[PYTHON/LANGGRAPH] CompiledStateGraph 클래스 : get_state_history 메소드를 사용해 상태 히스토리 구하기

■ CompiledStateGraph 클래스의 get_state_history 메소드를 사용해 상태 히스토리를 구하는 방법을 보여준다.

※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.
※ TAVILY_API_KEY 환경 변수 값은 .env 파일에 정의한다.

▶ main.py


from dotenv                                  import load_dotenv
from typing                                  import Annotated
from langchain_openai                        import ChatOpenAI
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.messages                 import AIMessage
from langchain_core.messages                 import ToolMessage
from pydantic                                import BaseModel
from typing_extensions                       import TypedDict
from langgraph.checkpoint.memory             import MemorySaver
from langgraph.graph                         import StateGraph
from langgraph.graph                         import START
from langgraph.graph                         import END
from langgraph.graph.message                 import add_messages
from langgraph.prebuilt                      import ToolNode
from langgraph.prebuilt                      import tools_condition

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o-mini")

tavilySearchResults = TavilySearchResults(max_results = 2)

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

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

toolList = [tavilySearchResults, RequestAssistance]

runnableBinding = chatOpenAI.bind_tools(toolList)

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

def chat(state : State):
    responseAIMessage = runnableBinding.invoke(state["messages"])
    askHuman          = False
    if (responseAIMessage.tool_calls and responseAIMessage.tool_calls[0]["name"] == RequestAssistance.__name__):
        askHuman = True
    return {"messages" : [responseAIMessage], "askHuman" : askHuman}

def createToolMessage(content : str, aiMessage : AIMessage):
    return ToolMessage(content = content, tool_call_id = aiMessage.tool_calls[0]["id"])

def checkHuman(state : State):
    newMessageList = []
    if not isinstance(state["messages"][-1], ToolMessage):
        # 일반적으로 사용자는 인터럽트 중에 상태를 업데이트했을 것이다.
        # 그렇지 않으면 LLM이 계속 진행될 수 있도록 플레이스홀더 ToolMessage를 포함한다.
        newMessageList.append(createToolMessage("No response from human.", state["messages"][-1]))
    return {"messages" : newMessageList, "askHuman" : False}

def selectNextNode(state : State):
    if state["askHuman"]:
        return "human_node"
    return tools_condition(state)

stateGraph = StateGraph(State)

stateGraph.add_node("chatbot_node", chat)
stateGraph.add_node("tools"       , ToolNode(tools = [tavilySearchResults]))
stateGraph.add_node("human_node"  , checkHuman)

stateGraph.add_conditional_edges("chatbot_node", selectNextNode, {"human_node" : "human_node", "tools" : "tools", END : END})
stateGraph.add_edge("tools"     , "chatbot_node")
stateGraph.add_edge("human_node", "chatbot_node")
stateGraph.add_edge(START       , "chatbot_node")

memorySaver = MemorySaver()

compiledStateGraph = stateGraph.compile(checkpointer = memorySaver, interrupt_before = ["human_node"])

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

generator1 = compiledStateGraph.stream(
    {"messages" : [("user", "I'm learning LangGraph. Could you do some research on it for me?")]},
    configurableDictionary,
    stream_mode = "values"
)

for addableUpdatesDict in generator1:
    if "messages" in addableUpdatesDict:
        print(addableUpdatesDict["messages"][-1])
print("-" * 50)

generator2 = compiledStateGraph.stream(
    {"messages" : [("user", "Ya that's helpful. Maybe I'll build an autonomous agent with it!")]},
    configurableDictionary,
    stream_mode = "values"
)

for addableUpdatesDict in generator2:
    if "messages" in addableUpdatesDict:
        print(addableUpdatesDict["messages"][-1])
print("-" * 50)

stateToReplay = None

for stateSnapshot in compiledStateGraph.get_state_history(configurableDictionary):
    print(f"Message count : {len(stateSnapshot.values["messages"])}, Next : {stateSnapshot.next}")
    if len(stateSnapshot.values["messages"]) == 6:
        # 우리는 상태에서 채팅 메시지 수에 따라 특정 상태를 다소 임의로 선택하고 있다.
        stateToReplay = stateSnapshot
print("-" * 50)

