[PYTHON/LANGGRAPH] 커스텀 상태를 사용해 동작하기

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

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

runnableBinding = chatOpenAI.bind_tools([tavilySearchResults, RequestAssistance])

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 humanNode(state : State):
    newMessageList = []
    if not isinstance(state["messages"][-1], ToolMessage):
        # 일반적으로 사용자는 인터럽트 중에 상태를 업데이트했을 것이다.
        # 그렇지 않은 경우 LLM이 계속 진행되도록 플레이스홀더 ToolMessage를 포함한다하.
        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("human_node"  , humanNode)
stateGraph.add_node("tools"       , ToolNode(tools = [tavilySearchResults]))

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

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

memorySaver = MemorySaver()

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

userInput              = "I need some expert guidance for building this AI agent. Could you request assistance for me?"
configurableDictionary = {"configurable" : {"thread_id" : "1"}}

generator1 = compiledStateGraph.stream({"messages" : [("user", userInput)]}, configurableDictionary, stream_mode = "values")

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

stateSnapshot = compiledStateGraph.get_state(configurableDictionary)

aiMessage = stateSnapshot.values["messages"][-1]

toolMessage = createToolMessage
(
    "We, the experts are here to help! We'd recommend you check out LangGraph to build your agent. It's much more reliable and extensible than simple autonomous agents.",
    aiMessage
)

compiledStateGraph.update_state(configurableDictionary, {"messages" : [toolMessage]})

generator2 = compiledStateGraph.stream(None, configurableDictionary, stream_mode = "values")

