AIエージェントにおけるフレームワークの動向

# pip install langgraph==0.6.7 langchain-openai==0.3.33 langchain==0.3.27

import random
import uuid
from typing import Annotated, Dict, List

from langchain.chat_models import init_chat_model
from langchain_core.messages import BaseMessage
from langchain_core.tools import tool
from typing_extensions import TypedDict

from langgraph.checkpoint.memory import InMemorySaver
from langgraph.graph import START, StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition

# =========================
# Config
# =========================
MODEL_NAME = "openai:gpt-4o-mini"

# =========================
# State Schema
# =========================
class State(TypedDict):
    """LangGraphのStateを定義。messagesは会話歴を保持する。"""

    messages: Annotated[List[BaseMessage], add_messages]

# =========================
# Tools
# =========================
@tool
def make_reservation_tool() -> str:
    """ダミーの予約コードを生成して返す。"""
    return f"R-{random.randint(1000, 9999)}"

# =========================
# Graph Builder
# =========================
def build_graph():
    """LangGraph のコンパイル済みグラフを生成して返す。"""
    llm = init_chat_model(MODEL_NAME)
    llm_with_tools = llm.bind_tools([make_reservation_tool])

    def chatbot(state: State) -> Dict[str, List[BaseMessage]]:
        ai_msg = llm_with_tools.invoke(state["messages"])
        return {"messages": [ai_msg]}

    graph_builder = StateGraph(State)

    # ノード定義
    graph_builder.add_node("chatbot", chatbot)
    graph_builder.add_node("tools", ToolNode(tools=[make_reservation_tool]))

    # 遷移定義
    graph_builder.add_edge(START, "chatbot")
    # chatbotの出力がツール呼び出しなら "tools" へ、なければ自動でENDへ
    graph_builder.add_conditional_edges("chatbot", tools_condition)
    # ツール実行後は再びchatbotへ戻す（Reason→Act→Observeのループ）
    graph_builder.add_edge("tools", "chatbot")

    # メモリ（チェックポイント）
    checkpointer = InMemorySaver()

    return graph_builder.compile(checkpointer=checkpointer)

# =========================
# CLI Runner
# =========================
def run_cli() -> None:
    """簡易CLIループ。'exit', 'quit', 'q' で終了。"""
    print("AIエージェントとの対話を開始します。終了するには 'exit' と入力してください。")
    print("レストラン予約についてお聞かせください:")

    graph = build_graph()
    thread_id = uuid.uuid4().hex

    while True:
        user_input = input("\\nYOU: ").strip()

        if user_input.lower() in {"exit", "quit", "q"}:
            print("対話を終了します。")
            break

        try:
            response = graph.invoke(
                {"messages": [{"role": "user", "content": user_input}]},
                config={"configurable": {"thread_id": thread_id}},
            )
            # レスポンスから最後のメッセージを取得
            last_message = response["messages"][-1]
            print(f"\\nAI: {last_message.content}")
        except Exception as e:
            print(f"エラーが発生しました: {e}")

if __name__ == "__main__":
    run_cli()

    

# pip install google-adk==1.3.0 litellm==1.73.0

import asyncio
import random

from google.adk.agents import Agent
from google.adk.models.lite_llm import LiteLlm
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types

# =========================
# Config
# =========================
MODEL_NAME = "openai/gpt-4o-mini"
APP_NAME = "ReservationApp"
USER_ID = "user123"
SESSION_ID = "session123"

# =========================
# Tools
# =========================
def make_reservation_tool() -> str:
    """ダミーの予約コードを生成して返す。"""
    return f"R-{random.randint(1000, 9999)}"

# =========================
# Agent
# =========================
reservation_agent = Agent(
    name="reservation_agent",
    model=LiteLlm(model=MODEL_NAME),
    description="Agent to answer questions about restaurant reservations.",
    instruction="I can help you with your reservation inquiries.",
    tools=[make_reservation_tool],
)

# =========================
# Setup / Runner
# =========================
async def create_runner(app_name: str, user_id: str, session_id: str) -> Runner:
    """InMemory セッションを作成して Runner を返す。"""
    session_service = InMemorySessionService()
    await session_service.create_session(app_name=app_name, user_id=user_id, session_id=session_id)
    return Runner(agent=reservation_agent, app_name=app_name, session_service=session_service)

