is weather in sf")}) == {
        "messages": [
            HumanMessage(content="what is weather in sf", id=AnyStr()),
            AIMessage(
                id="ai1",
                content="",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "query"},
                    },
                ],
            ),
            ToolMessage(
                content="result for query",
                name="search_api",
                id=AnyStr(),
                tool_call_id="tool_call123",
            ),
            AIMessage(
                id="ai2",
                content="",
                tool_calls=[
                    {
                        "id": "tool_call234",
                        "name": "search_api",
                        "args": {"query": "another", "idx": 0},
                    },
                    {
                        "id": "tool_call567",
                        "name": "search_api",
                        "args": {"query": "a third one", "idx": 1},
                    },
                ],
            ),
            ToolMessage(
                content="result for another",
                name="search_api",
                id=AnyStr(),
                tool_call_id="tool_call234",
            ),
            ToolMessage(
                content="result for a third one",
                name="search_api",
                id=AnyStr(),
                tool_call_id="tool_call567",
            ),
            AIMessage(content="answer", id="ai3"),
        ]
    }

    assert [
        c
        for c in app.stream(
            {"messages": [HumanMessage(content="what is weather in sf")]}
        )
    ] == [
        {
            "agent": {
                "messages": AIMessage(
                    id="ai1",
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call123",
                            "name": "search_api",
                            "args": {"query": "query"},
                        },
                    ],
                )
            },
        },
        {
            "tools": {
                "messages": ToolMessage(
                    content="result for query",
                    name="search_api",
                    id=AnyStr(),
                    tool_call_id="tool_call123",
                )
            }
        },
        {
            "agent": {
                "messages": AIMessage(
                    id="ai2",
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call234",
                            "name": "search_api",
                            "args": {"query": "another", "idx": 0},
                        },
                        {
                            "id": "tool_call567",
                            "name": "search_api",
                            "args": {"query": "a third one", "idx": 1},
                        },
                    ],
                )
            }
        },
        {
            "tools": {
                "messages": ToolMessage(
                    content="result for another",
                    name="search_api",
                    id=AnyStr(),
                    tool_call_id="tool_call234",
                )
            },
        },
        {
            "tools": {
                "messages": ToolMessage(
                    content="result for a third one",
                    name="search_api",
                    id=AnyStr(),
                    tool_call_id="tool_call567",
                ),
            },
        },
        {"agent": {"messages": AIMessage(content="answer", id="ai3")}},
    ]

    app_w_interrupt = workflow.compile(
        checkpointer=MemorySaverAssertImmutable(serde=serde),
        interrupt_after=["agent"],
    )
    config = {"configurable": {"thread_id": "1"}}

    assert [
        c
        for c in app_w_interrupt.stream(
            {"messages": HumanMessage(content="what is weather in sf")}, config
        )
    ] == [
        {
            "agent": {
                "messages": AIMessage(
                    id="ai1",
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call123",
                            "name": "search_api",
                            "args": {"query": "query"},
                        },
                    ],
                )
            }
        },
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "messages": [
                HumanMessage(
                    content="what is weather in sf",
                    id=AnyStr(),
                ),
                AIMessage(
                    id="ai1",
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call123",
                            "name": "search_api",
                            "args": {"query": "query"},
                        },
                    ],
                ),
            ]
        },
        next=("tools",),
        config=(app_w_interrupt.checkpointer.get_tuple(config)).config,
        created_at=(app_w_interrupt.checkpointer.get_tuple(config)).checkpoint["ts"],
        metadata={
            "source": "loop",
            "step": 1,
            "writes": {
                "agent": {
                    "messages": AIMessage(
                        id="ai1",
                        content="",
                        tool_calls=[
                            {
                                "id": "tool_call123",
                                "name": "search_api",
                                "args": {"query": "query"},
                            },
                        ],
                    )
                }
            },
        },
    )

    # modify ai message
    last_message = (app_w_interrupt.get_state(config)).values["messages"][-1]
    last_message.tool_calls[0]["args"]["query"] = "a different query"
    app_w_interrupt.update_state(
        config, {"messages": last_message, "something_extra": "hi there"}
    )

    # message was replaced instead of appended
    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "messages": [
                HumanMessage(
                    content="what is weather in sf",
                    id=AnyStr(),
                ),
                AIMessage(
                    id="ai1",
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call123",
                            "name": "search_api",
                            "args": {"query": "a different query"},
                        },
                    ],
                ),
            ]
        },
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=(app_w_interrupt.checkpointer.get_tuple(config)).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 2,
            "writes": {
                "agent": {
                    "messages": AIMessage(
                        id="ai1",
                        content="",
                        tool_calls=[
                            {
                                "id": "tool_call123",
                                "name": "search_api",
                                "args": {"query": "a different query"},
                            },
                        ],
                    ),
                    "something_extra": "hi there",
                }
            },
        },
    )

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {
            "tools": {
                "messages": ToolMessage(
                    content="result for a different query",
                    name="search_api",
                    id=AnyStr(),
                    tool_call_id="tool_call123",
                )
            }
        },
        {
            "agent": {
                "messages": AIMessage(
                    id="ai2",
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call234",
                            "name": "search_api",
                            "args": {"query": "another", "idx": 0},
                        },
                        {
                            "id": "tool_call567",
                            "name": "search_api",
                            "args": {"query": "a third one", "idx": 1},
                        },
                    ],
                )
            },
        },
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "messages": [
                HumanMessage(
                    content="what is weather in sf",
                    id=AnyStr(),
                ),
                AIMessage(
                    id="ai1",
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call123",
                            "name": "search_api",
                            "args": {"query": "a different query"},
                        },
                    ],
                ),
                ToolMessage(
                    content="result for a different query",
                    name="search_api",
                    id=AnyStr(),
                    tool_call_id="tool_call123",
                ),
                AIMessage(
                    id="ai2",
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call234",
                            "name": "search_api",
                            "args": {"query": "another", "idx": 0},
                        },
                        {
                            "id": "tool_call567",
                            "name": "search_api",
                            "args": {"query": "a third one", "idx": 1},
                        },
                    ],
                ),
            ]
        },
        next=("tools", "tools"),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=(app_w_interrupt.checkpointer.get_tuple(config)).checkpoint["ts"],
        metadata={
            "source": "loop",
            "step": 4,
            "writes": {
                "agent": {
                    "messages": AIMessage(
                        id="ai2",
                        content="",
                        tool_calls=[
                            {
                                "id": "tool_call234",
                                "name": "search_api",
                                "args": {"query": "another", "idx": 0},
                            },
                            {
                                "id": "tool_call567",
                                "name": "search_api",
                                "args": {"query": "a third one", "idx": 1},
                            },
                        ],
                    )
                },
            },
        },
    )

    app_w_interrupt.update_state(
        config,
        {
            "messages": AIMessage(content="answer", id="ai2"),
            "something_extra": "hi there",
        },
    )

    # replaces message even if object identity is different, as long as id is the same
    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "messages": [
                HumanMessage(
                    content="what is weather in sf",
                    id=AnyStr(),
                ),
                AIMessage(
                    id="ai1",
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call123",
                            "name": "search_api",
                            "args": {"query": "a different query"},
                        },
                    ],
                ),
                ToolMessage(
                    content="result for a different query",
                    name="search_api",
                    id=AnyStr(),
                    tool_call_id="tool_call123",
                ),
                AIMessage(content="answer", id="ai2"),
            ]
        },
        next=(),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=(app_w_interrupt.checkpointer.get_tuple(config)).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 5,
            "writes": {
                "agent": {
                    "messages": AIMessage(content="answer", id="ai2"),
                    "something_extra": "hi there",
                }
            },
        },
    )


def test_message_graph(
    snapshot: SnapshotAssertion,
    deterministic_uuids: MockerFixture,
) -> None:
    from copy import deepcopy

    from langchain_core.callbacks import CallbackManagerForLLMRun
    from langchain_core.language_models.fake_chat_models import (
        FakeMessagesListChatModel,
    )
    from langchain_core.messages import (
        AIMessage,
        BaseMessage,
        HumanMessage,
        ToolMessage,
    )
    from langchain_core.outputs import ChatGeneration, ChatResult
    from langchain_core.tools import tool

    class FakeFuntionChatModel(FakeMessagesListChatModel):
        def bind_functions(self, functions: list):
            return self

        def _generate(
            self,
            messages: list[BaseMessage],
            stop: Optional[list[str]] = None,
            run_manager: Optional[CallbackManagerForLLMRun] = None,
            **kwargs: Any,
        ) -> ChatResult:
            response = deepcopy(self.responses[self.i])
            if self.i < len(self.responses) - 1:
                self.i += 1
            else:
                self.i = 0
            generation = ChatGeneration(message=response)
            return ChatResult(generations=[generation])

    @tool()
    def search_api(query: str) -> str:
        """Searches the API for the query."""
        return f"result for {query}"

    tools = [search_api]

    model = FakeFuntionChatModel(
        responses=[
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "query"},
                    }
                ],
                id="ai1",
            ),
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call456",
                        "name": "search_api",
                        "args": {"query": "another"},
                    }
                ],
                id="ai2",
            ),
            AIMessage(content="answer", id="ai3"),
        ]
    )

    # Define the function that determines whether to continue or not
    def should_continue(messages):
        last_message = messages[-1]
        # If there is no function call, then we finish
        if not last_message.tool_calls:
            return "end"
        # Otherwise if there is, we continue
        else:
            return "continue"

    # Define a new graph
    workflow = MessageGraph()

    # Define the two nodes we will cycle between
    workflow.add_node("agent", model)
    workflow.add_node("tools", ToolNode(tools))

    # Set the entrypoint as `agent`
    # This means that this node is the first one called
    workflow.set_entry_point("agent")

    # We now add a conditional edge
    workflow.add_conditional_edges(
        # First, we define the start node. We use `agent`.
        # This means these are the edges taken after the `agent` node is called.
        "agent",
        # Next, we pass in the function that will determine which node is called next.
        should_continue,
        # Finally we pass in a mapping.
        # The keys are strings, and the values are other nodes.
        # END is a special node marking that the graph should finish.
        # What will happen is we will call `should_continue`, and then the output of that
        # will be matched against the keys in this mapping.
        # Based on which one it matches, that node will then be called.
        {
            # If `tools`, then we call the tool node.
            "continue": "tools",
            # Otherwise we finish.
            "end": END,
        },
    )

    # We now add a normal edge from `tools` to `agent`.
    # This means that after `tools` is called, `agent` node is called next.
    workflow.add_edge("tools", "agent")