# `to_replay.config`의 `checkpoint_id`는 체크 포인터에 저장된 상태에 해당한다.
for addableUpdatesDict in compiledStateGraph.stream(None, stateToReplay.config, stream_mode = "values"):
    if "messages" in addableUpdatesDict:
        print(addableUpdatesDict["messages"][-1])
print("-" * 50)

"""
content="I'm learning LangGraph. Could you do some research on it for me?" additional_kwargs={} response_metadata={} id='415a41dd-fd1f-4f41-ae89-618f8139f5f2'
content='' additional_kwargs={'tool_calls': [{'id': 'call_ADj6WXkV0LrMxEzCcY46RLa9', 'function': {'arguments': '{"query":"LangGraph"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}], 'refusal': None} response_metadata={'token_usage': {'completion_tokens': 20, 'prompt_tokens': 157, 'total_tokens': 177, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_0aa8d3e20b', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-f645107d-250f-4596-bda4-817e2c65fbb8-0' tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'LangGraph'}, 'id': 'call_ADj6WXkV0LrMxEzCcY46RLa9', 'type': 'tool_call'}] usage_metadata={'input_tokens': 157, 'output_tokens': 20, 'total_tokens': 177, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}
content='[{"url": "https://langchain-ai.github.io/langgraph/", "content": "Overview¶ · LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows."}, {"url": "https://langchain-ai.github.io/langgraph/tutorials/introduction/", "content": "LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM"}]' name='tavily_search_results_json' id='b6108c11-a863-40d8-8a5b-832af87d0260' tool_call_id='call_ADj6WXkV0LrMxEzCcY46RLa9' artifact={'query': 'LangGraph', 'follow_up_questions': None, 'answer': None, 'images': [], 'results': [{'url': 'https://langchain-ai.github.io/langgraph/', 'title': 'LangGraph', 'content': 'Overview¶ · LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows.', 'score': 0.93131906, 'raw_content': None}, {'url': 'https://langchain-ai.github.io/langgraph/tutorials/introduction/', 'title': 'LangGraph Quick Start - GitHub Pages', 'content': 'LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM', 'score': 0.915091, 'raw_content': None}], 'response_time': 2.0}
content='LangGraph is a library designed for building stateful, multi-actor applications using Large Language Models (LLMs). It focuses on creating agent and multi-agent workflows, allowing developers to leverage the capabilities of LLMs in a structured manner.\n\nFor more detailed information, you can check out the following resources:\n- [LangGraph Overview](https://langchain-ai.github.io/langgraph/)\n- [LangGraph Introduction Tutorial](https://langchain-ai.github.io/langgraph/tutorials/introduction/) \n\nThese resources provide insights into its features, use cases, and how to get started with LangGraph. If you have specific questions or need further assistance, feel free to ask!' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 136, 'prompt_tokens': 296, 'total_tokens': 432, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_0aa8d3e20b', 'finish_reason': 'stop', 'logprobs': None} id='run-c470584c-8118-478f-abda-26375a0565e8-0' usage_metadata={'input_tokens': 296, 'output_tokens': 136, 'total_tokens': 432, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}
--------------------------------------------------
content="Ya that's helpful. Maybe I'll build an autonomous agent with it!" additional_kwargs={} response_metadata={} id='eea3bf16-557d-4efb-a101-c297ff5c0b2e'
content='That sounds like an exciting project! Building an autonomous agent with LangGraph can open up many possibilities for automation and intelligent workflows. If you have any questions about the development process, specific functionalities, or need tips and resources as you work on your project, feel free to reach out. Good luck with your autonomous agent!' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 65, 'prompt_tokens': 451, 'total_tokens': 516, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details':
{'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_0aa8d3e20b', 'finish_reason': 'stop', 'logprobs': None} id='run-e56dd90c-ac11-4ee0-85a2-c86a732d5778-0' usage_metadata={'input_tokens': 451, 'output_tokens': 65, 'total_tokens': 516, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}
--------------------------------------------------
Message count : 6, Next : ()
Message count : 5, Next : ('chatbot_node',)
Message count : 4, Next : ('__start__',)
Message count : 4, Next : ()
Message count : 3, Next : ('chatbot_node',)
Message count : 2, Next : ('tools',)
Message count : 1, Next : ('chatbot_node',)
Message count : 0, Next : ('__start__',)
--------------------------------------------------
content='That sounds like an exciting project! Building an autonomous agent with LangGraph can open up many possibilities for automation and intelligent workflows. If you have any questions about the development process, specific functionalities, or need tips and resources as you work on your project, feel free to reach out. Good luck with your autonomous agent!' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 65, 'prompt_tokens': 451, 'total_tokens': 516, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details':
{'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_0aa8d3e20b', 'finish_reason': 'stop', 'logprobs': None} id='run-e56dd90c-ac11-4ee0-85a2-c86a732d5778-0' usage_metadata={'input_tokens': 451, 'output_tokens': 65, 'total_tokens': 516, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}
--------------------------------------------------
"""

