VS Code + Cline で ”Devin 風”開発を行う方法⑦ - マルチエージェントループ実行編 #3【LangGraph 版】

C:\miniconda3\shell\condabin\conda-hook.ps1
conda activate base

conda update -n base -c defaults conda
conda create -n langgraphv1.2 python=3.12
conda activate langgraphv1.2

python -m pip install langchain
python -m pip install langgraph
python -m pip install langchain-ollama

# SQLite パッケージ
pip install -U langgraph-checkpoint-sqlite

# watchdoc パッケージ
python -m pip install watchdog

# pydantic パッケージ (型チェックを厳密に行う)
python -m pip install pydantic

# python-dotenv パッケージ
python -m pip install python-dotenv

from langchain_ollama import ChatOllama

model = ChatOllama(
    model="gpt-oss:20b",
)

response = model.invoke("PythonでHello Worldを書いてください")
print(response.content)

from langchain_ollama import ChatOllama

model = ChatOllama(
    model="gpt-oss:20b",
    base_url="http://192.168.11.50:11434",
    temperature=0,
)

from langchain_core.tools import tool
from datetime import datetime

@tool
def get_current_time() -> str:
    """現在の日時を取得します。"""
    return datetime.now().isoformat()

tools = [get_current_time]
model_with_tools = model.bind_tools(tools)

from langgraph.graph import START, END, MessagesState, StateGraph

def call_model(state: MessagesState):
    response = model_with_tools.invoke(
        state["messages"]
    )

    return {
        "messages": [response]
    }

def route(state: MessagesState):
    last_message = state["messages"][-1]

    if last_message.tool_calls:
        return "tools"

    return END

from langgraph.prebuilt import ToolNode

def create_graph():
    builder = StateGraph(MessagesState)
    builder.add_node("agent", call_model)
    builder.add_node("tools", ToolNode(tools))
    builder.add_edge(START,"agent")
    builder.add_conditional_edges(
        "agent",
        route,
        {
            "tools": "tools",
            END: END,
        },
    )
    builder.add_edge("tools","agent")

    return builder.compile()

from langchain_core.messages import HumanMessage

def main():
    graph = create_graph()
    result = graph.invoke(
        {
            "messages": [
                HumanMessage(
                    content="現在の時刻を教えてください。"
                )
            ]
        }
    )
    print(result["messages"][-1].content)

if __name__ == "__main__":
    main()

{
  "version": "0.2.0",
  "configurations": [
    {
      "name": "Python: quick_start",
      "type": "debugpy",
      "request": "launch",
      "program": "${workspaceFolder}/quick_start.py",
      "cwd": "${workspaceFolder}",
      "console": "integratedTerminal",
      "args": [],
      "justMyCode": true
    }
  ]
}

from __future__ import annotations

import argparse
from typing import TypedDict
from langgraph.graph import START, END, StateGraph
from langgraph.types import Command, interrupt
from contextlib import ExitStack
from langgraph.checkpoint.sqlite import SqliteSaver

class State(TypedDict):
    data: str

def task_start(state: State):
    print("タスクを開始します。")

def wait_task(state: State):
    interrupt("waiting")

def task_check(state: State):
    print("タスクをチェックします。")

def create_graph():
    graph = StateGraph(State)
    graph.add_node("task_start", task_start)
    graph.add_node("wait_task", wait_task)
    graph.add_node("task_check", task_check)
    graph.add_edge(START, "task_start")
    graph.add_edge("task_start", "wait_task")
    graph.add_edge("wait_task", "task_check")
    graph.add_edge("task_check", END)

    stack = ExitStack()
    db_path = "langgraph.sqlite"
    checkpointer = stack.enter_context(
        SqliteSaver.from_conn_string(str(db_path))
    )

    return graph.compile(checkpointer=checkpointer), stack

def main() -> None:
    parser = argparse.ArgumentParser()
    sub = parser.add_subparsers(dest="command", required=True)
    sub.add_parser("start")
    sub.add_parser("resume")
    args = parser.parse_args()

    thread_id = "thread-12345"
    config = {"configurable": {"thread_id": thread_id}}
    graph, stack = create_graph()

    if args.command == "start":
        initial_state: State = {
            "data": "1234",
        }
        with stack:
            graph.invoke(initial_state, config=config)
            snapshot = graph.get_state(config)
        if snapshot.interrupts:
            print("\nグラフは正常に中断されました。")
    else:
        resume_state = {"status": "completed"}
        with stack:
            graph.invoke(Command(resume=resume_state), config=config)

if __name__ == "__main__":
    main()

====================
Time: 2026-08-20 16:45:46
Command: npx kanban task list
Output:
{
  "ok": true,
  "workspacePath": "D:\\MAMP\\htdocs\\cline\\YelpCampCleanArch",
  "column": null,
  "tasks": [
    {
      "id": "42642",
      "prompt": "/worker-workflow",
      "column": "in_progress",
      "baseRef": "refactor/login",
      "startInPlanMode": false,
      "autoReviewEnabled": true,
      "autoReviewMode": "commit",
      "createdAt": 1787211934478,
      "updatedAt": 1787211945546,
      "session": null
    },
    {
      "id": "bff90",
      "prompt": "/reviewer-workflow",
      "column": "review",
      "baseRef": "refactor/login",
      "startInPlanMode": false,
      "autoReviewEnabled": true,
      "autoReviewMode": "commit",
      "createdAt": 1787211786322,
      "updatedAt": 1787211921532,
      "session": {
        "state": "awaiting_review",
        "agentId": "cline",
        "pid": null,
        "startedAt": 1787211794702,
        "updatedAt": 1787211920731,
        "lastOutputAt": 1787211920731,
        "reviewReason": "exit",
        "exitCode": null
      }
    }
  ],
  "dependencies": [],
  "count": 2
}

kanban/
└── python/
   └── lang_graph/
      └── data/
         └── requests/
            └── 2ae34.json

{
    "thread_id":  "thread-a3d92f6e1e56",
    "task_id":  "2ae34",
    "task_type":  "WORKER",
    "baseref":  "refactor/login",
    "loop_status":  "LOOP_STARTED",
    "workspace_path":  "D:\\MAMP\\htdocs\\cline\\YelpCampCleanArch",
    "attempt":  0,
    "status":  "submitted",
    "additional_instruction":  "",
    "created_at":  "2026-08-20T07:59:40.508287+00:00",
    "review_attempt":  0
}

{
    "Success":true,
    "Output":[
        {
            "filename":"D:\\MAMP\\htdocs\\cline\\YelpCampCleanArch\\kanban\\python\\lang_graph\\data\\requests\\95ea0.json",
            "id":"2ae34",
            "status":"finished",
            "loopStatus":"WORKER_STARTED"
        }
    ],
    "ExitCode":0
}

python .\kanban\python\lang_graph\watcher.py

git init
cd app
composer install

python .\kanban\python\lang_graph\main.py start --baseref refactor/login