    # Finally, we compile it!
    # This compiles it into a LangChain Runnable,
    # meaning you can use it as you would any other runnable
    app = workflow.compile()

    assert app.get_input_schema().schema_json() == snapshot
    assert app.get_output_schema().schema_json() == snapshot
    assert json.dumps(app.get_graph().to_json(), indent=2) == snapshot
    assert app.get_graph().draw_mermaid(with_styles=False) == snapshot

    assert app.invoke(HumanMessage(content="what is weather in sf")) == [
        HumanMessage(
            content="what is weather in sf",
            id="00000000-0000-4000-8000-000000000002",  # adds missing ids
        ),
        AIMessage(
            content="",
            tool_calls=[
                {
                    "id": "tool_call123",
                    "name": "search_api",
                    "args": {"query": "query"},
                }
            ],
            id="ai1",  # respects ids passed in
        ),
        ToolMessage(
            content="result for query",
            name="search_api",
            tool_call_id="tool_call123",
            id="00000000-0000-4000-8000-000000000011",
        ),
        AIMessage(
            content="",
            tool_calls=[
                {
                    "id": "tool_call456",
                    "name": "search_api",
                    "args": {"query": "another"},
                }
            ],
            id="ai2",
        ),
        ToolMessage(
            content="result for another",
            name="search_api",
            tool_call_id="tool_call456",
            id="00000000-0000-4000-8000-000000000020",
        ),
        AIMessage(content="answer", id="ai3"),
    ]

    assert [*app.stream([HumanMessage(content="what is weather in sf")])] == [
        {
            "agent": AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "query"},
                    }
                ],
                id="ai1",
            )
        },
        {
            "tools": [
                ToolMessage(
                    content="result for query",
                    name="search_api",
                    tool_call_id="tool_call123",
                    id="00000000-0000-4000-8000-000000000036",
                )
            ]
        },
        {
            "agent": AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call456",
                        "name": "search_api",
                        "args": {"query": "another"},
                    }
                ],
                id="ai2",
            )
        },
        {
            "tools": [
                ToolMessage(
                    content="result for another",
                    name="search_api",
                    tool_call_id="tool_call456",
                    id="00000000-0000-4000-8000-000000000045",
                )
            ]
        },
        {"agent": AIMessage(content="answer", id="ai3")},
    ]

    app_w_interrupt = workflow.compile(
        checkpointer=MemorySaverAssertImmutable(),
        interrupt_after=["agent"],
    )
    config = {"configurable": {"thread_id": "1"}}

    assert [
        c for c in app_w_interrupt.stream(("human", "what is weather in sf"), config)
    ] == [
        {
            "agent": AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "query"},
                    }
                ],
                id="ai1",
            )
        },
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
            HumanMessage(content="what is weather in sf", id=AnyStr()),
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "query"},
                    }
                ],
                id="ai1",
            ),
        ],
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "loop",
            "step": 1,
            "writes": {
                "agent": AIMessage(
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call123",
                            "name": "search_api",
                            "args": {"query": "query"},
                        }
                    ],
                    id="ai1",
                )
            },
        },
    )

    # modify ai message
    last_message = app_w_interrupt.get_state(config).values[-1]
    last_message.tool_calls[0]["args"] = {"query": "a different query"}
    next_config = app_w_interrupt.update_state(config, last_message)

    # message was replaced instead of appended
    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
            HumanMessage(content="what is weather in sf", id=AnyStr()),
            AIMessage(
                content="",
                id="ai1",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "a different query"},
                    }
                ],
            ),
        ],
        next=("tools",),
        config=next_config,
        created_at=AnyStr(),
        metadata={
            "source": "update",
            "step": 2,
            "writes": {
                "agent": AIMessage(
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call123",
                            "name": "search_api",
                            "args": {"query": "a different query"},
                        }
                    ],
                    id="ai1",
                )
            },
        },
    )

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {
            "tools": [
                ToolMessage(
                    content="result for a different query",
                    name="search_api",
                    tool_call_id="tool_call123",
                    id=AnyStr(),
                )
            ]
        },
        {
            "agent": AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call456",
                        "name": "search_api",
                        "args": {"query": "another"},
                    }
                ],
                id="ai2",
            )
        },
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
            HumanMessage(
                content="what is weather in sf",
                id=AnyStr(),
            ),
            AIMessage(
                content="",
                id="ai1",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "a different query"},
                    }
                ],
            ),
            ToolMessage(
                content="result for a different query",
                name="search_api",
                tool_call_id="tool_call123",
                id=AnyStr(),
            ),
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call456",
                        "name": "search_api",
                        "args": {"query": "another"},
                    }
                ],
                id="ai2",
            ),
        ],
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "loop",
            "step": 4,
            "writes": {
                "agent": AIMessage(
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call456",
                            "name": "search_api",
                            "args": {"query": "another"},
                        }
                    ],
                    id="ai2",
                )
            },
        },
    )

    app_w_interrupt.update_state(
        config,
        AIMessage(content="answer", id="ai2"),  # replace existing message
    )

    # replaces message even if object identity is different, as long as id is the same
    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
            HumanMessage(
                content="what is weather in sf",
                id=AnyStr(),
            ),
            AIMessage(
                content="",
                id="ai1",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "a different query"},
                    }
                ],
            ),
            ToolMessage(
                content="result for a different query",
                name="search_api",
                tool_call_id="tool_call123",
                id=AnyStr(),
            ),
            AIMessage(content="answer", id="ai2"),
        ],
        next=(),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 5,
            "writes": {"agent": AIMessage(content="answer", id="ai2")},
        },
    )

    app_w_interrupt = workflow.compile(
        checkpointer=MemorySaverAssertImmutable(),
        interrupt_before=["tools"],
    )
    config = {"configurable": {"thread_id": "2"}}
    model.i = 0  # reset the llm

    assert [c for c in app_w_interrupt.stream("what is weather in sf", config)] == [
        {
            "agent": AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "query"},
                    }
                ],
                id="ai1",
            )
        },
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
            HumanMessage(
                content="what is weather in sf",
                id=AnyStr(),
            ),
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "query"},
                    }
                ],
                id="ai1",
            ),
        ],
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "loop",
            "step": 1,
            "writes": {
                "agent": AIMessage(
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call123",
                            "name": "search_api",
                            "args": {"query": "query"},
                        }
                    ],
                    id="ai1",
                )
            },
        },
    )

    # modify ai message
    last_message = app_w_interrupt.get_state(config).values[-1]
    last_message.tool_calls[0]["args"] = {"query": "a different query"}
    app_w_interrupt.update_state(config, last_message)

    # message was replaced instead of appended
    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
            HumanMessage(
                content="what is weather in sf",
                id=AnyStr(),
            ),
            AIMessage(
                content="",
                id="ai1",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "a different query"},
                    }
                ],
            ),
        ],
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 2,
            "writes": {
                "agent": AIMessage(
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call123",
                            "name": "search_api",
                            "args": {"query": "a different query"},
                        }
                    ],
                    id="ai1",
                )
            },
        },
    )

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {
            "tools": [
                ToolMessage(
                    content="result for a different query",
                    name="search_api",
                    tool_call_id="tool_call123",
                    id=AnyStr(),
                )
            ]
        },
        {
            "agent": AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call456",
                        "name": "search_api",
                        "args": {"query": "another"},
                    }
                ],
                id="ai2",
            )
        },
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
            HumanMessage(
                content="what is weather in sf",
                id=AnyStr(),
            ),
            AIMessage(
                content="",
                id="ai1",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "a different query"},
                    }
                ],
            ),
            ToolMessage(
                content="result for a different query",
                name="search_api",
                tool_call_id="tool_call123",
                id=AnyStr(),
            ),
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call456",
                        "name": "search_api",
                        "args": {"query": "another"},
                    }
                ],
                id="ai2",
            ),
        ],
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "loop",
            "step": 4,
            "writes": {
                "agent": AIMessage(
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call456",
                            "name": "search_api",
                            "args": {"query": "another"},
                        }
                    ],
                    id="ai2",
                )
            },
        },
    )

    app_w_interrupt.update_state(
        config,
        AIMessage(content="answer", id="ai2"),
    )

    # replaces message even if object identity is different, as long as id is the same
    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
            HumanMessage(
                content="what is weather in sf",
                id=AnyStr(),
            ),
            AIMessage(
                content="",
                id="ai1",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "a different query"},
                    }
                ],
            ),
            ToolMessage(
                content="result for a different query",
                name="search_api",
                tool_call_id="tool_call123",
                id=AnyStr(),
            ),
            AIMessage(content="answer", id="ai2"),
        ],
        next=(),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 5,
            "writes": {"agent": AIMessage(content="answer", id="ai2")},
        },
    )

    # add an extra message as if it came from "tools" node
    app_w_interrupt.update_state(config, ("ai", "an extra message"), as_node="tools")

    # extra message is coerced BaseMessge and appended
    # now the next node is "agent" per the graph edges
    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
            HumanMessage(
                content="what is weather in sf",
                id=AnyStr(),
            ),
            AIMessage(
                content="",
                id="ai1",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "a different query"},
                    }
                ],
            ),
            ToolMessage(
                content="result for a different query",
                name="search_api",
                tool_call_id="tool_call123",
                id=AnyStr(),
            ),
            AIMessage(content="answer", id="ai2"),
            AIMessage(content="an extra message", id=AnyStr()),
        ],
        next=("agent",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 6,
            "writes": {"tools": ("ai", "an extra message")},
        },
    )


def test_root_graph(
    snapshot: SnapshotAssertion,
    deterministic_uuids: MockerFixture,
) -> None:
    from copy import deepcopy

    from langchain_core.callbacks import CallbackManagerForLLMRun
    from langchain_core.language_models.fake_chat_models import (
        FakeMessagesListChatModel,
    )
    from langchain_core.messages import (
        AIMessage,
        BaseMessage,
        HumanMessage,
        ToolMessage,
    )
    from langchain_core.outputs import ChatGeneration, ChatResult
    from langchain_core.tools import tool

    class FakeFuntionChatModel(FakeMessagesListChatModel):
        def bind_functions(self, functions: list):
            return self

        def _generate(
            self,
            messages: list[BaseMessage],
            stop: Optional[list[str]] = None,
            run_manager: Optional[CallbackManagerForLLMRun] = None,
            **kwargs: Any,
        ) -> ChatResult:
            response = deepcopy(self.responses[self.i])
            if self.i < len(self.responses) - 1:
                self.i += 1
            else:
                self.i = 0
            generation = ChatGeneration(message=response)
            return ChatResult(generations=[generation])

    @tool()
    def search_api(query: str) -> str:
        """Searches the API for the query."""
        return f"result for {query}"

    tools = [search_api]

    model = FakeFuntionChatModel(
        responses=[
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "query"},
                    }
                ],
                id="ai1",
            ),
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call456",
                        "name": "search_api",
                        "args": {"query": "another"},
                    }
                ],
                id="ai2",
            ),
            AIMessage(content="answer", id="ai3"),
        ]
    )

    # Define the function that determines whether to continue or not
    def should_continue(messages):
        last_message = messages[-1]
        # If there is no function call, then we finish
        if not last_message.tool_calls:
            return "end"
        # Otherwise if there is, we continue
        else:
            return "continue"

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

    # Define a new graph
    workflow = StateGraph(State)

    # Define the two nodes we will cycle between
    workflow.add_node("agent", model)
    workflow.add_node("tools", ToolNode(tools))

    # Set the entrypoint as `agent`
    # This means that this node is the first one called
    workflow.set_entry_point("agent")

    # We now add a conditional edge
    workflow.add_conditional_edges(
        # First, we define the start node. We use `agent`.
        # This means these are the edges taken after the `agent` node is called.
        "agent",
        # Next, we pass in the function that will determine which node is called next.
        should_continue,
        # Finally we pass in a mapping.
        # The keys are strings, and the values are other nodes.
        # END is a special node marking that the graph should finish.
        # What will happen is we will call `should_continue`, and then the output of that
        # will be matched against the keys in this mapping.
        # Based on which one it matches, that node will then be called.
        {
            # If `tools`, then we call the tool node.
            "continue": "tools",
            # Otherwise we finish.
            "end": END,
        },
    )

    # We now add a normal edge from `tools` to `agent`.
    # This means that after `tools` is called, `agent` node is called next.
    workflow.add_edge("tools", "agent")