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from dotenv import load_dotenv

from typing import Annotated

from langchain_openai import ChatOpenAI

from langchain_community.tools.tavily_search import TavilySearchResults

from langchain_core.messages import AIMessage

from langchain_core.messages import ToolMessage

from pydantic import BaseModel

from typing_extensions import TypedDict

from langgraph.checkpoint.memory import MemorySaver

from langgraph.graph import StateGraph

from langgraph.graph import START

from langgraph.graph import END

from langgraph.graph.message import add_messages

from langgraph.prebuilt import ToolNode

from langgraph.prebuilt import tools_condition

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o-mini")

tavilySearchResults = TavilySearchResults(max_results = 2)

class RequestAssistance(BaseModel):

"""Escalate the conversation to an expert. Use this if you are unable to assist directly or if the user requires support beyond your permissions.

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

"""

request : str

toolList = [tavilySearchResults, RequestAssistance]

runnableBinding = chatOpenAI.bind_tools(toolList)

class State(TypedDict):

messages : Annotated[list, add_messages]

askHuman : bool

def chat(state : State):

responseAIMessage = runnableBinding.invoke(state["messages"])

askHuman = False

if (responseAIMessage.tool_calls and responseAIMessage.tool_calls[0]["name"] == RequestAssistance.__name__):

askHuman = True

return {"messages" : [responseAIMessage], "askHuman" : askHuman}

def createToolMessage(content : str, aiMessage : AIMessage):

return ToolMessage(content = content, tool_call_id = aiMessage.tool_calls[0]["id"])

def checkHuman(state : State):

newMessageList = []

if not isinstance(state["messages"][-1], ToolMessage):

# 일반적으로 사용자는 인터럽트 중에 상태를 업데이트했을 것이다.

# 그렇지 않으면 LLM이 계속 진행될 수 있도록 플레이스홀더 ToolMessage를 포함한다.

newMessageList.append(createToolMessage("No response from human.", state["messages"][-1]))

return {"messages" : newMessageList, "askHuman" : False}

def selectNextNode(state : State):

if state["askHuman"]:

return "human_node"

return tools_condition(state)

stateGraph = StateGraph(State)

stateGraph.add_node("chatbot_node", chat)

stateGraph.add_node("tools" , ToolNode(tools = [tavilySearchResults]))

stateGraph.add_node("human_node" , checkHuman)

stateGraph.add_conditional_edges("chatbot_node", selectNextNode, {"human_node" : "human_node", "tools" : "tools", END : END})

stateGraph.add_edge("tools" , "chatbot_node")

stateGraph.add_edge("human_node", "chatbot_node")

stateGraph.add_edge(START , "chatbot_node")

memorySaver = MemorySaver()

compiledStateGraph = stateGraph.compile(checkpointer = memorySaver, interrupt_before = ["human_node"])

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

generator1 = compiledStateGraph.stream(

{"messages" : [("user", "I'm learning LangGraph. Could you do some research on it for me?")]},

configurableDictionary,

stream_mode = "values"

)

for addableUpdatesDict in generator1:

if "messages" in addableUpdatesDict:

print(addableUpdatesDict["messages"][-1])

print("-" * 50)

generator2 = compiledStateGraph.stream(

{"messages" : [("user", "Ya that's helpful. Maybe I'll build an autonomous agent with it!")]},

configurableDictionary,

stream_mode = "values"