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

"""
{
    'messages' : [
        HumanMessage(
            content           = 'I need some expert guidance for building this AI agent. Could you request assistance for me?',
            additional_kwargs = {},
            response_metadata = {},
            id                = '597c2770-d5cd-4460-80f3-2cbfe2e4affd'
        )
    ]
}
{
    'messages' : [
        HumanMessage(
            content           = 'I need some expert guidance for building this AI agent. Could you request assistance for me?',
            additional_kwargs = {},
            response_metadata = {},
            id                = '597c2770-d5cd-4460-80f3-2cbfe2e4affd'
        ),
        AIMessage(
            content           = '',
            additional_kwargs = {
                'tool_calls' : [
                    {
                        'id'       : 'call_TnzKa6e4uqsMi99PVEADbh6p',
                        'function' : {
                            'arguments' : '{"request":"I am looking for expert guidance on building an AI agent. I need advice on the best practices, technologies, and frameworks to use for developing an effective AI agent."}',
                            'name'      : 'RequestAssistance'
                        },
                        'type'     : 'function'
                    }
                ],
                'refusal' : None
            },
            response_metadata = {
                'token_usage' : {
                    'completion_tokens'         : 48,
                    'prompt_tokens'             : 160,
                    'total_tokens'              : 208,
                    '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-94370388-2485-44e2-a42e-f5b39abfd707-0',
            tool_calls= [
                {
                    'name' : 'RequestAssistance',
                    'args' : {'request' : 'I am looking for expert guidance on building an AI agent. I need advice on the best practices, technologies, and frameworks to use for developing an effective AI agent.'},
                    'id'   : 'call_TnzKa6e4uqsMi99PVEADbh6p',
                    'type' : 'tool_call'
                }
            ],
            usage_metadata = {
                'input_tokens'         : 160,
                'output_tokens'        : 48,
                'total_tokens'         : 208,
                'input_token_details'  : {'audio' : 0, 'cache_read' : 0},
                'output_token_details' : {'audio' : 0, 'reasoning'  : 0}
            }
        )
    ],
    'askHuman' : True
}
--------------------------------------------------
{
    'messages' : [
        HumanMessage(
            content           = 'I need some expert guidance for building this AI agent. Could you request assistance for me?',
            additional_kwargs = {},
            response_metadata = {},
            id='597c2770-d5cd-4460-80f3-2cbfe2e4affd'
        ),
        AIMessage(
            content           = '',
            additional_kwargs = {
                'tool_calls' : [
                    {
                        'id'       : 'call_TnzKa6e4uqsMi99PVEADbh6p',
                        'function' : {
                            'arguments' : '{"request":"I am looking for expert guidance on building an AI agent. I need advice on the best practices, technologies, and frameworks to use for developing an effective AI agent."}',
                            'name'      : 'RequestAssistance'
                        },
                        'type'     : 'function'
                    }
                ],
                'refusal' : None
            },
            response_metadata = {
                'token_usage' : {
                    'completion_tokens'         : 48,
                    'prompt_tokens'             : 160,
                    'total_tokens'              : 208,
                    '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-94370388-2485-44e2-a42e-f5b39abfd707-0',
            tool_calls = [
                {
                    'name' : 'RequestAssistance',
                    'args' : {'request' : 'I am looking for expert guidance on building an AI agent. I need advice on the best practices, technologies, and frameworks to use for developing an effective AI agent.'},
                    'id'   : 'call_TnzKa6e4uqsMi99PVEADbh6p',
                    'type' : 'tool_call'
                }
            ],
            usage_metadata = {
                'input_tokens'         : 160,
                'output_tokens'        : 48,
                'total_tokens'         : 208,
                'input_token_details'  : {'audio' : 0, 'cache_read' : 0},
                'output_token_details' : {'audio' : 0, 'reasoning'  : 0}
            }
        ),
        ToolMessage(
            content      = "We, the experts are here to help! We'd recommend you check out LangGraph to build your agent. It's much more reliable and extensible than simple autonomous agents.",
            id           = '90011801-5705-46ab-89b2-9cfcfd124f87',
            tool_call_id = 'call_TnzKa6e4uqsMi99PVEADbh6p'
        )
    ],
    'askHuman' : True
}
{
    'messages' : [
        HumanMessage(
            content           = 'I need some expert guidance for building this AI agent. Could you request assistance for me?',
            additional_kwargs = {},
            response_metadata = {},
            id                = '597c2770-d5cd-4460-80f3-2cbfe2e4affd'
        ),
        AIMessage(
            content           = '',
            additional_kwargs = {
                'tool_calls' : [
                    {
                        'id'       : 'call_TnzKa6e4uqsMi99PVEADbh6p',
                        'function' : {
                            'arguments' : '{"request":"I am looking for expert guidance on building an AI agent. I need advice on the best practices, technologies, and frameworks to use for developing an effective AI agent."}',
                            'name'      : 'RequestAssistance'
                        },
                        'type'     : 'function'
                    }
                ],
                'refusal' : None
            },
            response_metadata = {
                'token_usage' : {
                    'completion_tokens'        : 48,
                    'prompt_tokens'             : 160,
                    'total_tokens'              : 208,
                    '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-94370388-2485-44e2-a42e-f5b39abfd707-0',
            tool_calls = [
                {
                    'name' : 'RequestAssistance',
                    'args' : {'request' : 'I am looking for expert guidance on building an AI agent. I need advice on the best practices, technologies, and frameworks to use for developing an effective AI agent.'},
                    'id'   : 'call_TnzKa6e4uqsMi99PVEADbh6p',
                    'type' : 'tool_call'
                }
            ],
            usage_metadata = {
                'input_tokens'         : 160,
                'output_tokens'        : 48,
                'total_tokens'         : 208,
                'input_token_details'  : {'audio' : 0, 'cache_read' : 0},
                'output_token_details' : {'audio' : 0, 'reasoning'  : 0}
            }
        ),
        ToolMessage(
            content      = "We, the experts are here to help! We'd recommend you check out LangGraph to build your agent. It's much more reliable and extensible than simple autonomous agents.",
            id           = '90011801-5705-46ab-89b2-9cfcfd124f87',
            tool_call_id = 'call_TnzKa6e4uqsMi99PVEADbh6p'
        )
    ],
    'askHuman' : False
}
{
    'messages' : [
        HumanMessage(
            content           = 'I need some expert guidance for building this AI agent. Could you request assistance for me?',
            additional_kwargs = {},
            response_metadata = {},
            id                = '597c2770-d5cd-4460-80f3-2cbfe2e4affd'
        ),
        AIMessage(
            content           = '',
            additional_kwargs = {
                'tool_calls' : [
                    {
                        'id'       : 'call_TnzKa6e4uqsMi99PVEADbh6p',
                        'function' : {
                            'arguments' : '{"request":"I am looking for expert guidance on building an AI agent. I need advice on the best practices, technologies, and frameworks to use for developing an effective AI agent."}',
                            'name'      : 'RequestAssistance'
                        },
                        'type'     : 'function'
                    }
                ],
                'refusal' : None
            },
            response_metadata = {
                'token_usage' : {
                    'completion_tokens'         : 48,
                    'prompt_tokens'             : 160,
                    'total_tokens'              : 208,
                    '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-94370388-2485-44e2-a42e-f5b39abfd707-0',
            tool_calls = [
                {
                    'name' : 'RequestAssistance',
                    'args' : {'request' : 'I am looking for expert guidance on building an AI agent. I need advice on the best practices, technologies, and frameworks to use for developing an effective AI agent.'},\
                    'id'   : 'call_TnzKa6e4uqsMi99PVEADbh6p',
                    'type' : 'tool_call'
                }
            ],
            usage_metadata = {
                'input_tokens'         : 160,
                'output_tokens'        : 48,
                'total_tokens'         : 208,
                'input_token_details'  : {'audio' : 0, 'cache_read' : 0},
                'output_token_details' : {'audio' : 0, 'reasoning'  : 0}
            }
        ),
        ToolMessage(
            content      = "We, the experts are here to help! We'd recommend you check out LangGraph to build your agent. It's much more reliable and extensible than simple autonomous agents.",
            id           = '90011801-5705-46ab-89b2-9cfcfd124f87',
            tool_call_id = 'call_TnzKa6e4uqsMi99PVEADbh6p'
        ),
        AIMessage(
            content           = "The experts recommend using LangGraph to build your AI agent. It's considered more reliable and extensible compared to simple autonomous agents. If you have any specific questions about using LangGraph or need further assistance, feel free to ask!",
            additional_kwargs = {'refusal' : None},
            response_metadata = {
                'token_usage' : {
                    'completion_tokens'         : 47,
                    'prompt_tokens'             : 249,
                    'total_tokens'              : 296,
                    '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-91719ca7-a533-4ccf-955e-1c65a240140f-0',
            usage_metadata = {
                'input_tokens'         : 249,
                'output_tokens'        : 47,
                'total_tokens'         : 296,
                'input_token_details'  : {'audio' : 0, 'cache_read' : 0},
                'output_token_details' : {'audio' : 0, 'reasoning'  : 0}
            }
        )
    ],
    'askHuman' : False
}
--------------------------------------------------
"""