    # Finally, we compile it!
    # This compiles it into a LangChain Runnable,
    # meaning you can use it as you would any other runnable
    app = workflow.compile()

    assert app.invoke(HumanMessage(content="what is weather in sf")) == [
        HumanMessage(
            content="what is weather in sf",
            id="00000000-0000-4000-8000-000000000002",  # adds missing ids
        ),
        AIMessage(
            content="",
            tool_calls=[
                {
                    "id": "tool_call123",
                    "name": "search_api",
                    "args": {"query": "query"},
                }
            ],
            id="ai1",  # respects ids passed in
        ),
        ToolMessage(
            content="result for query",
            name="search_api",
            tool_call_id="tool_call123",
            id="00000000-0000-4000-8000-000000000011",
        ),
        AIMessage(
            content="",
            tool_calls=[
                {
                    "id": "tool_call456",
                    "name": "search_api",
                    "args": {"query": "another"},
                }
            ],
            id="ai2",
        ),
        ToolMessage(
            content="result for another",
            name="search_api",
            tool_call_id="tool_call456",
            id="00000000-0000-4000-8000-000000000020",
        ),
        AIMessage(content="answer", id="ai3"),
    ]

    assert [*app.stream([HumanMessage(content="what is weather in sf")])] == [
        {
            "agent": AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "query"},
                    }
                ],
                id="ai1",
            )
        },
        {
            "tools": [
                ToolMessage(
                    content="result for query",
                    name="search_api",
                    tool_call_id="tool_call123",
                    id="00000000-0000-4000-8000-000000000036",
                )
            ]
        },
        {
            "agent": AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call456",
                        "name": "search_api",
                        "args": {"query": "another"},
                    }
                ],
                id="ai2",
            )
        },
        {
            "tools": [
                ToolMessage(
                    content="result for another",
                    name="search_api",
                    tool_call_id="tool_call456",
                    id="00000000-0000-4000-8000-000000000045",
                )
            ]
        },
        {"agent": AIMessage(content="answer", id="ai3")},
    ]

    app_w_interrupt = workflow.compile(
        checkpointer=MemorySaverAssertImmutable(),
        interrupt_after=["agent"],
    )
    config = {"configurable": {"thread_id": "1"}}

    assert [
        c for c in app_w_interrupt.stream(("human", "what is weather in sf"), config)
    ] == [
        {
            "agent": AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "query"},
                    }
                ],
                id="ai1",
            )
        },
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
            HumanMessage(content="what is weather in sf", id=AnyStr()),
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "query"},
                    }
                ],
                id="ai1",
            ),
        ],
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "loop",
            "step": 1,
            "writes": {
                "agent": AIMessage(
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call123",
                            "name": "search_api",
                            "args": {"query": "query"},
                        }
                    ],
                    id="ai1",
                )
            },
        },
    )

    # modify ai message
    last_message = app_w_interrupt.get_state(config).values[-1]
    last_message.tool_calls[0]["args"] = {"query": "a different query"}
    next_config = app_w_interrupt.update_state(config, last_message)

    # message was replaced instead of appended
    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
            HumanMessage(content="what is weather in sf", id=AnyStr()),
            AIMessage(
                content="",
                id="ai1",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "a different query"},
                    }
                ],
            ),
        ],
        next=("tools",),
        config=next_config,
        created_at=AnyStr(),
        metadata={
            "source": "update",
            "step": 2,
            "writes": {
                "agent": AIMessage(
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call123",
                            "name": "search_api",
                            "args": {"query": "a different query"},
                        }
                    ],
                    id="ai1",
                )
            },
        },
    )

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {
            "tools": [
                ToolMessage(
                    content="result for a different query",
                    name="search_api",
                    tool_call_id="tool_call123",
                    id=AnyStr(),
                )
            ]
        },
        {
            "agent": AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call456",
                        "name": "search_api",
                        "args": {"query": "another"},
                    }
                ],
                id="ai2",
            )
        },
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
            HumanMessage(
                content="what is weather in sf",
                id=AnyStr(),
            ),
            AIMessage(
                content="",
                id="ai1",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "a different query"},
                    }
                ],
            ),
            ToolMessage(
                content="result for a different query",
                name="search_api",
                tool_call_id="tool_call123",
                id=AnyStr(),
            ),
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call456",
                        "name": "search_api",
                        "args": {"query": "another"},
                    }
                ],
                id="ai2",
            ),
        ],
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "loop",
            "step": 4,
            "writes": {
                "agent": AIMessage(
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call456",
                            "name": "search_api",
                            "args": {"query": "another"},
                        }
                    ],
                    id="ai2",
                )
            },
        },
    )

    app_w_interrupt.update_state(
        config,
        AIMessage(content="answer", id="ai2"),  # replace existing message
    )

    # replaces message even if object identity is different, as long as id is the same
    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
            HumanMessage(
                content="what is weather in sf",
                id=AnyStr(),
            ),
            AIMessage(
                content="",
                id="ai1",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "a different query"},
                    }
                ],
            ),
            ToolMessage(
                content="result for a different query",
                name="search_api",
                tool_call_id="tool_call123",
                id=AnyStr(),
            ),
            AIMessage(content="answer", id="ai2"),
        ],
        next=(),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 5,
            "writes": {"agent": AIMessage(content="answer", id="ai2")},
        },
    )

    app_w_interrupt = workflow.compile(
        checkpointer=MemorySaverAssertImmutable(),
        interrupt_before=["tools"],
    )
    config = {"configurable": {"thread_id": "2"}}
    model.i = 0  # reset the llm

    assert [c for c in app_w_interrupt.stream("what is weather in sf", config)] == [
        {
            "agent": AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "query"},
                    }
                ],
                id="ai1",
            )
        },
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
            HumanMessage(
                content="what is weather in sf",
                id=AnyStr(),
            ),
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "query"},
                    }
                ],
                id="ai1",
            ),
        ],
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "loop",
            "step": 1,
            "writes": {
                "agent": AIMessage(
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call123",
                            "name": "search_api",
                            "args": {"query": "query"},
                        }
                    ],
                    id="ai1",
                )
            },
        },
    )

    # modify ai message
    last_message = app_w_interrupt.get_state(config).values[-1]
    last_message.tool_calls[0]["args"] = {"query": "a different query"}
    app_w_interrupt.update_state(config, last_message)

    # message was replaced instead of appended
    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
            HumanMessage(
                content="what is weather in sf",
                id=AnyStr(),
            ),
            AIMessage(
                content="",
                id="ai1",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "a different query"},
                    }
                ],
            ),
        ],
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 2,
            "writes": {
                "agent": AIMessage(
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call123",
                            "name": "search_api",
                            "args": {"query": "a different query"},
                        }
                    ],
                    id="ai1",
                )
            },
        },
    )

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {
            "tools": [
                ToolMessage(
                    content="result for a different query",
                    name="search_api",
                    tool_call_id="tool_call123",
                    id=AnyStr(),
                )
            ]
        },
        {
            "agent": AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call456",
                        "name": "search_api",
                        "args": {"query": "another"},
                    }
                ],
                id="ai2",
            )
        },
    ]

    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
            HumanMessage(
                content="what is weather in sf",
                id=AnyStr(),
            ),
            AIMessage(
                content="",
                id="ai1",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "a different query"},
                    }
                ],
            ),
            ToolMessage(
                content="result for a different query",
                name="search_api",
                tool_call_id="tool_call123",
                id=AnyStr(),
            ),
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call456",
                        "name": "search_api",
                        "args": {"query": "another"},
                    }
                ],
                id="ai2",
            ),
        ],
        next=("tools",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "loop",
            "step": 4,
            "writes": {
                "agent": AIMessage(
                    content="",
                    tool_calls=[
                        {
                            "id": "tool_call456",
                            "name": "search_api",
                            "args": {"query": "another"},
                        }
                    ],
                    id="ai2",
                )
            },
        },
    )

    app_w_interrupt.update_state(
        config,
        AIMessage(content="answer", id="ai2"),
    )

    # replaces message even if object identity is different, as long as id is the same
    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
            HumanMessage(
                content="what is weather in sf",
                id=AnyStr(),
            ),
            AIMessage(
                content="",
                id="ai1",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "a different query"},
                    }
                ],
            ),
            ToolMessage(
                content="result for a different query",
                name="search_api",
                tool_call_id="tool_call123",
                id=AnyStr(),
            ),
            AIMessage(content="answer", id="ai2"),
        ],
        next=(),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 5,
            "writes": {"agent": AIMessage(content="answer", id="ai2")},
        },
    )

    # add an extra message as if it came from "tools" node
    app_w_interrupt.update_state(config, ("ai", "an extra message"), as_node="tools")

    # extra message is coerced BaseMessge and appended
    # now the next node is "agent" per the graph edges
    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
            HumanMessage(
                content="what is weather in sf",
                id=AnyStr(),
            ),
            AIMessage(
                content="",
                id="ai1",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "a different query"},
                    }
                ],
            ),
            ToolMessage(
                content="result for a different query",
                name="search_api",
                tool_call_id="tool_call123",
                id=AnyStr(),
            ),
            AIMessage(content="answer", id="ai2"),
            AIMessage(content="an extra message", id=AnyStr()),
        ],
        next=("agent",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 6,
            "writes": {"tools": ("ai", "an extra message")},
        },
    )

    # create new graph with one more state key, reuse previous thread history

    def simple_add(left, right):
        if not isinstance(right, list):
            right = [right]
        return left + right

    class MoreState(TypedDict):
        __root__: Annotated[list[BaseMessage], simple_add]
        something_else: str

    # Define a new graph
    new_workflow = StateGraph(MoreState)
    new_workflow.add_node(
        "agent", RunnableMap(__root__=RunnablePick("__root__") | model)
    )
    new_workflow.add_node(
        "tools", RunnableMap(__root__=RunnablePick("__root__") | ToolNode(tools))
    )
    new_workflow.set_entry_point("agent")
    new_workflow.add_conditional_edges(
        "agent",
        RunnablePick("__root__") | should_continue,
        {
            # If `tools`, then we call the tool node.
            "continue": "tools",
            # Otherwise we finish.
            "end": END,
        },
    )
    new_workflow.add_edge("tools", "agent")
    new_app = new_workflow.compile(checkpointer=app_w_interrupt.checkpointer)
    model.i = 0  # reset the llm

    # previous state is converted to new schema
    assert new_app.get_state(config) == StateSnapshot(
        values={
            "__root__": [
                HumanMessage(
                    content="what is weather in sf",
                    id=AnyStr(),
                ),
                AIMessage(
                    content="",
                    id="ai1",
                    tool_calls=[
                        {
                            "id": "tool_call123",
                            "name": "search_api",
                            "args": {"query": "a different query"},
                        }
                    ],
                ),
                ToolMessage(
                    content="result for a different query",
                    name="search_api",
                    tool_call_id="tool_call123",
                    id=AnyStr(),
                ),
                AIMessage(content="answer", id="ai2"),
                AIMessage(content="an extra message", id=AnyStr()),
            ]
        },
        next=("agent",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 6,
            "writes": {"tools": ("ai", "an extra message")},
        },
    )