)

for addableUpdatesDict in generator2:

if "messages" in addableUpdatesDict:

print(addableUpdatesDict["messages"][-1])

print("-" * 50)

stateToReplay = None

for stateSnapshot in compiledStateGraph.get_state_history(configurableDictionary):

print(f"Message count : {len(stateSnapshot.values["messages"])}, Next : {stateSnapshot.next}")

if len(stateSnapshot.values["messages"]) == 6:

# 우리는 상태에서 채팅 메시지 수에 따라 특정 상태를 다소 임의로 선택하고 있다.

stateToReplay = stateSnapshot

print("-" * 50)

# `to_replay.config`의 `checkpoint_id`는 체크 포인터에 저장된 상태에 해당한다.

for addableUpdatesDict in compiledStateGraph.stream(None, stateToReplay.config, stream_mode = "values"):

if "messages" in addableUpdatesDict:

print(addableUpdatesDict["messages"][-1])

print("-" * 50)

"""

content="I'm learning LangGraph. Could you do some research on it for me?" additional_kwargs={} response_metadata={} id='415a41dd-fd1f-4f41-ae89-618f8139f5f2'

content='' additional_kwargs={'tool_calls': [{'id': 'call_ADj6WXkV0LrMxEzCcY46RLa9', 'function': {'arguments': '{"query":"LangGraph"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}], 'refusal': None} response_metadata={'token_usage': {'completion_tokens': 20, 'prompt_tokens': 157, 'total_tokens': 177, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_0aa8d3e20b', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-f645107d-250f-4596-bda4-817e2c65fbb8-0' tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'LangGraph'}, 'id': 'call_ADj6WXkV0LrMxEzCcY46RLa9', 'type': 'tool_call'}] usage_metadata={'input_tokens': 157, 'output_tokens': 20, 'total_tokens': 177, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}

content='[{"url": "https://langchain-ai.github.io/langgraph/", "content": "Overview¶ · LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows."}, {"url": "https://langchain-ai.github.io/langgraph/tutorials/introduction/", "content": "LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM"}]' name='tavily_search_results_json' id='b6108c11-a863-40d8-8a5b-832af87d0260' tool_call_id='call_ADj6WXkV0LrMxEzCcY46RLa9' artifact={'query': 'LangGraph', 'follow_up_questions': None, 'answer': None, 'images': [], 'results': [{'url': 'https://langchain-ai.github.io/langgraph/', 'title': 'LangGraph', 'content': 'Overview¶ · LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows.', 'score': 0.93131906, 'raw_content': None}, {'url': 'https://langchain-ai.github.io/langgraph/tutorials/introduction/', 'title': 'LangGraph Quick Start - GitHub Pages', 'content': 'LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM', 'score': 0.915091, 'raw_content': None}], 'response_time': 2.0}

content='LangGraph is a library designed for building stateful, multi-actor applications using Large Language Models (LLMs). It focuses on creating agent and multi-agent workflows, allowing developers to leverage the capabilities of LLMs in a structured manner.\n\nFor more detailed information, you can check out the following resources:\n- [LangGraph Overview](https://langchain-ai.github.io/langgraph/)\n- [LangGraph Introduction Tutorial](https://langchain-ai.github.io/langgraph/tutorials/introduction/) \n\nThese resources provide insights into its features, use cases, and how to get started with LangGraph. If you have specific questions or need further assistance, feel free to ask!' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 136, 'prompt_tokens': 296, 'total_tokens': 432, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_0aa8d3e20b', 'finish_reason': 'stop', 'logprobs': None} id='run-c470584c-8118-478f-abda-26375a0565e8-0' usage_metadata={'input_tokens': 296, 'output_tokens': 136, 'total_tokens': 432, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}

--------------------------------------------------

content="Ya that's helpful. Maybe I'll build an autonomous agent with it!" additional_kwargs={} response_metadata={} id='eea3bf16-557d-4efb-a101-c297ff5c0b2e'

content='That sounds like an exciting project! Building an autonomous agent with LangGraph can open up many possibilities for automation and intelligent workflows. If you have any questions about the development process, specific functionalities, or need tips and resources as you work on your project, feel free to reach out. Good luck with your autonomous agent!' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 65, 'prompt_tokens': 451, 'total_tokens': 516, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details':