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from dotenv import load_dotenv

from langchain_openai import ChatOpenAI

from langchain_community.tools.tavily_search import TavilySearchResults

from pydantic import BaseModel

from typing_extensions import TypedDict

from typing import Annotated

from langgraph.graph.message import add_messages

from langchain_core.messages import AIMessage

from langchain_core.messages import ToolMessage

from langgraph.prebuilt import tools_condition

from langgraph.graph import StateGraph

from langgraph.prebuilt import ToolNode

from langgraph.graph import START

from langgraph.graph import END

from langgraph.checkpoint.memory import MemorySaver

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

runnableBinding = chatOpenAI.bind_tools([tavilySearchResults, RequestAssistance])

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 humanNode(state : State):

newMessageList = []

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

# 일반적으로 사용자는 인터럽트 중에 상태를 업데이트했을 것이다.

# 그렇지 않은 경우 LLM이 계속 진행되도록 플레이스홀더 ToolMessage를 포함한다하.

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("human_node" , humanNode)

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

stateGraph.add_edge(START , "chatbot_node")

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

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

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

memorySaver = MemorySaver()

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

userInput = "I need some expert guidance for building this AI agent. Could you request assistance for me?"

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

generator1 = compiledStateGraph.stream({"messages" : [("user", userInput)]}, configurableDictionary, stream_mode = "values")

for addableUpdatesDict in generator1:

if "messages" in addableUpdatesDict:

print(addableUpdatesDict)

print("-" *50)

stateSnapshot = compiledStateGraph.get_state(configurableDictionary)

aiMessage = stateSnapshot.values["messages"][-1]

toolMessage = createToolMessage

(

"We, the experts are here to help! We'd recommend you check out LangGraph to build your agent. It's much more reliable and extensible than simple autonomous agents.",

aiMessage

)

compiledStateGraph.update_state(configurableDictionary, {"messages" : [toolMessage]})

generator2 = compiledStateGraph.stream(None, configurableDictionary, stream_mode = "values")

for addableUpdatesDict in generator2:

if "messages" in addableUpdatesDict:

print(addableUpdatesDict)

print("-" *50)