    # new input is merged to old state
    assert new_app.invoke(
        {
            "__root__": [HumanMessage(content="what is weather in la")],
            "something_else": "value",
        },
        config,
        interrupt_before=["agent"],
    ) == {
        "__root__": [
            HumanMessage(
                content="what is weather in sf",
                id="00000000-0000-4000-8000-000000000077",
            ),
            AIMessage(
                content="",
                id="ai1",
                tool_calls=[
                    {
                        "name": "search_api",
                        "args": {"query": "a different query"},
                        "id": "tool_call123",
                    }
                ],
            ),
            ToolMessage(
                content="result for a different query",
                name="search_api",
                id="00000000-0000-4000-8000-000000000091",
                tool_call_id="tool_call123",
            ),
            AIMessage(content="answer", id="ai2"),
            AIMessage(
                content="an extra message", id="00000000-0000-4000-8000-000000000101"
            ),
            HumanMessage(content="what is weather in la"),
        ],
        "something_else": "value",
    }


def test_in_one_fan_out_out_one_graph_state() -> None:
    def sorted_add(x: list[str], y: list[str]) -> list[str]:
        return sorted(operator.add(x, y))

    class State(TypedDict, total=False):
        query: str
        answer: str
        docs: Annotated[list[str], sorted_add]

    def rewrite_query(data: State) -> State:
        return {"query": f'query: {data["query"]}'}

    def retriever_one(data: State) -> State:
        # timer ensures stream output order is stable
        # also, it confirms that the update order is not dependent on finishing order
        # instead being defined by the order of the nodes/edges in the graph definition
        # ie. stable between invocations
        time.sleep(0.1)
        return {"docs": ["doc1", "doc2"]}

    def retriever_two(data: State) -> State:
        return {"docs": ["doc3", "doc4"]}

    def qa(data: State) -> State:
        return {"answer": ",".join(data["docs"])}

    workflow = StateGraph(State)

    workflow.add_node("rewrite_query", rewrite_query)
    workflow.add_node("retriever_one", retriever_one)
    workflow.add_node("retriever_two", retriever_two)
    workflow.add_node("qa", qa)

    workflow.set_entry_point("rewrite_query")
    workflow.add_edge("rewrite_query", "retriever_one")
    workflow.add_edge("rewrite_query", "retriever_two")
    workflow.add_edge("retriever_one", "qa")
    workflow.add_edge("retriever_two", "qa")
    workflow.set_finish_point("qa")

    app = workflow.compile()

    assert app.invoke({"query": "what is weather in sf"}) == {
        "query": "query: what is weather in sf",
        "docs": ["doc1", "doc2", "doc3", "doc4"],
        "answer": "doc1,doc2,doc3,doc4",
    }

    assert [*app.stream({"query": "what is weather in sf"})] == [
        {"rewrite_query": {"query": "query: what is weather in sf"}},
        {"retriever_two": {"docs": ["doc3", "doc4"]}},
        {"retriever_one": {"docs": ["doc1", "doc2"]}},
        {"qa": {"answer": "doc1,doc2,doc3,doc4"}},
    ]

    assert [*app.stream({"query": "what is weather in sf"}, stream_mode="values")] == [
        {"query": "what is weather in sf", "docs": []},
        {"query": "query: what is weather in sf", "docs": []},
        {
            "query": "query: what is weather in sf",
            "docs": ["doc1", "doc2", "doc3", "doc4"],
        },
        {
            "query": "query: what is weather in sf",
            "docs": ["doc1", "doc2", "doc3", "doc4"],
            "answer": "doc1,doc2,doc3,doc4",
        },
    ]

    assert [
        *app.stream(
            {"query": "what is weather in sf"},
            stream_mode=["values", "updates", "debug"],
        )
    ] == [
        ("values", {"query": "what is weather in sf", "docs": []}),
        (
            "debug",
            {
                "type": "task",
                "timestamp": AnyStr(),
                "step": 1,
                "payload": {
                    "id": "592f3430-c17c-5d1c-831f-fecebb2c05bf",
                    "name": "rewrite_query",
                    "input": {
                        "query": "what is weather in sf",
                        "answer": None,
                        "docs": [],
                    },
                    "triggers": ["start:rewrite_query"],
                },
            },
        ),
        ("updates", {"rewrite_query": {"query": "query: what is weather in sf"}}),
        (
            "debug",
            {
                "type": "task_result",
                "timestamp": AnyStr(),
                "step": 1,
                "payload": {
                    "id": "592f3430-c17c-5d1c-831f-fecebb2c05bf",
                    "name": "rewrite_query",
                    "result": [("query", "query: what is weather in sf")],
                },
            },
        ),
        ("values", {"query": "query: what is weather in sf", "docs": []}),
        (
            "debug",
            {
                "type": "task",
                "timestamp": AnyStr(),
                "step": 2,
                "payload": {
                    "id": "7db5e9d8-e132-5079-ab99-ced15e67d48b",
                    "name": "retriever_one",
                    "input": {
                        "query": "query: what is weather in sf",
                        "answer": None,
                        "docs": [],
                    },
                    "triggers": ["rewrite_query"],
                },
            },
        ),
        (
            "debug",
            {
                "type": "task",
                "timestamp": AnyStr(),
                "step": 2,
                "payload": {
                    "id": "96965ed0-2c10-52a1-86eb-081ba6de73b2",
                    "name": "retriever_two",
                    "input": {
                        "query": "query: what is weather in sf",
                        "answer": None,
                        "docs": [],
                    },
                    "triggers": ["rewrite_query"],
                },
            },
        ),
        (
            "updates",
            {"retriever_two": {"docs": ["doc3", "doc4"]}},
        ),
        (
            "debug",
            {
                "type": "task_result",
                "timestamp": AnyStr(),
                "step": 2,
                "payload": {
                    "id": "96965ed0-2c10-52a1-86eb-081ba6de73b2",
                    "name": "retriever_two",
                    "result": [("docs", ["doc3", "doc4"])],
                },
            },
        ),
        (
            "updates",
            {"retriever_one": {"docs": ["doc1", "doc2"]}},
        ),
        (
            "debug",
            {
                "type": "task_result",
                "timestamp": AnyStr(),
                "step": 2,
                "payload": {
                    "id": "7db5e9d8-e132-5079-ab99-ced15e67d48b",
                    "name": "retriever_one",
                    "result": [("docs", ["doc1", "doc2"])],
                },
            },
        ),
        (
            "values",
            {
                "query": "query: what is weather in sf",
                "docs": ["doc1", "doc2", "doc3", "doc4"],
            },
        ),
        (
            "debug",
            {
                "type": "task",
                "timestamp": AnyStr(),
                "step": 3,
                "payload": {
                    "id": "8959fb57-d0f5-5725-9ac4-ec1c554fb0a0",
                    "name": "qa",
                    "input": {
                        "query": "query: what is weather in sf",
                        "answer": None,
                        "docs": ["doc1", "doc2", "doc3", "doc4"],
                    },
                    "triggers": ["retriever_one", "retriever_two"],
                },
            },
        ),
        ("updates", {"qa": {"answer": "doc1,doc2,doc3,doc4"}}),
        (
            "debug",
            {
                "type": "task_result",
                "timestamp": AnyStr(),
                "step": 3,
                "payload": {
                    "id": "8959fb57-d0f5-5725-9ac4-ec1c554fb0a0",
                    "name": "qa",
                    "result": [("answer", "doc1,doc2,doc3,doc4")],
                },
            },
        ),
        (
            "values",
            {
                "query": "query: what is weather in sf",
                "answer": "doc1,doc2,doc3,doc4",
                "docs": ["doc1", "doc2", "doc3", "doc4"],
            },
        ),
    ]


def test_start_branch_then(snapshot: SnapshotAssertion) -> None:
    class State(TypedDict):
        my_key: Annotated[str, operator.add]
        market: str

    tool_two_graph = StateGraph(State)
    tool_two_graph.add_node("tool_two_slow", lambda s: {"my_key": " slow"})
    tool_two_graph.add_node("tool_two_fast", lambda s: {"my_key": " fast"})
    tool_two_graph.set_conditional_entry_point(
        lambda s: "tool_two_slow" if s["market"] == "DE" else "tool_two_fast", then=END
    )
    tool_two = tool_two_graph.compile()
    assert tool_two.get_graph().draw_mermaid() == snapshot

    assert tool_two.invoke({"my_key": "value", "market": "DE"}) == {
        "my_key": "value slow",
        "market": "DE",
    }
    assert tool_two.invoke({"my_key": "value", "market": "US"}) == {
        "my_key": "value fast",
        "market": "US",
    }

    with SqliteSaver.from_conn_string(":memory:") as saver:
        tool_two = tool_two_graph.compile(
            checkpointer=saver, interrupt_before=["tool_two_fast", "tool_two_slow"]
        )

        # missing thread_id
        with pytest.raises(ValueError, match="thread_id"):
            tool_two.invoke({"my_key": "value", "market": "DE"})

        thread1 = {"configurable": {"thread_id": "1"}}
        # stop when about to enter node
        assert tool_two.invoke({"my_key": "value ⛰️", "market": "DE"}, thread1) == {
            "my_key": "value ⛰️",
            "market": "DE",
        }
        assert tool_two.get_state(thread1) == StateSnapshot(
            values={"my_key": "value ⛰️", "market": "DE"},
            next=("tool_two_slow",),
            config=tool_two.checkpointer.get_tuple(thread1).config,
            created_at=tool_two.checkpointer.get_tuple(thread1).checkpoint["ts"],
            metadata={"source": "loop", "step": 0, "writes": None},
            parent_config=[*tool_two.checkpointer.list(thread1, limit=2)][-1].config,
        )
        # resume, for same result as above
        assert tool_two.invoke(None, thread1, debug=1) == {
            "my_key": "value ⛰️ slow",
            "market": "DE",
        }
        assert tool_two.get_state(thread1) == StateSnapshot(
            values={"my_key": "value ⛰️ slow", "market": "DE"},
            next=(),
            config=tool_two.checkpointer.get_tuple(thread1).config,
            created_at=tool_two.checkpointer.get_tuple(thread1).checkpoint["ts"],
            metadata={
                "source": "loop",
                "step": 1,
                "writes": {"tool_two_slow": {"my_key": " slow"}},
            },
            parent_config=[*tool_two.checkpointer.list(thread1, limit=2)][-1].config,
        )

        thread2 = {"configurable": {"thread_id": "2"}}
        # stop when about to enter node
        assert tool_two.invoke({"my_key": "value", "market": "US"}, thread2) == {
            "my_key": "value",
            "market": "US",
        }
        assert tool_two.get_state(thread2) == StateSnapshot(
            values={"my_key": "value", "market": "US"},
            next=("tool_two_fast",),
            config=tool_two.checkpointer.get_tuple(thread2).config,
            created_at=tool_two.checkpointer.get_tuple(thread2).checkpoint["ts"],
            metadata={"source": "loop", "step": 0, "writes": None},
            parent_config=[*tool_two.checkpointer.list(thread2, limit=2)][-1].config,
        )
        # resume, for same result as above
        assert tool_two.invoke(None, thread2, debug=1) == {
            "my_key": "value fast",
            "market": "US",
        }
        assert tool_two.get_state(thread2) == StateSnapshot(
            values={"my_key": "value fast", "market": "US"},
            next=(),
            config=tool_two.checkpointer.get_tuple(thread2).config,
            created_at=tool_two.checkpointer.get_tuple(thread2).checkpoint["ts"],
            metadata={
                "source": "loop",
                "step": 1,
                "writes": {"tool_two_fast": {"my_key": " fast"}},
            },
            parent_config=[*tool_two.checkpointer.list(thread2, limit=2)][-1].config,
        )

        thread3 = {"configurable": {"thread_id": "3"}}
        # stop when about to enter node
        assert tool_two.invoke({"my_key": "value", "market": "US"}, thread3) == {
            "my_key": "value",
            "market": "US",
        }
        assert tool_two.get_state(thread3) == StateSnapshot(
            values={"my_key": "value", "market": "US"},
            next=("tool_two_fast",),
            config=tool_two.checkpointer.get_tuple(thread3).config,
            created_at=tool_two.checkpointer.get_tuple(thread3).checkpoint["ts"],
            metadata={"source": "loop", "step": 0, "writes": None},
            parent_config=[*tool_two.checkpointer.list(thread3, limit=2)][-1].config,
        )
        # update state
        tool_two.update_state(thread3, {"my_key": "key"})  # appends to my_key
        assert tool_two.get_state(thread3) == StateSnapshot(
            values={"my_key": "valuekey", "market": "US"},
            next=("tool_two_fast",),
            config=tool_two.checkpointer.get_tuple(thread3).config,
            created_at=tool_two.checkpointer.get_tuple(thread3).checkpoint["ts"],
            metadata={
                "source": "update",
                "step": 1,
                "writes": {START: {"my_key": "key"}},
            },
            parent_config=[*tool_two.checkpointer.list(thread3, limit=2)][-1].config,
        )
        # resume, for same result as above
        assert tool_two.invoke(None, thread3, debug=1) == {
            "my_key": "valuekey fast",
            "market": "US",
        }
        assert tool_two.get_state(thread3) == StateSnapshot(
            values={"my_key": "valuekey fast", "market": "US"},
            next=(),
            config=tool_two.checkpointer.get_tuple(thread3).config,
            created_at=tool_two.checkpointer.get_tuple(thread3).checkpoint["ts"],
            metadata={
                "source": "loop",
                "step": 2,
                "writes": {"tool_two_fast": {"my_key": " fast"}},
            },
            parent_config=[*tool_two.checkpointer.list(thread3, limit=2)][-1].config,
        )


