AmazonがNova Act SDKを発表！ブラウザ操作を自動化できるAIエージェント開発キットとは？

from nova_act import NovaAct
import pandas as pd
from concurrent.futures import ThreadPoolExecutor

# 複数のショッピングサイトで価格比較を行う例
sites = ["amazon.com", "walmart.com", "target.com"]
product = "coffee maker"
results = []

def check_price(site):
    with NovaAct(starting_page=f"https://www.{site}") as n:
        n.act(f"search for {product}")
        n.act("click on the first result")
        price = n.extract("find the price of the product")
        return {"site": site, "price": price}

# 並列実行
with ThreadPoolExecutor(max_workers=3) as executor:
    results = list(executor.map(check_price, sites))

# 結果を表示
df = pd.DataFrame(results)
print(df.sort_values("price"))Copy

pip install nova-actCopy

export NOVA_ACT_API_KEY="your_api_key"Copy

from nova_act import NovaAct

# オンラインショップで特定製品の価格をチェックする例
with NovaAct(starting_page="https://www.bestbuy.com") as n:
    # サイトでゲーミングノートPCを検索
    n.act("search for gaming laptop with RTX 4070")
    
    # フィルターを適用して結果を絞り込む
    n.act("filter by price range between $1000 and $1500")
    n.act("sort by customer rating from highest to lowest")
    
    # 最初の3つの結果の情報を取得
    n.act("get the name, price, and rating of the first three laptops")
    
    # 気に入った商品を見つけたら詳細ページへ
    n.act("click on the second laptop in the results")
    
    # 仕様の確認
    n.act("scroll down to specifications section")
    n.act("check if it has at least 16GB RAM and 1TB storage")
    
    # 在庫確認と配送オプションの確認
    n.act("check if the item is in stock for delivery")Copy

from nova_act import NovaAct
n = NovaAct(starting_page="https://www.amazon.com")
n.start()  # ブラウザを起動

# 操作を1つずつ実行
n.act("search for a coffee maker")
# ブラウザ上で動作確認後、次の操作
n.act("select the first result")Copy

from nova_act import NovaAct
from pydantic import BaseModel
from typing import Optional, List

# データモデルを定義
class Product(BaseModel):
    name: str
    price: float
    rating: Optional[float] = None
    reviews: Optional[int] = None

with NovaAct(starting_page="https://www.amazon.com") as n:
    n.act("search for coffee makers")
    # 複数の商品情報を抽出
    products = n.extract(Product, multiple=True)
    
    # 結果を表示
    for product in products:
        print(f"商品名: {product.name}")
        print(f"価格: ${product.price}")
        if product.rating:
            print(f"評価: {product.rating}⭐ ({product.reviews}件のレビュー)")
        print("---")Copy

class Apartment(BaseModel):
    address: str
    price: float
    bedrooms: int
    bathrooms: float
    sqft: Optional[int] = None

# 物件情報を抽出
with NovaAct(starting_page="https://www.realestate.com") as n:
    n.act("search for 2 bedroom apartments in Redwood City")
    apartments = n.extract(Apartment, multiple=True)

# 各物件の最寄り駅までの距離を調査
def add_station_distance(apartment):
    with NovaAct(starting_page="https://maps.google.com") as n:
        n.act(f"get directions from {apartment.address} to nearest train station by bike")
        distance = n.extract("find the biking distance in minutes")
        apartment.station_distance = distance
    return apartment

# 並列処理で効率的に情報収集
from concurrent.futures import ThreadPoolExecutor
with ThreadPoolExecutor(max_workers=5) as executor:
    apts_with_distance = list(executor.map(add_station_distance, apartments))

# 駅からの距離でソート
import pandas as pd
df = pd.DataFrame(apts_with_distance)
df.sort_values("station_distance")Copy

import schedule
import time

def order_salad():
    with NovaAct(user_data_dir="./my_chrome_profile") as n:  # ログイン済みのブラウザプロファイル
        n.act("go to sweetgreen.com")
        n.act("select my usual salad")
        n.act("proceed to checkout")
        n.act("select delivery for tonight")
        n.act("complete the order")
    print("夕食のサラダを注文しました！")