# =========================
# CLI Runner
# =========================
async def run_cli():
    """簡易CLIループ。'exit', 'quit', 'q' で終了。"""
    print("AIエージェントとの対話を開始します。終了するには 'exit' と入力してください。")
    print("レストラン予約についてお聞かせください:")

    runner = await create_runner(APP_NAME, USER_ID, SESSION_ID)

    while True:
        user_input = input("\\nYOU: ").strip()

        if user_input.lower() in {"exit", "quit", "q"}:
            print("対話を終了します。")
            break

        try:
            content = types.Content(role="user", parts=[types.Part(text=user_input)])
            events = runner.run(user_id=USER_ID, session_id=SESSION_ID, new_message=content)
            for event in events:
                if event.is_final_response():
                    final_response = event.content.parts[0].text
                    print(f"\\nAI: {final_response}")
        except Exception as e:
            print(f"\\nエラーが発生しました: {e}")

# メイン処理
if __name__ == "__main__":
    asyncio.run(run_cli())

    

# pip install strands-agents[openai]==0.2.1 strands-agents-builder==0.1.5 strands-agents-tools==0.1.8

import os
import random

from strands import Agent, tool
from strands.models.openai import OpenAIModel

# =========================
# Config
# =========================
MODEL_NAME = "gpt-4o-mini"

# =========================
# Tools
# =========================
@tool
def make_reservation_tool() -> str:
    """ダミーの予約コードを生成して返す。"""
    return f"R-{random.randint(1000, 9999)}"

# =========================
# Agent
# =========================
model = OpenAIModel(
    client_args={
        "api_key": os.getenv("OPENAI_API_KEY"),
    },
    model_id=MODEL_NAME,
)
agent = Agent(model=model, tools=[make_reservation_tool])

# =========================
# CLI Runner
# =========================
def run_cli():
    """簡易CLIループ。'exit', 'quit', 'q' で終了。"""
    print("AIエージェントとの対話を開始します。終了するには 'exit' と入力してください。")
    print("レストラン予約についてお聞かせください:")

    while True:
        user_input = input("\\nYOU: ").strip()

        if user_input.lower() in {"exit", "quit", "q"}:
            print("対話を終了します。")
            break

        try:
            print("\\nAI: ", end="", flush=True)
            agent(user_input)

        except Exception as e:
            print(f"エラーが発生しました: {e}")

if __name__ == "__main__":
    run_cli()

    

# pip install openai-agents==0.2.4

import random
from typing import Optional

from agents import Agent, Runner, SQLiteSession, function_tool, trace

# =========================
# Config
# =========================
MODEL_NAME = "gpt-4o-mini"
USER_ID = "user_123"
THREAD_ID = "thread_123"

# =========================
# Tools
# =========================
@function_tool
def make_reservation_tool() -> str:
    """ダミーの予約コードを生成して返す。"""
    return f"R-{random.randint(1000, 9999)}"

# =========================
# Agent
# =========================
agent = Agent(
    name="reservation_agent",
    instructions="I can help you with your reservation inquiries.",
    model=MODEL_NAME,
    tools=[make_reservation_tool],
)

# =========================
# CLI Runner
# =========================
def run_cli():
    """簡易CLIループ。'exit', 'quit', 'q' で終了。"""
    print("AIエージェントとの対話を開始します。終了するには 'exit' と入力してください。")
    print("レストラン予約についてお聞かせください:")

    session = SQLiteSession(USER_ID)

    while True:
        user_input = input("\\nYOU: ").strip()

        if user_input.lower() in {"exit", "quit", "q"}:
            print("対話を終了します。")
            break

        try:
            with trace(workflow_name="Conversation", group_id=THREAD_ID):
                result = Runner.run_sync(agent, user_input, session=session)
            print(f"\\nAI: {result.final_output}")
        except Exception as e:
            print(f"\\nエラーが発生しました: {e}")

if __name__ == "__main__":
    run_cli()

    

# pip install agno==1.7.2 openai==1.95.1

import random

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools import tool

# =========================
# Config
# =========================
MODEL_NAME = "gpt-4o-mini"

# =========================
# Tools
# =========================
@tool(show_result=True, stop_after_tool_call=False)
def make_reservation_tool() -> str:
    """ダミーの予約コードを生成して返す。"""
    return f"R-{random.randint(1000, 9999)}"

# =========================
# Agent
# =========================
reservation_agent = Agent(
    name="ReservationAgent",
    model=OpenAIChat(id=MODEL_NAME),
    tools=[make_reservation_tool],
    show_tool_calls=True,
    instructions="ユーザーのレストラン予約に関する要望をもとに、適切な提案を行ってください。",
    add_history_to_messages=True,
    num_history_runs=5,
    markdown=True,
)

