如何查看和更新子图中的状态¶
一旦您添加了persistence，就可以轻松地在任何时间点查看和更新子图的状态。这使得许多人机交互模式成为可能：
- 您可以在中断时向用户显示状态，让他们接受某个操作。
- 您可以回溯子图以重现或避免问题。
- 您可以修改状态，让用户更好地控制其操作。
本指南将展示如何做到这一点。
设置环境¶
首先，让我们安装所需的包
接下来，我们需要为OpenAI（我们将使用的大型语言模型）设置API密钥：
import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")
为LangGraph开发设置LangSmith
注册LangSmith，可以快速发现并解决您的LangGraph项目中的问题，提高项目性能。LangSmith允许您使用跟踪数据来调试、测试和监控使用LangGraph构建的LLM应用程序——了解如何开始使用，请参阅这里。
定义子图¶
首先，让我们设置我们的子图。为此，我们将创建一个简单的图，该图可以获取特定城市的天气。我们将在 weather_node 之前编译此图，并设置一个断点。
from langgraph.graph import StateGraph, END, START, MessagesState
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
@tool
def get_weather(city: str):
"""Get the weather for a specific city"""
return f"It's sunny in {city}!"
raw_model = ChatOpenAI(model="gpt-4o")
model = raw_model.with_structured_output(get_weather)
class SubGraphState(MessagesState):
city: str
def model_node(state: SubGraphState):
result = model.invoke(state["messages"])
return {"city": result["city"]}
def weather_node(state: SubGraphState):
result = get_weather.invoke({"city": state["city"]})
return {"messages": [{"role": "assistant", "content": result}]}
subgraph = StateGraph(SubGraphState)
subgraph.add_node(model_node)
subgraph.add_node(weather_node)
subgraph.add_edge(START, "model_node")
subgraph.add_edge("model_node", "weather_node")
subgraph.add_edge("weather_node", END)
subgraph = subgraph.compile(interrupt_before=["weather_node"])
API Reference: StateGraph | END | START | tool | ChatOpenAI
定义父图¶
现在我们可以设置整个图了。这个图首先会路由到子图以获取天气信息，否则它将路由到一个普通的LLM。
from typing import Literal
from typing_extensions import TypedDict
from langgraph.checkpoint.memory import MemorySaver
memory = MemorySaver()
class RouterState(MessagesState):
route: Literal["weather", "other"]
class Router(TypedDict):
route: Literal["weather", "other"]
router_model = raw_model.with_structured_output(Router)
def router_node(state: RouterState):
system_message = "Classify the incoming query as either about weather or not."
messages = [{"role": "system", "content": system_message}] + state["messages"]
route = router_model.invoke(messages)
return {"route": route["route"]}
def normal_llm_node(state: RouterState):
response = raw_model.invoke(state["messages"])
return {"messages": [response]}
def route_after_prediction(
state: RouterState,
) -> Literal["weather_graph", "normal_llm_node"]:
if state["route"] == "weather":
return "weather_graph"
else:
return "normal_llm_node"
graph = StateGraph(RouterState)
graph.add_node(router_node)
graph.add_node(normal_llm_node)
graph.add_node("weather_graph", subgraph)
graph.add_edge(START, "router_node")
graph.add_conditional_edges("router_node", route_after_prediction)
graph.add_edge("normal_llm_node", END)
graph.add_edge("weather_graph", END)
graph = graph.compile(checkpointer=memory)
API Reference: MemorySaver
from IPython.display import Image, display
# Setting xray to 1 will show the internal structure of the nested graph
display(Image(graph.get_graph(xray=1).draw_mermaid_png()))
让我们通过一个普通的查询来测试一下，确保它按预期工作！
config = {"configurable": {"thread_id": "1"}}
inputs = {"messages": [{"role": "user", "content": "hi!"}]}
for update in graph.stream(inputs, config=config, stream_mode="updates"):
print(update)
{'router_node': {'route': 'other'}}