"""

{

'messages' : [

HumanMessage(

content = 'I need some expert guidance for building this AI agent. Could you request assistance for me?',

additional_kwargs = {},

response_metadata = {},

id = '597c2770-d5cd-4460-80f3-2cbfe2e4affd'

)

]

}

{

'messages' : [

HumanMessage(

content = 'I need some expert guidance for building this AI agent. Could you request assistance for me?',

additional_kwargs = {},

response_metadata = {},

id = '597c2770-d5cd-4460-80f3-2cbfe2e4affd'

AIMessage(

content = '',

additional_kwargs = {

'tool_calls' : [

{

'id' : 'call_TnzKa6e4uqsMi99PVEADbh6p',

'function' : {

'arguments' : '{"request":"I am looking for expert guidance on building an AI agent. I need advice on the best practices, technologies, and frameworks to use for developing an effective AI agent."}',

'name' : 'RequestAssistance'

'type' : 'function'

}

'refusal' : None

response_metadata = {

'token_usage' : {

'completion_tokens' : 48,

'prompt_tokens' : 160,

'total_tokens' : 208,

'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-94370388-2485-44e2-a42e-f5b39abfd707-0',

tool_calls= [

{

'name' : 'RequestAssistance',

'args' : {'request' : 'I am looking for expert guidance on building an AI agent. I need advice on the best practices, technologies, and frameworks to use for developing an effective AI agent.'},

'id' : 'call_TnzKa6e4uqsMi99PVEADbh6p',

'type' : 'tool_call'

}

usage_metadata = {

'input_tokens' : 160,

'output_tokens' : 48,

'total_tokens' : 208,

'input_token_details' : {'audio' : 0, 'cache_read' : 0},

'output_token_details' : {'audio' : 0, 'reasoning' : 0}

}

)

'askHuman' : True

}

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

{

'messages' : [

HumanMessage(

content = 'I need some expert guidance for building this AI agent. Could you request assistance for me?',

additional_kwargs = {},

response_metadata = {},

id='597c2770-d5cd-4460-80f3-2cbfe2e4affd'

AIMessage(

content = '',

additional_kwargs = {

'tool_calls' : [

{

'id' : 'call_TnzKa6e4uqsMi99PVEADbh6p',

'function' : {

'arguments' : '{"request":"I am looking for expert guidance on building an AI agent. I need advice on the best practices, technologies, and frameworks to use for developing an effective AI agent."}',

'name' : 'RequestAssistance'

'type' : 'function'

}

'refusal' : None

response_metadata = {

'token_usage' : {

'completion_tokens' : 48,

'prompt_tokens' : 160,

'total_tokens' : 208,

'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-94370388-2485-44e2-a42e-f5b39abfd707-0',

tool_calls = [

{

'name' : 'RequestAssistance',

'args' : {'request' : 'I am looking for expert guidance on building an AI agent. I need advice on the best practices, technologies, and frameworks to use for developing an effective AI agent.'},

'id' : 'call_TnzKa6e4uqsMi99PVEADbh6p',

'type' : 'tool_call'

}

usage_metadata = {

'input_tokens' : 160,

'output_tokens' : 48,

'total_tokens' : 208,

'input_token_details' : {'audio' : 0, 'cache_read' : 0},

'output_token_details' : {'audio' : 0, 'reasoning' : 0}

}

ToolMessage(

content = "We, the experts are here to help! We'd recommend you check out LangGraph to build your agent. It's much more reliable and extensible than simple autonomous agents.",

id = '90011801-5705-46ab-89b2-9cfcfd124f87',

tool_call_id = 'call_TnzKa6e4uqsMi99PVEADbh6p'

)

'askHuman' : True

}

{

'messages' : [

HumanMessage(

content = 'I need some expert guidance for building this AI agent. Could you request assistance for me?',

additional_kwargs = {},

response_metadata = {},

id = '597c2770-d5cd-4460-80f3-2cbfe2e4affd'