def test_branch_then(snapshot: SnapshotAssertion) -> None:
    class State(TypedDict):
        my_key: Annotated[str, operator.add]
        market: str

    tool_two_graph = StateGraph(State)
    tool_two_graph.set_entry_point("prepare")
    tool_two_graph.set_finish_point("finish")
    tool_two_graph.add_conditional_edges(
        source="prepare",
        path=lambda s: "tool_two_slow" if s["market"] == "DE" else "tool_two_fast",
        then="finish",
    )
    tool_two_graph.add_node("prepare", lambda s: {"my_key": " prepared"})
    tool_two_graph.add_node("tool_two_slow", lambda s: {"my_key": " slow"})
    tool_two_graph.add_node("tool_two_fast", lambda s: {"my_key": " fast"})
    tool_two_graph.add_node("finish", lambda s: {"my_key": " finished"})
    tool_two = tool_two_graph.compile()
    assert tool_two.get_graph().draw_mermaid(with_styles=False) == snapshot
    assert tool_two.get_graph().draw_mermaid() == snapshot

    assert tool_two.invoke({"my_key": "value", "market": "DE"}, debug=1) == {
        "my_key": "value prepared slow finished",
        "market": "DE",
    }
    assert tool_two.invoke({"my_key": "value", "market": "US"}) == {
        "my_key": "value prepared fast finished",
        "market": "US",
    }

    with SqliteSaver.from_conn_string(":memory:") as saver:
        # test stream_mode=debug
        tool_two = tool_two_graph.compile(checkpointer=saver)
        thread10 = {"configurable": {"thread_id": "10"}}
        assert [
            *tool_two.stream(
                {"my_key": "value", "market": "DE"}, thread10, stream_mode="debug"
            )
        ] == [
            {
                "type": "checkpoint",
                "timestamp": AnyStr(),
                "step": -1,
                "payload": {
                    "config": {
                        "tags": [],
                        "metadata": {"thread_id": "10"},
                        "callbacks": None,
                        "recursion_limit": 25,
                        "configurable": {
                            "thread_id": "10",
                            "thread_ts": AnyStr(),
                        },
                    },
                    "values": {"my_key": ""},
                    "metadata": {
                        "source": "input",
                        "step": -1,
                        "writes": {"my_key": "value", "market": "DE"},
                    },
                },
            },
            {
                "type": "checkpoint",
                "timestamp": AnyStr(),
                "step": 0,
                "payload": {
                    "config": {
                        "tags": [],
                        "metadata": {"thread_id": "10"},
                        "callbacks": None,
                        "recursion_limit": 25,
                        "configurable": {
                            "thread_id": "10",
                            "thread_ts": AnyStr(),
                        },
                    },
                    "values": {
                        "my_key": "value",
                        "market": "DE",
                    },
                    "metadata": {
                        "source": "loop",
                        "step": 0,
                        "writes": None,
                    },
                },
            },
            {
                "type": "task",
                "timestamp": AnyStr(),
                "step": 1,
                "payload": {
                    "id": "7b7b0713-e958-5d07-803c-c9910a7cc162",
                    "name": "prepare",
                    "input": {"my_key": "value", "market": "DE"},
                    "triggers": ["start:prepare"],
                },
            },
            {
                "type": "task_result",
                "timestamp": AnyStr(),
                "step": 1,
                "payload": {
                    "id": "7b7b0713-e958-5d07-803c-c9910a7cc162",
                    "name": "prepare",
                    "result": [("my_key", " prepared")],
                },
            },
            {
                "type": "checkpoint",
                "timestamp": AnyStr(),
                "step": 1,
                "payload": {
                    "config": {
                        "tags": [],
                        "metadata": {"thread_id": "10"},
                        "callbacks": None,
                        "recursion_limit": 25,
                        "configurable": {
                            "thread_id": "10",
                            "thread_ts": AnyStr(),
                        },
                    },
                    "values": {
                        "my_key": "value prepared",
                        "market": "DE",
                    },
                    "metadata": {
                        "source": "loop",
                        "step": 1,
                        "writes": {"prepare": {"my_key": " prepared"}},
                    },
                },
            },
            {
                "type": "task",
                "timestamp": AnyStr(),
                "step": 2,
                "payload": {
                    "id": "dd9f2fa5-ccfa-5d12-81ec-942563056a08",
                    "name": "tool_two_slow",
                    "input": {"my_key": "value prepared", "market": "DE"},
                    "triggers": ["branch:prepare:condition:tool_two_slow"],
                },
            },
            {
                "type": "task_result",
                "timestamp": AnyStr(),
                "step": 2,
                "payload": {
                    "id": "dd9f2fa5-ccfa-5d12-81ec-942563056a08",
                    "name": "tool_two_slow",
                    "result": [("my_key", " slow")],
                },
            },
            {
                "type": "checkpoint",
                "timestamp": AnyStr(),
                "step": 2,
                "payload": {
                    "config": {
                        "tags": [],
                        "metadata": {"thread_id": "10"},
                        "callbacks": None,
                        "recursion_limit": 25,
                        "configurable": {
                            "thread_id": "10",
                            "thread_ts": AnyStr(),
                        },
                    },
                    "values": {
                        "my_key": "value prepared slow",
                        "market": "DE",
                    },
                    "metadata": {
                        "source": "loop",
                        "step": 2,
                        "writes": {"tool_two_slow": {"my_key": " slow"}},
                    },
                },
            },
            {
                "type": "task",
                "timestamp": AnyStr(),
                "step": 3,
                "payload": {
                    "id": "ceada3c5-5f25-59e4-9ea5-544599ce1d2f",
                    "name": "finish",
                    "input": {"my_key": "value prepared slow", "market": "DE"},
                    "triggers": ["branch:prepare:condition:then"],
                },
            },
            {
                "type": "task_result",
                "timestamp": AnyStr(),
                "step": 3,
                "payload": {
                    "id": "ceada3c5-5f25-59e4-9ea5-544599ce1d2f",
                    "name": "finish",
                    "result": [("my_key", " finished")],
                },
            },
            {
                "type": "checkpoint",
                "timestamp": AnyStr(),
                "step": 3,
                "payload": {
                    "config": {
                        "tags": [],
                        "metadata": {"thread_id": "10"},
                        "callbacks": None,
                        "recursion_limit": 25,
                        "configurable": {
                            "thread_id": "10",
                            "thread_ts": AnyStr(),
                        },
                    },
                    "values": {
                        "my_key": "value prepared slow finished",
                        "market": "DE",
                    },
                    "metadata": {
                        "source": "loop",
                        "step": 3,
                        "writes": {"finish": {"my_key": " finished"}},
                    },
                },
            },
        ]

        tool_two = tool_two_graph.compile(
            checkpointer=saver, interrupt_before=["tool_two_fast", "tool_two_slow"]
        )

        # missing thread_id
        with pytest.raises(ValueError, match="thread_id"):
            tool_two.invoke({"my_key": "value", "market": "DE"})

        thread1 = {"configurable": {"thread_id": "1"}}
        # stop when about to enter node
        assert tool_two.invoke({"my_key": "value", "market": "DE"}, thread1) == {
            "my_key": "value prepared",
            "market": "DE",
        }
        assert tool_two.get_state(thread1) == StateSnapshot(
            values={"my_key": "value prepared", "market": "DE"},
            next=("tool_two_slow",),
            config=tool_two.checkpointer.get_tuple(thread1).config,
            created_at=tool_two.checkpointer.get_tuple(thread1).checkpoint["ts"],
            metadata={
                "source": "loop",
                "step": 1,
                "writes": {"prepare": {"my_key": " prepared"}},
            },
            parent_config=[*tool_two.checkpointer.list(thread1, limit=2)][-1].config,
        )
        # resume, for same result as above
        assert tool_two.invoke(None, thread1, debug=1) == {
            "my_key": "value prepared slow finished",
            "market": "DE",
        }
        assert tool_two.get_state(thread1) == StateSnapshot(
            values={"my_key": "value prepared slow finished", "market": "DE"},
            next=(),
            config=tool_two.checkpointer.get_tuple(thread1).config,
            created_at=tool_two.checkpointer.get_tuple(thread1).checkpoint["ts"],
            metadata={
                "source": "loop",
                "step": 3,
                "writes": {"finish": {"my_key": " finished"}},
            },
            parent_config=[*tool_two.checkpointer.list(thread1, limit=2)][-1].config,
        )

        thread2 = {"configurable": {"thread_id": "2"}}
        # stop when about to enter node
        assert tool_two.invoke({"my_key": "value", "market": "US"}, thread2) == {
            "my_key": "value prepared",
            "market": "US",
        }
        assert tool_two.get_state(thread2) == StateSnapshot(
            values={"my_key": "value prepared", "market": "US"},
            next=("tool_two_fast",),
            config=tool_two.checkpointer.get_tuple(thread2).config,
            created_at=tool_two.checkpointer.get_tuple(thread2).checkpoint["ts"],
            metadata={
                "source": "loop",
                "step": 1,
                "writes": {"prepare": {"my_key": " prepared"}},
            },
            parent_config=[*tool_two.checkpointer.list(thread2, limit=2)][-1].config,
        )
        # resume, for same result as above
        assert tool_two.invoke(None, thread2, debug=1) == {
            "my_key": "value prepared fast finished",
            "market": "US",
        }
        assert tool_two.get_state(thread2) == StateSnapshot(
            values={"my_key": "value prepared fast finished", "market": "US"},
            next=(),
            config=tool_two.checkpointer.get_tuple(thread2).config,
            created_at=tool_two.checkpointer.get_tuple(thread2).checkpoint["ts"],
            metadata={
                "source": "loop",
                "step": 3,
                "writes": {"finish": {"my_key": " finished"}},
            },
            parent_config=[*tool_two.checkpointer.list(thread2, limit=2)][-1].config,
        )

    with SqliteSaver.from_conn_string(":memory:") as saver:
        tool_two = tool_two_graph.compile(
            checkpointer=saver, interrupt_before=["finish"]
        )

        thread1 = {"configurable": {"thread_id": "1"}}

        # stop when about to enter node
        assert tool_two.invoke({"my_key": "value", "market": "DE"}, thread1) == {
            "my_key": "value prepared slow",
            "market": "DE",
        }
        assert tool_two.get_state(thread1) == StateSnapshot(
            values={
                "my_key": "value prepared slow",
                "market": "DE",
            },
            next=("finish",),
            config=tool_two.checkpointer.get_tuple(thread1).config,
            created_at=tool_two.checkpointer.get_tuple(thread1).checkpoint["ts"],
            metadata={
                "source": "loop",
                "step": 2,
                "writes": {"tool_two_slow": {"my_key": " slow"}},
            },
            parent_config=[*tool_two.checkpointer.list(thread1, limit=2)][-1].config,
        )

        # update state
        tool_two.update_state(thread1, {"my_key": "er"})
        assert tool_two.get_state(thread1) == StateSnapshot(
            values={
                "my_key": "value prepared slower",
                "market": "DE",
            },
            next=("finish",),
            config=tool_two.checkpointer.get_tuple(thread1).config,
            created_at=tool_two.checkpointer.get_tuple(thread1).checkpoint["ts"],
            metadata={
                "source": "update",
                "step": 3,
                "writes": {"tool_two_slow": {"my_key": "er"}},
            },
            parent_config=[*tool_two.checkpointer.list(thread1, limit=2)][-1].config,
        )

    with SqliteSaver.from_conn_string(":memory:") as saver:
        tool_two = tool_two_graph.compile(
            checkpointer=saver, interrupt_after=["prepare"]
        )