{'normal_llm_node': {'messages': [AIMessage(content='Hello! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 9, 'total_tokens': 18, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-35de4577-2117-40e4-ab3b-cd2ac6e27b4c-0', usage_metadata={'input_tokens': 9, 'output_tokens': 9, 'total_tokens': 18})]}}
从断点处恢复¶
现在让我们看看断点处会发生什么。我们将使用一个查询来调用它，该查询应该被路由到具有中断节点的天气子图中。
config = {"configurable": {"thread_id": "2"}}
inputs = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
for update in graph.stream(inputs, config=config, stream_mode="updates"):
print(update)
subgraphs=True，从而接收子图事件，如下所示：
config = {"configurable": {"thread_id": "3"}}
inputs = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
for update in graph.stream(inputs, config=config, stream_mode="values", subgraphs=True):
print(update)
((), {'messages': [HumanMessage(content="what's the weather in sf", additional_kwargs={}, response_metadata={}, id='108eb27a-2cbf-48d2-a6e7-6e07e82eafbc')]})
((), {'messages': [HumanMessage(content="what's the weather in sf", additional_kwargs={}, response_metadata={}, id='108eb27a-2cbf-48d2-a6e7-6e07e82eafbc')], 'route': 'weather'})
(('weather_graph:0c47aeb3-6f4d-5e68-ccf4-42bd48e8ef20',), {'messages': [HumanMessage(content="what's the weather in sf", additional_kwargs={}, response_metadata={}, id='108eb27a-2cbf-48d2-a6e7-6e07e82eafbc')]})
(('weather_graph:0c47aeb3-6f4d-5e68-ccf4-42bd48e8ef20',), {'messages': [HumanMessage(content="what's the weather in sf", additional_kwargs={}, response_metadata={}, id='108eb27a-2cbf-48d2-a6e7-6e07e82eafbc')], 'city': 'San Francisco'})
weather_graph处暂停了。
如果我们查看当前状态下待处理的任务，可以看到有一个名为weather_graph的任务，它对应于子图任务。
(PregelTask(id='0c47aeb3-6f4d-5e68-ccf4-42bd48e8ef20', name='weather_graph', path=('__pregel_pull', 'weather_graph'), error=None, interrupts=(), state={'configurable': {'thread_id': '3', 'checkpoint_ns': 'weather_graph:0c47aeb3-6f4d-5e68-ccf4-42bd48e8ef20'}}),)
然而，由于我们使用父图的配置获取了状态，因此无法访问子图的状态。如果你查看上面PregelTask的state值，会发现它仅仅是父图的配置。如果我们希望实际填充子图的状态，可以在调用get_state时传入subgraphs=True，如下所示：
PregelTask(id='0c47aeb3-6f4d-5e68-ccf4-42bd48e8ef20', name='weather_graph', path=('__pregel_pull', 'weather_graph'), error=None, interrupts=(), state=StateSnapshot(values={'messages': [HumanMessage(content="what's the weather in sf", additional_kwargs={}, response_metadata={}, id='108eb27a-2cbf-48d2-a6e7-6e07e82eafbc')], 'city': 'San Francisco'}, next=('weather_node',), config={'configurable': {'thread_id': '3', 'checkpoint_ns': 'weather_graph:0c47aeb3-6f4d-5e68-ccf4-42bd48e8ef20', 'checkpoint_id': '1ef75ee0-d9c3-6242-8001-440e7a3fb19f', 'checkpoint_map': {'': '1ef75ee0-d4e8-6ede-8001-2542067239ef', 'weather_graph:0c47aeb3-6f4d-5e68-ccf4-42bd48e8ef20': '1ef75ee0-d9c3-6242-8001-440e7a3fb19f'}}}, metadata={'source': 'loop', 'writes': {'model_node': {'city': 'San Francisco'}}, 'step': 1, 'parents': {'': '1ef75ee0-d4e8-6ede-8001-2542067239ef'}}, created_at='2024-09-18T18:44:36.278105+00:00', parent_config={'configurable': {'thread_id': '3', 'checkpoint_ns': 'weather_graph:0c47aeb3-6f4d-5e68-ccf4-42bd48e8ef20', 'checkpoint_id': '1ef75ee0-d4ef-6dec-8000-5d5724f3ef73'}}, tasks=(PregelTask(id='26f4384a-41d7-5ca9-cb94-4001de62e8aa', name='weather_node', path=('__pregel_pull', 'weather_node'), error=None, interrupts=(), state=None),)))
现在我们可以访问子图的状态了！如果你查看PregelTask的state值，你会发现它包含了我们需要的所有信息，比如下一个节点（weather_node）和当前的状态值（例如city）。
要恢复执行，我们只需像平常一样调用外部图即可：
for update in graph.stream(None, config=config, stream_mode="values", subgraphs=True):
print(update)