{'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_0aa8d3e20b', 'finish_reason': 'stop', 'logprobs': None} id='run-e56dd90c-ac11-4ee0-85a2-c86a732d5778-0' usage_metadata={'input_tokens': 451, 'output_tokens': 65, 'total_tokens': 516, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}

--------------------------------------------------

Message count : 6, Next : ()

Message count : 5, Next : ('chatbot_node',)

Message count : 4, Next : ('__start__',)

Message count : 4, Next : ()

Message count : 3, Next : ('chatbot_node',)

Message count : 2, Next : ('tools',)

Message count : 1, Next : ('chatbot_node',)

Message count : 0, Next : ('__start__',)

--------------------------------------------------

"""

▶ requirements.txt


aiohappyeyeballs==2.4.4
aiohttp==3.11.11
aiosignal==1.3.2
annotated-types==0.7.0
anyio==4.7.0
attrs==24.3.0
certifi==2024.12.14
charset-normalizer==3.4.1
colorama==0.4.6
dataclasses-json==0.6.7
distro==1.9.0
frozenlist==1.5.0
greenlet==3.1.1
h11==0.14.0
httpcore==1.0.7
httpx==0.28.1
httpx-sse==0.4.0
idna==3.10
jiter==0.8.2
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.13
langchain-community==0.3.13
langchain-core==0.3.28
langchain-openai==0.2.14
langchain-text-splitters==0.3.4
langgraph==0.2.60
langgraph-checkpoint==2.0.9
langgraph-sdk==0.1.48
langsmith==0.2.7
marshmallow==3.23.2
msgpack==1.1.0
multidict==6.1.0
mypy-extensions==1.0.0
numpy==2.2.1
openai==1.58.1
orjson==3.10.13
packaging==24.2
propcache==0.2.1
pydantic==2.10.4
pydantic-settings==2.7.1
pydantic_core==2.27.2
python-dotenv==1.0.1
PyYAML==6.0.2
regex==2024.11.6
requests==2.32.3
requests-toolbelt==1.0.0
sniffio==1.3.1
SQLAlchemy==2.0.36
tenacity==9.0.0
tiktoken==0.8.0
tqdm==4.67.1
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.3.0
yarl==1.18.3

aiohappyeyeballs==2.4.4

aiohttp==3.11.11

aiosignal==1.3.2

annotated-types==0.7.0

anyio==4.7.0

attrs==24.3.0

certifi==2024.12.14

charset-normalizer==3.4.1

colorama==0.4.6

dataclasses-json==0.6.7

distro==1.9.0

frozenlist==1.5.0

greenlet==3.1.1

h11==0.14.0

httpcore==1.0.7

httpx==0.28.1

httpx-sse==0.4.0

idna==3.10

jiter==0.8.2

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.13

langchain-community==0.3.13

langchain-core==0.3.28

langchain-openai==0.2.14

langchain-text-splitters==0.3.4

langgraph==0.2.60

langgraph-checkpoint==2.0.9

langgraph-sdk==0.1.48

langsmith==0.2.7

marshmallow==3.23.2

msgpack==1.1.0

multidict==6.1.0

mypy-extensions==1.0.0

numpy==2.2.1

openai==1.58.1

orjson==3.10.13

packaging==24.2

propcache==0.2.1

pydantic==2.10.4

pydantic-settings==2.7.1

pydantic_core==2.27.2

python-dotenv==1.0.1

PyYAML==6.0.2

regex==2024.11.6

requests==2.32.3

requests-toolbelt==1.0.0

sniffio==1.3.1

SQLAlchemy==2.0.36

tenacity==9.0.0

tiktoken==0.8.0

tqdm==4.67.1

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.3.0

yarl==1.18.3

※ pip install python-dotenv langchain-community langchain-openai langgraph 명령을 실행했다.

AI LANGGRAPH LLM PYTHON

icodebroker

[PYTHON/LANGGRAPH] CompiledStateGraph 클래스 : get_state_history 메소드를 사용해 상태 히스토리 구하기

분류

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