AIMessage(

content = '',

additional_kwargs = {

'tool_calls' : [

{

'id' : 'call_TnzKa6e4uqsMi99PVEADbh6p',

'function' : {

'arguments' : '{"request":"I am looking for expert guidance on building an AI agent. I need advice on the best practices, technologies, and frameworks to use for developing an effective AI agent."}',

'name' : 'RequestAssistance'

'type' : 'function'

}

'refusal' : None

response_metadata = {

'token_usage' : {

'completion_tokens' : 48,

'prompt_tokens' : 160,

'total_tokens' : 208,

'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-94370388-2485-44e2-a42e-f5b39abfd707-0',

tool_calls = [

{

'name' : 'RequestAssistance',

'args' : {'request' : 'I am looking for expert guidance on building an AI agent. I need advice on the best practices, technologies, and frameworks to use for developing an effective AI agent.'},

'id' : 'call_TnzKa6e4uqsMi99PVEADbh6p',

'type' : 'tool_call'

}

usage_metadata = {

'input_tokens' : 160,

'output_tokens' : 48,

'total_tokens' : 208,

'input_token_details' : {'audio' : 0, 'cache_read' : 0},

'output_token_details' : {'audio' : 0, 'reasoning' : 0}

}

ToolMessage(

content = "We, the experts are here to help! We'd recommend you check out LangGraph to build your agent. It's much more reliable and extensible than simple autonomous agents.",

id = '90011801-5705-46ab-89b2-9cfcfd124f87',

tool_call_id = 'call_TnzKa6e4uqsMi99PVEADbh6p'

)

'askHuman' : False

}

{

'messages' : [

HumanMessage(

content = 'I need some expert guidance for building this AI agent. Could you request assistance for me?',

additional_kwargs = {},

response_metadata = {},

id = '597c2770-d5cd-4460-80f3-2cbfe2e4affd'

AIMessage(

content = '',

additional_kwargs = {

'tool_calls' : [

{

'id' : 'call_TnzKa6e4uqsMi99PVEADbh6p',

'function' : {

'arguments' : '{"request":"I am looking for expert guidance on building an AI agent. I need advice on the best practices, technologies, and frameworks to use for developing an effective AI agent."}',

'name' : 'RequestAssistance'

'type' : 'function'

}

'refusal' : None

response_metadata = {

'token_usage' : {

'completion_tokens' : 48,

'prompt_tokens' : 160,

'total_tokens' : 208,

'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-94370388-2485-44e2-a42e-f5b39abfd707-0',

tool_calls = [

{

'name' : 'RequestAssistance',

'args' : {'request' : 'I am looking for expert guidance on building an AI agent. I need advice on the best practices, technologies, and frameworks to use for developing an effective AI agent.'},\

'id' : 'call_TnzKa6e4uqsMi99PVEADbh6p',

'type' : 'tool_call'

}

usage_metadata = {

'input_tokens' : 160,

'output_tokens' : 48,

'total_tokens' : 208,

'input_token_details' : {'audio' : 0, 'cache_read' : 0},

'output_token_details' : {'audio' : 0, 'reasoning' : 0}

}

ToolMessage(

content = "We, the experts are here to help! We'd recommend you check out LangGraph to build your agent. It's much more reliable and extensible than simple autonomous agents.",

id = '90011801-5705-46ab-89b2-9cfcfd124f87',

tool_call_id = 'call_TnzKa6e4uqsMi99PVEADbh6p'

AIMessage(

content = "The experts recommend using LangGraph to build your AI agent. It's considered more reliable and extensible compared to simple autonomous agents. If you have any specific questions about using LangGraph or need further assistance, feel free to ask!",

additional_kwargs = {'refusal' : None},

response_metadata = {

'token_usage' : {

'completion_tokens' : 47,

'prompt_tokens' : 249,

'total_tokens' : 296,

'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-91719ca7-a533-4ccf-955e-1c65a240140f-0',

usage_metadata = {

'input_tokens' : 249,

'output_tokens' : 47,

'total_tokens' : 296,

'input_token_details' : {'audio' : 0, 'cache_read' : 0},

'output_token_details' : {'audio' : 0, 'reasoning' : 0}

}

)

'askHuman' : False

}

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

"""

▶ 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] 커스텀 상태를 사용해 동작하기

분류

가장 많이 읽힌 글

보관함