((), {'messages': [HumanMessage(content="what's the weather in sf", additional_kwargs={}, response_metadata={}, id='108eb27a-2cbf-48d2-a6e7-6e07e82eafbc')], 'route': 'weather'})
(('weather_graph:0c47aeb3-6f4d-5e68-ccf4-42bd48e8ef20',), {'messages': [HumanMessage(content="what's the weather in sf", additional_kwargs={}, response_metadata={}, id='108eb27a-2cbf-48d2-a6e7-6e07e82eafbc')], 'city': 'San Francisco'})
(('weather_graph:0c47aeb3-6f4d-5e68-ccf4-42bd48e8ef20',), {'messages': [HumanMessage(content="what's the weather in sf", additional_kwargs={}, response_metadata={}, id='108eb27a-2cbf-48d2-a6e7-6e07e82eafbc'), AIMessage(content="It's sunny in San Francisco!", additional_kwargs={}, response_metadata={}, id='c996ce37-438c-44f4-9e60-5aed8bcdae8a')], 'city': 'San Francisco'})
((), {'messages': [HumanMessage(content="what's the weather in sf", additional_kwargs={}, response_metadata={}, id='108eb27a-2cbf-48d2-a6e7-6e07e82eafbc'), AIMessage(content="It's sunny in San Francisco!", additional_kwargs={}, response_metadata={}, id='c996ce37-438c-44f4-9e60-5aed8bcdae8a')], 'route': 'weather'})
从特定子图节点恢复¶
在上面的例子中，我们是从外部图重新播放的——这会自动从子图之前的状态（例如，在我们的例子中，子图在weather_node之前被暂停）重新播放，但是也可以从子图内部重新播放。为了做到这一点，我们需要从我们想要重新播放的确切子图状态获取配置。
我们可以通过探索子图的状态历史，并选择model_node之前的状态来实现这一点——我们可以通过过滤.next参数来实现。
为了获取子图的状态历史，我们首先需要传入
parent_graph_state_before_subgraph = next(
h for h in graph.get_state_history(config) if h.next == ("weather_graph",)
)
subgraph_state_before_model_node = next(
h
for h in graph.get_state_history(parent_graph_state_before_subgraph.tasks[0].state)
if h.next == ("model_node",)
)
# This pattern can be extended no matter how many levels deep
# subsubgraph_stat_history = next(h for h in graph.get_state_history(subgraph_state_before_model_node.tasks[0].state) if h.next == ('my_subsubgraph_node',))
我们可以通过比较subgraph_state_before_model_node的.next参数来确认已经获得了正确的状态。
完美！我们现在获得了正确的状态快照，并可以从此子图中的model_node恢复。
for value in graph.stream(
None,
config=subgraph_state_before_model_node.config,
stream_mode="values",
subgraphs=True,
):
print(value)
((), {'messages': [HumanMessage(content="what's the weather in sf", additional_kwargs={}, response_metadata={}, id='108eb27a-2cbf-48d2-a6e7-6e07e82eafbc')], 'route': 'weather'})
(('weather_graph:0c47aeb3-6f4d-5e68-ccf4-42bd48e8ef20',), {'messages': [HumanMessage(content="what's the weather in sf", additional_kwargs={}, response_metadata={}, id='108eb27a-2cbf-48d2-a6e7-6e07e82eafbc')]})
(('weather_graph:0c47aeb3-6f4d-5e68-ccf4-42bd48e8ef20',), {'messages': [HumanMessage(content="what's the weather in sf", additional_kwargs={}, response_metadata={}, id='108eb27a-2cbf-48d2-a6e7-6e07e82eafbc')], 'city': 'San Francisco'})
修改子图的状态¶
如果我们想修改子图的状态怎么办？我们可以像更新普通图的状态那样进行操作，只是需要小心地将子图的配置传递给update_state。
config = {"configurable": {"thread_id": "4"}}
inputs = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
for update in graph.stream(inputs, config=config, stream_mode="updates"):
print(update)
[HumanMessage(content="what's the weather in sf", additional_kwargs={}, response_metadata={}, id='05ee2159-3b25-4d6c-97d6-82beda3cabd4')]
为了更新**内部**图的状态，我们需要传递**内部**图的配置，可以通过调用state.tasks[0].state.config来获取——因为我们是在子图内部中断的，所以任务的状态就是子图的状态。
{'configurable': {'thread_id': '4',
'checkpoint_ns': 'weather_graph:67f32ef7-aee0-8a20-0eb0-eeea0fd6de6e',
'checkpoint_id': '1ef75e5a-0b00-6bc0-8002-5726e210fef4',