# =========================
# CLI Runner
# =========================
def run_cli():
    """簡易CLIループ。'exit', 'quit', 'q' で終了。"""
    print("AIエージェントとの対話を開始します。終了するには 'exit' と入力してください。")
    print("レストラン予約についてお聞かせください:")

    while True:
        user_input = input("\\nYOU: ").strip()

        if user_input.lower() in {"exit", "quit", "q"}:
            print("対話を終了します。")
            break

        try:
            print("\\nAI: ", end="")
            reservation_agent.print_response(user_input)

        except Exception as e:
            print(f"\\nエラーが発生しました: {e}")

if __name__ == "__main__":
    run_cli()


    

# pip install ag2[openai]==0.9.9

import random

from autogen import ConversableAgent, LLMConfig

# =========================
# Config
# =========================
API_TYPE = "openai"
MODEL_NAME = "gpt-4o-mini"

llm_config = LLMConfig(api_type=API_TYPE, model=MODEL_NAME)

with llm_config:
    # ユーザーと会話するLLM（人を呼ばない＝NEVER）
    my_agent = ConversableAgent(
        name="helpful_agent",
        system_message=(
            "You are a helpful assistant for restaurant reservations.\\n"
            "If the user asks to make a reservation or needs a reservation code, "
            "call the tool make_reservation_tool and include the returned code."
        ),
        human_input_mode="NEVER",
        max_consecutive_auto_reply=1,
    )

    # 実行専用（ツールだけ動かす、しゃべらない）
    executor = ConversableAgent(
        name="executor",
        human_input_mode="NEVER",
        llm_config=False,
        default_auto_reply="",
    )

    # ツール登録：my_agent に“使えるよ”と知らせ、executor が実行
    @my_agent.register_for_llm(description="Generate and return a dummy reservation code.")
    @executor.register_for_execution()
    def make_reservation_tool() -> str:
        """ダミーの予約コードを生成して返す。"""
        return f"R-{random.randint(1000, 9999)}"

# =========================
# CLI Runner
# =========================
def run_cli() -> None:
    """簡易CLIループ。'exit', 'quit', 'q' で終了。"""
    print("AIエージェントとの対話を開始します。終了するには 'exit' と入力してください。")
    print("レストラン予約についてお聞かせください:")

    while True:
        user_input = input("\\nYOU: ").strip()

        if user_input.lower() in {"exit", "quit", "q"}:
            print("対話を終了します。")
            break

        try:
            # 人の入力を毎ターン渡す（内部で必要ならツールを呼ぶ）
            response = executor.initiate_chat(
                recipient=my_agent,
                message=user_input,
                max_turns=3,
                clear_history=False,  # 履歴を保持して継続
                summary_method="last_msg",
            )

            # helpful_agent の最新発話だけ拾って表示
            msg = None
            for m in reversed(response.chat_history or []):
                if m.get("name") == "helpful_agent" and m.get("content"):
                    msg = m["content"]
                    break
            print(f"\\nAI: {msg or response.summary or '(no response)'}")
        except Exception as e:
            print(f"エラーが発生しました: {e}")

if __name__ == "__main__":
    run_cli()

    

# pip install "pydantic-ai-slim[openai]"==0.7.4

import random

from pydantic_ai import Agent, RunContext
from pydantic_ai.messages import ModelMessage

# =========================
# Config
# =========================
MODEL_NAME = "openai:gpt-4o-mini"

# =========================
# Agent
# =========================
reservation_agent = Agent(
    MODEL_NAME,
    system_prompt=(
        "あなたはレストラン予約を手伝うアシスタントです。"
        "ユーザーの要望（日付・時間・人数・ジャンル・特別要望）を確認し、"
        "必要なら予約コード発行ツールを呼び出して応答してください。"
    ),
)

# =========================
# Tools
# =========================
@reservation_agent.tool
def make_reservation_tool(ctx: RunContext) -> str:
    """ダミーの予約コードを生成して返す。"""
    return f"R-{random.randint(1000, 9999)}"

# =========================
# CLI
# =========================
def run_cli() -> None:
    print("AIエージェントとの対話を開始します。終了するには 'exit' と入力してください。")
    print("レストラン予約についてお聞かせください:")

    history: list[ModelMessage] = []  # 会話履歴を保持

    while True:
        user_input = input("\\nあなた: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("対話を終了します。")
            break

        try:
            result = reservation_agent.run_sync(user_input, message_history=history)
            print(f"\\nAI: {result.output}")

            # この会話ターンで増えた分だけ追記
            history.extend(result.new_messages())
        except Exception as e:
            print(f"\\nエラーが発生しました: {e}")

if __name__ == "__main__":
    run_cli()