        # missing thread_id
        with pytest.raises(ValueError, match="thread_id"):
            tool_two.invoke({"my_key": "value", "market": "DE"})

        thread1 = {"configurable": {"thread_id": "1"}}
        # stop when about to enter node
        assert tool_two.invoke({"my_key": "value", "market": "DE"}, thread1) == {
            "my_key": "value prepared",
            "market": "DE",
        }
        assert tool_two.get_state(thread1) == StateSnapshot(
            values={"my_key": "value prepared", "market": "DE"},
            next=("tool_two_slow",),
            config=tool_two.checkpointer.get_tuple(thread1).config,
            created_at=tool_two.checkpointer.get_tuple(thread1).checkpoint["ts"],
            metadata={
                "source": "loop",
                "step": 1,
                "writes": {"prepare": {"my_key": " prepared"}},
            },
            parent_config=[*tool_two.checkpointer.list(thread1, limit=2)][-1].config,
        )
        # resume, for same result as above
        assert tool_two.invoke(None, thread1, debug=1) == {
            "my_key": "value prepared slow finished",
            "market": "DE",
        }
        assert tool_two.get_state(thread1) == StateSnapshot(
            values={"my_key": "value prepared slow finished", "market": "DE"},
            next=(),
            config=tool_two.checkpointer.get_tuple(thread1).config,
            created_at=tool_two.checkpointer.get_tuple(thread1).checkpoint["ts"],
            metadata={
                "source": "loop",
                "step": 3,
                "writes": {"finish": {"my_key": " finished"}},
            },
            parent_config=[*tool_two.checkpointer.list(thread1, limit=2)][-1].config,
        )

        thread2 = {"configurable": {"thread_id": "2"}}
        # stop when about to enter node
        assert tool_two.invoke({"my_key": "value", "market": "US"}, thread2) == {
            "my_key": "value prepared",
            "market": "US",
        }
        assert tool_two.get_state(thread2) == StateSnapshot(
            values={"my_key": "value prepared", "market": "US"},
            next=("tool_two_fast",),
            config=tool_two.checkpointer.get_tuple(thread2).config,
            created_at=tool_two.checkpointer.get_tuple(thread2).checkpoint["ts"],
            metadata={
                "source": "loop",
                "step": 1,
                "writes": {"prepare": {"my_key": " prepared"}},
            },
            parent_config=[*tool_two.checkpointer.list(thread2, limit=2)][-1].config,
        )
        # resume, for same result as above
        assert tool_two.invoke(None, thread2, debug=1) == {
            "my_key": "value prepared fast finished",
            "market": "US",
        }
        assert tool_two.get_state(thread2) == StateSnapshot(
            values={"my_key": "value prepared fast finished", "market": "US"},
            next=(),
            config=tool_two.checkpointer.get_tuple(thread2).config,
            created_at=tool_two.checkpointer.get_tuple(thread2).checkpoint["ts"],
            metadata={
                "source": "loop",
                "step": 3,
                "writes": {"finish": {"my_key": " finished"}},
            },
            parent_config=[*tool_two.checkpointer.list(thread2, limit=2)][-1].config,
        )

        thread3 = {"configurable": {"thread_id": "3"}}
        # update an empty thread before first run
        uconfig = tool_two.update_state(thread3, {"my_key": "key", "market": "DE"})
        # check current state
        assert tool_two.get_state(thread3) == StateSnapshot(
            values={"my_key": "key", "market": "DE"},
            next=("prepare",),
            config=uconfig,
            created_at=AnyStr(),
            metadata={
                "source": "update",
                "step": -1,
                "writes": {START: {"my_key": "key", "market": "DE"}},
            },
        )
        # run from this point
        assert tool_two.invoke(None, thread3) == {
            "my_key": "key prepared",
            "market": "DE",
        }
        # get state after first node
        assert tool_two.get_state(thread3) == StateSnapshot(
            values={"my_key": "key prepared", "market": "DE"},
            next=("tool_two_slow",),
            config=tool_two.checkpointer.get_tuple(thread3).config,
            created_at=tool_two.checkpointer.get_tuple(thread3).checkpoint["ts"],
            metadata={
                "source": "loop",
                "step": 0,
                "writes": {"prepare": {"my_key": " prepared"}},
            },
            parent_config=uconfig,
        )
        # resume, for same result as above
        assert tool_two.invoke(None, thread3, debug=1) == {
            "my_key": "key prepared slow finished",
            "market": "DE",
        }
        assert tool_two.get_state(thread3) == StateSnapshot(
            values={"my_key": "key prepared slow finished", "market": "DE"},
            next=(),
            config=tool_two.checkpointer.get_tuple(thread3).config,
            created_at=tool_two.checkpointer.get_tuple(thread3).checkpoint["ts"],
            metadata={
                "source": "loop",
                "step": 2,
                "writes": {"finish": {"my_key": " finished"}},
            },
            parent_config=[*tool_two.checkpointer.list(thread3, limit=2)][-1].config,
        )


def test_in_one_fan_out_state_graph_waiting_edge(snapshot: SnapshotAssertion) -> None:
    def sorted_add(
        x: list[str], y: Union[list[str], list[tuple[str, str]]]
    ) -> list[str]:
        if isinstance(y[0], tuple):
            for rem, _ in y:
                x.remove(rem)
            y = [t[1] for t in y]
        return sorted(operator.add(x, y))

    class State(TypedDict, total=False):
        query: str
        answer: str
        docs: Annotated[list[str], sorted_add]

    workflow = StateGraph(State)

    @workflow.add_node
    def rewrite_query(data: State) -> State:
        return {"query": f'query: {data["query"]}'}

    def analyzer_one(data: State) -> State:
        return {"query": f'analyzed: {data["query"]}'}

    def retriever_one(data: State) -> State:
        return {"docs": ["doc1", "doc2"]}

    def retriever_two(data: State) -> State:
        time.sleep(0.1)  # to ensure stream order
        return {"docs": ["doc3", "doc4"]}

    def qa(data: State) -> State:
        return {"answer": ",".join(data["docs"])}

    workflow.add_node(analyzer_one)
    workflow.add_node(retriever_one)
    workflow.add_node(retriever_two)
    workflow.add_node(qa)

    workflow.set_entry_point("rewrite_query")
    workflow.add_edge("rewrite_query", "analyzer_one")
    workflow.add_edge("analyzer_one", "retriever_one")
    workflow.add_edge("rewrite_query", "retriever_two")
    workflow.add_edge(["retriever_one", "retriever_two"], "qa")
    workflow.set_finish_point("qa")

    app = workflow.compile()

    assert app.get_graph().draw_mermaid(with_styles=False) == snapshot

    assert app.invoke({"query": "what is weather in sf"}) == {
        "query": "analyzed: query: what is weather in sf",
        "docs": ["doc1", "doc2", "doc3", "doc4"],
        "answer": "doc1,doc2,doc3,doc4",
    }

    assert [*app.stream({"query": "what is weather in sf"})] == [
        {"rewrite_query": {"query": "query: what is weather in sf"}},
        {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
        {"retriever_two": {"docs": ["doc3", "doc4"]}},
        {"retriever_one": {"docs": ["doc1", "doc2"]}},
        {"qa": {"answer": "doc1,doc2,doc3,doc4"}},
    ]

    app_w_interrupt = workflow.compile(
        checkpointer=MemorySaverAssertImmutable(),
        interrupt_after=["retriever_one"],
    )
    config = {"configurable": {"thread_id": "1"}}

    assert [
        c for c in app_w_interrupt.stream({"query": "what is weather in sf"}, config)
    ] == [
        {"rewrite_query": {"query": "query: what is weather in sf"}},
        {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
        {"retriever_two": {"docs": ["doc3", "doc4"]}},
        {"retriever_one": {"docs": ["doc1", "doc2"]}},
    ]

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {"qa": {"answer": "doc1,doc2,doc3,doc4"}},
    ]

    app_w_interrupt = workflow.compile(
        checkpointer=MemorySaverAssertImmutable(),
        interrupt_before=["qa"],
    )
    config = {"configurable": {"thread_id": "1"}}

    assert [
        c for c in app_w_interrupt.stream({"query": "what is weather in sf"}, config)
    ] == [
        {"rewrite_query": {"query": "query: what is weather in sf"}},
        {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
        {"retriever_two": {"docs": ["doc3", "doc4"]}},
        {"retriever_one": {"docs": ["doc1", "doc2"]}},
    ]

    app_w_interrupt.update_state(config, {"docs": ["doc5"]})
    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values={
            "query": "analyzed: query: what is weather in sf",
            "docs": ["doc1", "doc2", "doc3", "doc4", "doc5"],
        },
        next=("qa",),
        config=app_w_interrupt.checkpointer.get_tuple(config).config,
        created_at=app_w_interrupt.checkpointer.get_tuple(config).checkpoint["ts"],
        metadata={
            "source": "update",
            "step": 4,
            "writes": {"retriever_one": {"docs": ["doc5"]}},
        },
    )

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {"qa": {"answer": "doc1,doc2,doc3,doc4,doc5"}},
    ]


def test_in_one_fan_out_state_graph_waiting_edge_via_branch(
    snapshot: SnapshotAssertion,
) -> None:
    def sorted_add(
        x: list[str], y: Union[list[str], list[tuple[str, str]]]
    ) -> list[str]:
        if isinstance(y[0], tuple):
            for rem, _ in y:
                x.remove(rem)
            y = [t[1] for t in y]
        return sorted(operator.add(x, y))

    class State(TypedDict, total=False):
        query: str
        answer: str
        docs: Annotated[list[str], sorted_add]

    def rewrite_query(data: State) -> State:
        return {"query": f'query: {data["query"]}'}

    def analyzer_one(data: State) -> State:
        return {"query": f'analyzed: {data["query"]}'}

    def retriever_one(data: State) -> State:
        return {"docs": ["doc1", "doc2"]}

    def retriever_two(data: State) -> State:
        time.sleep(0.1)
        return {"docs": ["doc3", "doc4"]}

    def qa(data: State) -> State:
        return {"answer": ",".join(data["docs"])}

    def rewrite_query_then(data: State) -> Literal["retriever_two"]:
        return "retriever_two"

    workflow = StateGraph(State)

    workflow.add_node("rewrite_query", rewrite_query)
    workflow.add_node("analyzer_one", analyzer_one)
    workflow.add_node("retriever_one", retriever_one)
    workflow.add_node("retriever_two", retriever_two)
    workflow.add_node("qa", qa)

    workflow.set_entry_point("rewrite_query")
    workflow.add_edge("rewrite_query", "analyzer_one")
    workflow.add_edge("analyzer_one", "retriever_one")
    workflow.add_conditional_edges("rewrite_query", rewrite_query_then)
    workflow.add_edge(["retriever_one", "retriever_two"], "qa")
    workflow.set_finish_point("qa")

    app = workflow.compile()

    assert app.get_graph().draw_mermaid(with_styles=False) == snapshot

    assert app.invoke({"query": "what is weather in sf"}, debug=True) == {
        "query": "analyzed: query: what is weather in sf",
        "docs": ["doc1", "doc2", "doc3", "doc4"],
        "answer": "doc1,doc2,doc3,doc4",
    }

    assert [*app.stream({"query": "what is weather in sf"})] == [
        {"rewrite_query": {"query": "query: what is weather in sf"}},
        {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
        {"retriever_two": {"docs": ["doc3", "doc4"]}},
        {"retriever_one": {"docs": ["doc1", "doc2"]}},
        {"qa": {"answer": "doc1,doc2,doc3,doc4"}},
    ]

    app_w_interrupt = workflow.compile(
        checkpointer=MemorySaverAssertImmutable(),
        interrupt_after=["retriever_one"],
    )
    config = {"configurable": {"thread_id": "1"}}

    assert [
        c for c in app_w_interrupt.stream({"query": "what is weather in sf"}, config)
    ] == [
        {"rewrite_query": {"query": "query: what is weather in sf"}},
        {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
        {"retriever_two": {"docs": ["doc3", "doc4"]}},
        {"retriever_one": {"docs": ["doc1", "doc2"]}},
    ]