'checkpoint_map': {'': '1ef75e59-1b13-6ffe-8001-0844ae748fd5',
'weather_graph:67f32ef7-aee0-8a20-0eb0-eeea0fd6de6e': '1ef75e5a-0b00-6bc0-8002-5726e210fef4'}}}
我们现在可以恢复外图的流式处理（这将恢复子图！），并检查我们是否已更新搜索以使用LA（洛杉矶）而不是SF（旧金山）。
for update in graph.stream(None, config=config, stream_mode="updates", subgraphs=True):
print(update)
(('weather_graph:9e512e8e-bac5-5412-babe-fe5c12a47cc2',), {'weather_node': {'messages': [{'role': 'assistant', 'content': "It's sunny in la!"}]}})
((), {'weather_graph': {'messages': [HumanMessage(content="what's the weather in sf", id='35e331c6-eb47-483c-a63c-585877b12f5d'), AIMessage(content="It's sunny in la!", id='c3d6b224-9642-4b21-94d5-eef8dc3f2cc9')]}})
作为子图节点¶
另一种更新状态的方法是自己充当 weather_node，而不是像上面那样在 weather_node 运行之前编辑状态。我们可以通过传递子图配置以及 as_node 参数来实现这一点，这允许我们像指定的节点一样更新状态。因此，通过在 weather_node 之前设置一个中断，然后使用更新状态函数作为 weather_node，图本身不会直接调用 weather_node，而是我们决定 weather_node 的输出结果。
config = {"configurable": {"thread_id": "14"}}
inputs = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
for update in graph.stream(
inputs, config=config, stream_mode="updates", subgraphs=True
):
print(update)
# Graph execution should stop before the weather node
print("interrupted!")
state = graph.get_state(config, subgraphs=True)
# We update the state by passing in the message we want returned from the weather node, and make sure to use as_node
graph.update_state(
state.tasks[0].state.config,
{"messages": [{"role": "assistant", "content": "rainy"}]},
as_node="weather_node",
)
for update in graph.stream(None, config=config, stream_mode="updates", subgraphs=True):
print(update)
print(graph.get_state(config).values["messages"])
((), {'router_node': {'route': 'weather'}})
(('weather_graph:c7eb1fc7-efab-b0e3-12ed-8586f37bc7a2',), {'model_node': {'city': 'San Francisco'}})
interrupted!
((), {'weather_graph': {'messages': [HumanMessage(content="what's the weather in sf", additional_kwargs={}, response_metadata={}, id='ad694c4e-8aac-4e1f-b5ca-790c60c1775b'), AIMessage(content='rainy', additional_kwargs={}, response_metadata={}, id='98a73aaf-3524-482a-9d07-971407df0389')]}})
[HumanMessage(content="what's the weather in sf", additional_kwargs={}, response_metadata={}, id='ad694c4e-8aac-4e1f-b5ca-790c60c1775b'), AIMessage(content='rainy', additional_kwargs={}, response_metadata={}, id='98a73aaf-3524-482a-9d07-971407df0389')]
作为整个子图¶
最后，我们还可以通过仅作为**整个**子图来更新图。这与上面的情况类似，但不是仅作为 weather_node，而是作为整个子图。这可以通过传递正常的图配置以及 as_node 参数来完成，在该参数中指定我们是作为整个子图节点来操作的。
config = {"configurable": {"thread_id": "8"}}
inputs = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
for update in graph.stream(
inputs, config=config, stream_mode="updates", subgraphs=True
):
print(update)
# Graph execution should stop before the weather node
print("interrupted!")
# We update the state by passing in the message we want returned from the weather graph, making sure to use as_node
# Note that we don't need to pass in the subgraph config, since we aren't updating the state inside the subgraph
graph.update_state(
config,
{"messages": [{"role": "assistant", "content": "rainy"}]},
as_node="weather_graph",
)
for update in graph.stream(None, config=config, stream_mode="updates"):
print(update)
print(graph.get_state(config).values["messages"])
((), {'router_node': {'route': 'weather'}})
(('weather_graph:53ab3fb1-23e8-5de0-acc6-9fb904fd4dc4',), {'model_node': {'city': 'San Francisco'}})
interrupted!