# 毎週火曜日の18時に実行
schedule.every().tuesday.at("18:00").do(order_salad)

while True:
    schedule.run_pending()
    time.sleep(60)Copy

from nova_act import NovaAct
from datetime import datetime, timedelta
from pydantic import BaseModel
from typing import List, Optional

class FlightOption(BaseModel):
    airline: str
    departure_time: str
    arrival_time: str
    duration: str
    stops: int
    price: float

# 旅行日程を設定
departure_date = (datetime.now() + timedelta(days=30)).strftime("%Y-%m-%d")
return_date = (datetime.now() + timedelta(days=37)).strftime("%Y-%m-%d")

# フライト検索
with NovaAct(starting_page="https://www.expedia.com") as n:
    n.act("go to flights section")
    n.act("select round trip")
    n.act("set departure from New York")
    n.act("set destination to Tokyo")
    n.act(f"set departure date to {departure_date}")
    n.act(f"set return date to {return_date}")
    n.act("search for flights")
    
    # フィルターを適用
    n.act("filter for flights with 1 stop or less")
    n.act("sort by price from lowest to highest")
    
    # 上位5件のフライト情報を抽出
    flights = n.extract(FlightOption, multiple=True, limit=5)
    
    # 最安値のフライトを選択
    n.act("select the cheapest flight option")
    
    # ホテルも検索
    n.act("add hotel to this trip")
    n.act("search for hotels in Tokyo city center")
    n.act("filter for 4-star hotels or better")
    n.act("select the hotel with the best reviews in the first 5 results")
    
    # レンタカーはスキップ
    n.act("skip rental car")
    
    # 予約情報の確認
    booking_summary = n.extract("get the total price and booking details")
    print(f"予約内容: {booking_summary}")Copy

from nova_act import NovaAct
import csv
from datetime import datetime

# 投稿予定コンテンツをCSVから読み込む
posts = []
with open('social_media_schedule.csv', 'r') as file:
    csv_reader = csv.DictReader(file)
    for row in csv_reader:
        posts.append(row)

# 今日の日付に対応する投稿を探す
today = datetime.now().strftime("%Y-%m-%d")
todays_posts = [post for post in posts if post['scheduled_date'] == today]

# 各プラットフォームに投稿
for post in todays_posts:
    platform = post['platform']
    content = post['content']
    image_path = post.get('image_path', None)
    
    if platform == 'twitter':
        with NovaAct(user_data_dir="./twitter_profile") as n:
            n.act("go to twitter.com")
            n.act("click on 'Post' button")
            n.act(f"type '{content}'")
            if image_path:
                # Playwrightを使って画像をアップロード
                n.page.locator("input[type='file']").set_input_files(image_path)
            n.act("click 'Post' to publish")
            
    elif platform == 'linkedin':
        with NovaAct(user_data_dir="./linkedin_profile") as n:
            n.act("go to linkedin.com")
            n.act("click on 'Start a post' button")
            n.act(f"type '{content}'")
            if image_path:
                n.act("click on 'Add image' button")
                # Playwrightを使って画像をアップロード
                n.page.locator("input[type='file']").set_input_files(image_path)
            n.act("click 'Post' to publish")
    
    elif platform == 'instagram':
        with NovaAct(user_data_dir="./instagram_profile") as n:
            n.act("go to instagram.com")
            n.act("click on 'Create' button")
            if image_path:
                # Playwrightを使って画像をアップロード
                n.page.locator("input[type='file']").set_input_files(image_path)
            n.act("click 'Next'")
            n.act(f"type '{content}' in the caption field")
            n.act("click 'Share' to publish")
    
    print(f"{platform}に投稿完了: {content[:30]}...")Copy