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {"qa": {"answer": "doc1,doc2,doc3,doc4"}},
    ]


def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class(
    snapshot: SnapshotAssertion,
) -> None:
    from langchain_core.pydantic_v1 import BaseModel, ValidationError

    def sorted_add(
        x: list[str], y: Union[list[str], list[tuple[str, str]]]
    ) -> list[str]:
        if isinstance(y[0], tuple):
            for rem, _ in y:
                x.remove(rem)
            y = [t[1] for t in y]
        return sorted(operator.add(x, y))

    class State(BaseModel):
        query: str
        answer: Optional[str] = None
        docs: Annotated[list[str], sorted_add]

    class StateUpdate(BaseModel):
        query: Optional[str] = None
        answer: Optional[str] = None
        docs: Optional[list[str]] = None

    def rewrite_query(data: State) -> State:
        return {"query": f"query: {data.query}"}

    def analyzer_one(data: State) -> State:
        return StateUpdate(query=f"analyzed: {data.query}")

    def retriever_one(data: State) -> State:
        return {"docs": ["doc1", "doc2"]}

    def retriever_two(data: State) -> State:
        time.sleep(0.1)
        return {"docs": ["doc3", "doc4"]}

    def qa(data: State) -> State:
        return {"answer": ",".join(data.docs)}

    def decider(data: State) -> str:
        assert isinstance(data, State)
        return "retriever_two"

    workflow = StateGraph(State)

    workflow.add_node("rewrite_query", rewrite_query)
    workflow.add_node("analyzer_one", analyzer_one)
    workflow.add_node("retriever_one", retriever_one)
    workflow.add_node("retriever_two", retriever_two)
    workflow.add_node("qa", qa)

    workflow.set_entry_point("rewrite_query")
    workflow.add_edge("rewrite_query", "analyzer_one")
    workflow.add_edge("analyzer_one", "retriever_one")
    workflow.add_conditional_edges(
        "rewrite_query", decider, {"retriever_two": "retriever_two"}
    )
    workflow.add_edge(["retriever_one", "retriever_two"], "qa")
    workflow.set_finish_point("qa")

    app = workflow.compile()

    assert app.get_graph().draw_mermaid(with_styles=False) == snapshot

    with pytest.raises(ValidationError):
        app.invoke({"query": {}})

    assert app.invoke({"query": "what is weather in sf"}) == {
        "query": "analyzed: query: what is weather in sf",
        "docs": ["doc1", "doc2", "doc3", "doc4"],
        "answer": "doc1,doc2,doc3,doc4",
    }

    assert [*app.stream({"query": "what is weather in sf"})] == [
        {"rewrite_query": {"query": "query: what is weather in sf"}},
        {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
        {"retriever_two": {"docs": ["doc3", "doc4"]}},
        {"retriever_one": {"docs": ["doc1", "doc2"]}},
        {"qa": {"answer": "doc1,doc2,doc3,doc4"}},
    ]

    app_w_interrupt = workflow.compile(
        checkpointer=MemorySaverAssertImmutable(),
        interrupt_after=["retriever_one"],
    )
    config = {"configurable": {"thread_id": "1"}}

    assert [
        c for c in app_w_interrupt.stream({"query": "what is weather in sf"}, config)
    ] == [
        {"rewrite_query": {"query": "query: what is weather in sf"}},
        {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
        {"retriever_two": {"docs": ["doc3", "doc4"]}},
        {"retriever_one": {"docs": ["doc1", "doc2"]}},
    ]

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {"qa": {"answer": "doc1,doc2,doc3,doc4"}},
    ]


def test_in_one_fan_out_state_graph_waiting_edge_plus_regular() -> None:
    def sorted_add(
        x: list[str], y: Union[list[str], list[tuple[str, str]]]
    ) -> list[str]:
        if isinstance(y[0], tuple):
            for rem, _ in y:
                x.remove(rem)
            y = [t[1] for t in y]
        return sorted(operator.add(x, y))

    class State(TypedDict, total=False):
        query: str
        answer: str
        docs: Annotated[list[str], sorted_add]

    def rewrite_query(data: State) -> State:
        return {"query": f'query: {data["query"]}'}

    def analyzer_one(data: State) -> State:
        time.sleep(0.1)
        return {"query": f'analyzed: {data["query"]}'}

    def retriever_one(data: State) -> State:
        return {"docs": ["doc1", "doc2"]}

    def retriever_two(data: State) -> State:
        time.sleep(0.2)
        return {"docs": ["doc3", "doc4"]}

    def qa(data: State) -> State:
        return {"answer": ",".join(data["docs"])}

    workflow = StateGraph(State)

    workflow.add_node("rewrite_query", rewrite_query)
    workflow.add_node("analyzer_one", analyzer_one)
    workflow.add_node("retriever_one", retriever_one)
    workflow.add_node("retriever_two", retriever_two)
    workflow.add_node("qa", qa)

    workflow.set_entry_point("rewrite_query")
    workflow.add_edge("rewrite_query", "analyzer_one")
    workflow.add_edge("analyzer_one", "retriever_one")
    workflow.add_edge("rewrite_query", "retriever_two")
    workflow.add_edge(["retriever_one", "retriever_two"], "qa")
    workflow.set_finish_point("qa")

    # silly edge, to make sure having been triggered before doesn't break
    # semantics of named barrier (== waiting edges)
    workflow.add_edge("rewrite_query", "qa")

    app = workflow.compile()

    assert app.invoke({"query": "what is weather in sf"}) == {
        "query": "analyzed: query: what is weather in sf",
        "docs": ["doc1", "doc2", "doc3", "doc4"],
        "answer": "doc1,doc2,doc3,doc4",
    }

    assert [*app.stream({"query": "what is weather in sf"})] == [
        {"rewrite_query": {"query": "query: what is weather in sf"}},
        {"qa": {"answer": ""}},
        {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
        {"retriever_two": {"docs": ["doc3", "doc4"]}},
        {"retriever_one": {"docs": ["doc1", "doc2"]}},
        {"qa": {"answer": "doc1,doc2,doc3,doc4"}},
    ]

    app_w_interrupt = workflow.compile(
        checkpointer=MemorySaverAssertImmutable(),
        interrupt_after=["retriever_one"],
    )
    config = {"configurable": {"thread_id": "1"}}

    assert [
        c for c in app_w_interrupt.stream({"query": "what is weather in sf"}, config)
    ] == [
        {"rewrite_query": {"query": "query: what is weather in sf"}},
        {"qa": {"answer": ""}},
        {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
        {"retriever_two": {"docs": ["doc3", "doc4"]}},
        {"retriever_one": {"docs": ["doc1", "doc2"]}},
    ]

    assert [c for c in app_w_interrupt.stream(None, config)] == [
        {"qa": {"answer": "doc1,doc2,doc3,doc4"}},
    ]


def test_in_one_fan_out_state_graph_waiting_edge_multiple() -> None:
    def sorted_add(
        x: list[str], y: Union[list[str], list[tuple[str, str]]]
    ) -> list[str]:
        if isinstance(y[0], tuple):
            for rem, _ in y:
                x.remove(rem)
            y = [t[1] for t in y]
        return sorted(operator.add(x, y))

    class State(TypedDict, total=False):
        query: str
        answer: str
        docs: Annotated[list[str], sorted_add]

    def rewrite_query(data: State) -> State:
        return {"query": f'query: {data["query"]}'}

    def analyzer_one(data: State) -> State:
        return {"query": f'analyzed: {data["query"]}'}

    def retriever_one(data: State) -> State:
        return {"docs": ["doc1", "doc2"]}

    def retriever_two(data: State) -> State:
        time.sleep(0.1)
        return {"docs": ["doc3", "doc4"]}

    def qa(data: State) -> State:
        return {"answer": ",".join(data["docs"])}

    def decider(data: State) -> None:
        return None

    def decider_cond(data: State) -> str:
        if data["query"].count("analyzed") > 1:
            return "qa"
        else:
            return "rewrite_query"

    workflow = StateGraph(State)

    workflow.add_node("rewrite_query", rewrite_query)
    workflow.add_node("analyzer_one", analyzer_one)
    workflow.add_node("retriever_one", retriever_one)
    workflow.add_node("retriever_two", retriever_two)
    workflow.add_node("decider", decider)
    workflow.add_node("qa", qa)

    workflow.set_entry_point("rewrite_query")
    workflow.add_edge("rewrite_query", "analyzer_one")
    workflow.add_edge("analyzer_one", "retriever_one")
    workflow.add_edge("rewrite_query", "retriever_two")
    workflow.add_edge(["retriever_one", "retriever_two"], "decider")
    workflow.add_conditional_edges("decider", decider_cond)
    workflow.set_finish_point("qa")

    app = workflow.compile()

    assert app.invoke({"query": "what is weather in sf"}) == {
        "query": "analyzed: query: analyzed: query: what is weather in sf",
        "answer": "doc1,doc1,doc2,doc2,doc3,doc3,doc4,doc4",
        "docs": ["doc1", "doc1", "doc2", "doc2", "doc3", "doc3", "doc4", "doc4"],
    }

    assert [*app.stream({"query": "what is weather in sf"})] == [
        {"rewrite_query": {"query": "query: what is weather in sf"}},
        {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
        {"retriever_two": {"docs": ["doc3", "doc4"]}},
        {"retriever_one": {"docs": ["doc1", "doc2"]}},
        {"rewrite_query": {"query": "query: analyzed: query: what is weather in sf"}},
        {
            "analyzer_one": {
                "query": "analyzed: query: analyzed: query: what is weather in sf"
            }
        },
        {"retriever_two": {"docs": ["doc3", "doc4"]}},
        {"retriever_one": {"docs": ["doc1", "doc2"]}},
        {"qa": {"answer": "doc1,doc1,doc2,doc2,doc3,doc3,doc4,doc4"}},
    ]


def test_in_one_fan_out_state_graph_waiting_edge_multiple_cond_edge() -> None:
    def sorted_add(
        x: list[str], y: Union[list[str], list[tuple[str, str]]]
    ) -> list[str]:
        if isinstance(y[0], tuple):
            for rem, _ in y:
                x.remove(rem)
            y = [t[1] for t in y]
        return sorted(operator.add(x, y))

    class State(TypedDict, total=False):
        query: str
        answer: str
        docs: Annotated[list[str], sorted_add]

    def rewrite_query(data: State) -> State:
        return {"query": f'query: {data["query"]}'}

    def retriever_picker(data: State) -> list[str]:
        return ["analyzer_one", "retriever_two"]

    def analyzer_one(data: State) -> State:
        return {"query": f'analyzed: {data["query"]}'}

    def retriever_one(data: State) -> State:
        return {"docs": ["doc1", "doc2"]}

    def retriever_two(data: State) -> State:
        time.sleep(0.1)
        return {"docs": ["doc3", "doc4"]}

    def qa(data: State) -> State:
        return {"answer": ",".join(data["docs"])}

    def decider(data: State) -> None:
        return None

    def decider_cond(data: State) -> str:
        if data["query"].count("analyzed") > 1:
            return "qa"
        else:
            return "rewrite_query"

    workflow = StateGraph(State)

    workflow.add_node("rewrite_query", rewrite_query)
    workflow.add_node("analyzer_one", analyzer_one)
    workflow.add_node("retriever_one", retriever_one)
    workflow.add_node("retriever_two", retriever_two)
    workflow.add_node("decider", decider)
    workflow.add_node("qa", qa)