[HumanMessage(content="what's the weather in sf", id='64b1b683-778b-4623-b783-4a8f81322ec8'), AIMessage(content='rainy', id='c1d1a2f3-c117-41e9-8c1f-8fb0a02a3b70')]
双重嵌套子图¶
无论嵌套级别如何，这种功能仍然有效。这里给出一个使用双重嵌套子图的示例（尽管任何级别的嵌套都可以使用）。我们在已经定义的图上添加另一个路由器。
from typing import Literal
from typing_extensions import TypedDict
from langgraph.checkpoint.memory import MemorySaver
memory = MemorySaver()
class RouterState(MessagesState):
route: Literal["weather", "other"]
class Router(TypedDict):
route: Literal["weather", "other"]
router_model = raw_model.with_structured_output(Router)
def router_node(state: RouterState):
system_message = "Classify the incoming query as either about weather or not."
messages = [{"role": "system", "content": system_message}] + state["messages"]
route = router_model.invoke(messages)
return {"route": route["route"]}
def normal_llm_node(state: RouterState):
response = raw_model.invoke(state["messages"])
return {"messages": [response]}
def route_after_prediction(
state: RouterState,
) -> Literal["weather_graph", "normal_llm_node"]:
if state["route"] == "weather":
return "weather_graph"
else:
return "normal_llm_node"
graph = StateGraph(RouterState)
graph.add_node(router_node)
graph.add_node(normal_llm_node)
graph.add_node("weather_graph", subgraph)
graph.add_edge(START, "router_node")
graph.add_conditional_edges("router_node", route_after_prediction)
graph.add_edge("normal_llm_node", END)
graph.add_edge("weather_graph", END)
graph = graph.compile()
API Reference: MemorySaver
from langgraph.checkpoint.memory import MemorySaver
memory = MemorySaver()
class GrandfatherState(MessagesState):
to_continue: bool
def router_node(state: GrandfatherState):
# Dummy logic that will always continue
return {"to_continue": True}
def route_after_prediction(state: GrandfatherState):
if state["to_continue"]:
return "graph"
else:
return END
grandparent_graph = StateGraph(GrandfatherState)
grandparent_graph.add_node(router_node)
grandparent_graph.add_node("graph", graph)
grandparent_graph.add_edge(START, "router_node")
grandparent_graph.add_conditional_edges(
"router_node", route_after_prediction, ["graph", END]
)
grandparent_graph.add_edge("graph", END)
grandparent_graph = grandparent_graph.compile(checkpointer=MemorySaver())
API Reference: MemorySaver
from IPython.display import Image, display
# Setting xray to 1 will show the internal structure of the nested graph
display(Image(grandparent_graph.get_graph(xray=2).draw_mermaid_png()))
如果我们运行到中断处，现在可以看到这三个图的当前状态快照。
config = {"configurable": {"thread_id": "2"}}
inputs = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
for update in grandparent_graph.stream(
inputs, config=config, stream_mode="updates", subgraphs=True
):
print(update)
((), {'router_node': {'to_continue': True}})
(('graph:e18ecd45-5dfb-53b0-bcb7-db793924e9a8',), {'router_node': {'route': 'weather'}})
(('graph:e18ecd45-5dfb-53b0-bcb7-db793924e9a8', 'weather_graph:12bd3069-de24-5bc6-b4f1-f39527605781'), {'model_node': {'city': 'San Francisco'}})
state = grandparent_graph.get_state(config, subgraphs=True)
print("Grandparent State:")
print(state.values)
print("---------------")
print("Parent Graph State:")
print(state.tasks[0].state.values)
print("---------------")
print("Subgraph State:")
print(state.tasks[0].state.tasks[0].state.values)
Grandparent State:
{'messages': [HumanMessage(content="what's the weather in sf", id='3bb28060-3d30-49a7-9f84-c90b6ada7848')], 'to_continue': True}
---------------
Parent Graph State:
{'messages': [HumanMessage(content="what's the weather in sf", id='3bb28060-3d30-49a7-9f84-c90b6ada7848')], 'route': 'weather'}
---------------
Subgraph State:
{'messages': [HumanMessage(content="what's the weather in sf", id='3bb28060-3d30-49a7-9f84-c90b6ada7848')], 'city': 'San Francisco'}
grandparent_graph_state = state
parent_graph_state = grandparent_graph_state.tasks[0].state
subgraph_state = parent_graph_state.tasks[0].state
grandparent_graph.update_state(
subgraph_state.config,
{"messages": [{"role": "assistant", "content": "rainy"}]},
as_node="weather_node",
)
for update in grandparent_graph.stream(
None, config=config, stream_mode="updates", subgraphs=True
):
print(update)
print(grandparent_graph.get_state(config).values["messages"])
(('graph:e18ecd45-5dfb-53b0-bcb7-db793924e9a8',), {'weather_graph': {'messages': [HumanMessage(content="what's the weather in sf", id='3bb28060-3d30-49a7-9f84-c90b6ada7848'), AIMessage(content='rainy', id='be926b59-c647-4355-88fd-a429b9e2b420')]}})
((), {'graph': {'messages': [HumanMessage(content="what's the weather in sf", id='3bb28060-3d30-49a7-9f84-c90b6ada7848'), AIMessage(content='rainy', id='be926b59-c647-4355-88fd-a429b9e2b420')]}})
[HumanMessage(content="what's the weather in sf", id='3bb28060-3d30-49a7-9f84-c90b6ada7848'), AIMessage(content='rainy', id='be926b59-c647-4355-88fd-a429b9e2b420')]
我们可以通过探索状态历史来查看祖父图的状态在每一步是如何更新的。
StateSnapshot(values={'messages': [HumanMessage(content="what's the weather in sf", id='5ff89e4d-8255-4d23-8b55-01633c112720'), AIMessage(content='rainy', id='7c80f847-248d-4b8f-8238-633ed757b353')], 'to_continue': True}, next=(), config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef66f40-7a2c-6f9e-8002-a37a61b26709'}}, metadata={'source': 'loop', 'writes': {'graph': {'messages': [HumanMessage(content="what's the weather in sf", id='5ff89e4d-8255-4d23-8b55-01633c112720'), AIMessage(content='rainy', id='7c80f847-248d-4b8f-8238-633ed757b353')]}}, 'step': 2, 'parents': {}}, created_at='2024-08-30T17:19:35.793847+00:00', parent_config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef66f3f-f312-6338-8001-766acddc781e'}}, tasks=())
-----
StateSnapshot(values={'messages': [HumanMessage(content="what's the weather in sf", id='5ff89e4d-8255-4d23-8b55-01633c112720')], 'to_continue': True}, next=('graph',), config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef66f3f-f312-6338-8001-766acddc781e'}}, metadata={'source': 'loop', 'writes': {'router_node': {'to_continue': True}}, 'step': 1, 'parents': {}}, created_at='2024-08-30T17:19:21.627097+00:00', parent_config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef66f3f-f303-61d0-8000-1945c8a74e9e'}}, tasks=(PregelTask(id='b59fe96f-fdce-5afe-aa58-bd2876a0d592', name='graph', error=None, interrupts=(), state={'configurable': {'thread_id': '2', 'checkpoint_ns': 'graph:b59fe96f-fdce-5afe-aa58-bd2876a0d592'}}),))
-----
StateSnapshot(values={'messages': [HumanMessage(content="what's the weather in sf", id='5ff89e4d-8255-4d23-8b55-01633c112720')]}, next=('router_node',), config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef66f3f-f303-61d0-8000-1945c8a74e9e'}}, metadata={'source': 'loop', 'writes': None, 'step': 0, 'parents': {}}, created_at='2024-08-30T17:19:21.620923+00:00', parent_config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef66f3f-f2f9-6d6a-bfff-c8b76e5b2462'}}, tasks=(PregelTask(id='e3d4a97a-f4ca-5260-801e-e65b02907825', name='router_node', error=None, interrupts=(), state=None),))
-----
StateSnapshot(values={'messages': []}, next=('__start__',), config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef66f3f-f2f9-6d6a-bfff-c8b76e5b2462'}}, metadata={'source': 'input', 'writes': {'messages': [{'role': 'user', 'content': "what's the weather in sf"}]}, 'step': -1, 'parents': {}}, created_at='2024-08-30T17:19:21.617127+00:00', parent_config=None, tasks=(PregelTask(id='f0538638-b794-58fc-a406-980d2fea28a1', name='__start__', error=None, interrupts=(), state=None),))
-----