    workflow.set_entry_point("rewrite_query")
    workflow.add_conditional_edges("rewrite_query", retriever_picker)
    workflow.add_edge("analyzer_one", "retriever_one")
    workflow.add_edge(["retriever_one", "retriever_two"], "decider")
    workflow.add_conditional_edges("decider", decider_cond)
    workflow.set_finish_point("qa")

    app = workflow.compile()

    assert app.invoke({"query": "what is weather in sf"}) == {
        "query": "analyzed: query: analyzed: query: what is weather in sf",
        "answer": "doc1,doc1,doc2,doc2,doc3,doc3,doc4,doc4",
        "docs": ["doc1", "doc1", "doc2", "doc2", "doc3", "doc3", "doc4", "doc4"],
    }

    assert [*app.stream({"query": "what is weather in sf"})] == [
        {"rewrite_query": {"query": "query: what is weather in sf"}},
        {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
        {"retriever_two": {"docs": ["doc3", "doc4"]}},
        {"retriever_one": {"docs": ["doc1", "doc2"]}},
        {"rewrite_query": {"query": "query: analyzed: query: what is weather in sf"}},
        {
            "analyzer_one": {
                "query": "analyzed: query: analyzed: query: what is weather in sf"
            }
        },
        {"retriever_two": {"docs": ["doc3", "doc4"]}},
        {"retriever_one": {"docs": ["doc1", "doc2"]}},
        {"qa": {"answer": "doc1,doc1,doc2,doc2,doc3,doc3,doc4,doc4"}},
    ]


def test_simple_multi_edge(snapshot: SnapshotAssertion) -> None:
    class State(TypedDict):
        my_key: Annotated[str, operator.add]

    def up(state: State):
        pass

    def side(state: State):
        pass

    def down(state: State):
        pass

    graph = StateGraph(State)

    graph.add_node("up", up)
    graph.add_node("side", side)
    graph.add_node("down", down)

    graph.set_entry_point("up")
    graph.add_edge("up", "side")
    graph.add_edge(["up", "side"], "down")
    graph.set_finish_point("down")

    app = graph.compile()

    assert app.get_graph().draw_mermaid(with_styles=False) == snapshot
    assert app.invoke({"my_key": "my_value"}) == {"my_key": "my_value"}


def test_nested_graph_xray(snapshot: SnapshotAssertion) -> None:
    class State(TypedDict):
        my_key: Annotated[str, operator.add]
        market: str

    def logic(state: State):
        pass

    tool_two_graph = StateGraph(State)
    tool_two_graph.add_node("tool_two_slow", logic)
    tool_two_graph.add_node("tool_two_fast", logic)
    tool_two_graph.set_conditional_entry_point(
        lambda s: "tool_two_slow" if s["market"] == "DE" else "tool_two_fast",
        then=END,
    )
    tool_two = tool_two_graph.compile()

    graph = StateGraph(State)
    graph.add_node("tool_one", logic)
    graph.add_node("tool_two", tool_two)
    graph.add_node("tool_three", logic)
    graph.set_conditional_entry_point(lambda s: "tool_one", then=END)
    app = graph.compile()

    assert app.get_graph(xray=True).to_json() == snapshot
    assert app.get_graph(xray=True).draw_mermaid() == snapshot


def test_nested_graph(snapshot: SnapshotAssertion) -> None:
    def never_called_fn(state: Any):
        assert 0, "This function should never be called"

    never_called = RunnableLambda(never_called_fn)

    class InnerState(TypedDict):
        my_key: str
        my_other_key: str

    def up(state: InnerState):
        return {"my_key": state["my_key"] + " there", "my_other_key": state["my_key"]}

    inner = StateGraph(InnerState)
    inner.add_node("up", up)
    inner.set_entry_point("up")
    inner.set_finish_point("up")

    class State(TypedDict):
        my_key: str
        never_called: Any

    def side(state: State):
        return {"my_key": state["my_key"] + " and back again"}

    graph = StateGraph(State)
    graph.add_node("inner", inner.compile())
    graph.add_node("side", side)
    graph.set_entry_point("inner")
    graph.add_edge("inner", "side")
    graph.set_finish_point("side")

    app = graph.compile()

    assert app.get_graph().draw_mermaid(with_styles=False) == snapshot
    assert app.get_graph(xray=True).draw_mermaid() == snapshot
    assert app.invoke(
        {"my_key": "my value", "never_called": never_called}, debug=True
    ) == {
        "my_key": "my value there and back again",
        "never_called": never_called,
    }
    assert [*app.stream({"my_key": "my value", "never_called": never_called})] == [
        {"inner": {"my_key": "my value there"}},
        {"side": {"my_key": "my value there and back again"}},
    ]
    assert [
        *app.stream(
            {"my_key": "my value", "never_called": never_called}, stream_mode="values"
        )
    ] == [
        {
            "my_key": "my value",
            "never_called": never_called,
        },
        {
            "my_key": "my value there",
            "never_called": never_called,
        },
        {
            "my_key": "my value there and back again",
            "never_called": never_called,
        },
    ]

    chain = app | RunnablePassthrough()

    assert chain.invoke({"my_key": "my value", "never_called": never_called}) == {
        "my_key": "my value there and back again",
        "never_called": never_called,
    }
    assert [*chain.stream({"my_key": "my value", "never_called": never_called})] == [
        {"inner": {"my_key": "my value there"}},
        {"side": {"my_key": "my value there and back again"}},
    ]


def test_repeat_condition(snapshot: SnapshotAssertion) -> None:
    class AgentState(TypedDict):
        hello: str

    def router(state: AgentState) -> str:
        return "hmm"

    workflow = StateGraph(AgentState)
    workflow.add_node("Researcher", lambda x: x)
    workflow.add_node("Chart Generator", lambda x: x)
    workflow.add_node("Call Tool", lambda x: x)
    workflow.add_conditional_edges(
        "Researcher",
        router,
        {
            "redo": "Researcher",
            "continue": "Chart Generator",
            "call_tool": "Call Tool",
            "end": END,
        },
    )
    workflow.add_conditional_edges(
        "Chart Generator",
        router,
        {"continue": "Researcher", "call_tool": "Call Tool", "end": END},
    )
    workflow.add_conditional_edges(
        "Call Tool",
        # Each agent node updates the 'sender' field
        # the tool calling node does not, meaning
        # this edge will route back to the original agent
        # who invoked the tool
        lambda x: x["sender"],
        {
            "Researcher": "Researcher",
            "Chart Generator": "Chart Generator",
        },
    )
    workflow.set_entry_point("Researcher")

    app = workflow.compile()
    assert app.get_graph().draw_mermaid(with_styles=False) == snapshot


def test_checkpoint_metadata() -> None:
    """This test verifies that a run's configurable fields are merged with the
    previous checkpoint config for each step in the run.
    """
    # set up test
    from langchain_core.language_models.fake_chat_models import (
        FakeMessagesListChatModel,
    )
    from langchain_core.messages import AIMessage, AnyMessage
    from langchain_core.prompts import ChatPromptTemplate
    from langchain_core.tools import tool

    # graph state
    class BaseState(TypedDict):
        messages: Annotated[list[AnyMessage], add_messages]

    # initialize graph nodes
    @tool()
    def search_api(query: str) -> str:
        """Searches the API for the query."""
        return f"result for {query}"

    tools = [search_api]

    prompt = ChatPromptTemplate.from_messages(
        [
            ("system", "You are a nice assistant."),
            ("placeholder", "{messages}"),
        ]
    )

    model = FakeMessagesListChatModel(
        responses=[
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "id": "tool_call123",
                        "name": "search_api",
                        "args": {"query": "query"},
                    },
                ],
            ),
            AIMessage(content="answer"),
        ]
    )

    @traceable(run_type="llm")
    def agent(state: BaseState) -> BaseState:
        formatted = prompt.invoke(state)
        response = model.invoke(formatted)
        return {"messages": response, "usage_metadata": {"total_tokens": 123}}

    def should_continue(data: BaseState) -> str:
        # Logic to decide whether to continue in the loop or exit
        if not data["messages"][-1].tool_calls:
            return "exit"
        else:
            return "continue"

    # define graphs w/ and w/o interrupt
    workflow = StateGraph(BaseState)
    workflow.add_node("agent", agent)
    workflow.add_node("tools", ToolNode(tools))
    workflow.set_entry_point("agent")
    workflow.add_conditional_edges(
        "agent", should_continue, {"continue": "tools", "exit": END}
    )
    workflow.add_edge("tools", "agent")

    # graph w/o interrupt
    checkpointer_1 = MemorySaverAssertCheckpointMetadata()
    app = workflow.compile(checkpointer=checkpointer_1)

    # graph w/ interrupt
    checkpointer_2 = MemorySaverAssertCheckpointMetadata()
    app_w_interrupt = workflow.compile(
        checkpointer=checkpointer_2, interrupt_before=["tools"]
    )

    # assertions

    # invoke graph w/o interrupt
    app.invoke(
        {"messages": ["what is weather in sf"]},
        {
            "configurable": {
                "thread_id": "1",
                "test_config_1": "foo",
                "test_config_2": "bar",
            },
        },
    )

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

    # assert that checkpoint metadata contains the run's configurable fields
    chkpnt_metadata_1 = checkpointer_1.get_tuple(config).metadata
    assert chkpnt_metadata_1["thread_id"] == "1"
    assert chkpnt_metadata_1["test_config_1"] == "foo"
    assert chkpnt_metadata_1["test_config_2"] == "bar"

    # Verify that all checkpoint metadata have the expected keys. This check
    # is needed because a run may have an arbitrary number of steps depending
    # on how the graph is constructed.
    chkpnt_tuples_1 = checkpointer_1.list(config)
    for chkpnt_tuple in chkpnt_tuples_1:
        assert chkpnt_tuple.metadata["thread_id"] == "1"
        assert chkpnt_tuple.metadata["test_config_1"] == "foo"
        assert chkpnt_tuple.metadata["test_config_2"] == "bar"

    # invoke graph, but interrupt before tool call
    app_w_interrupt.invoke(
        {"messages": ["what is weather in sf"]},
        {
            "configurable": {
                "thread_id": "2",
                "test_config_3": "foo",
                "test_config_4": "bar",
            },
        },
    )

    config = {"configurable": {"thread_id": "2"}}

    # assert that checkpoint metadata contains the run's configurable fields
    chkpnt_metadata_2 = checkpointer_2.get_tuple(config).metadata
    assert chkpnt_metadata_2["thread_id"] == "2"
    assert chkpnt_metadata_2["test_config_3"] == "foo"
    assert chkpnt_metadata_2["test_config_4"] == "bar"

    # resume graph execution
    app_w_interrupt.invoke(
        input=None,
        config={
            "configurable": {
                "thread_id": "2",
                "test_config_3": "foo",
                "test_config_4": "bar",
            }
        },
    )

    # assert that checkpoint metadata contains the run's configurable fields
    chkpnt_metadata_3 = checkpointer_2.get_tuple(config).metadata
    assert chkpnt_metadata_3["thread_id"] == "2"
    assert chkpnt_metadata_3["test_config_3"] == "foo"
    assert chkpnt_metadata_3["test_config_4"] == "bar"

    # Verify that all checkpoint metadata have the expected keys. This check
    # is needed because a run may have an arbitrary number of steps depending
    # on how the graph is constructed.
    chkpnt_tuples_2 = checkpointer_2.list(config)
    for chkpnt_tuple in chkpnt_tuples_2:
        assert chkpnt_tuple.metadata["thread_id"] == "2"
        assert chkpnt_tuple.metadata["test_config_3"] == "foo"
        assert chkpnt_tuple.metadata["test_config_4"] == "bar"