How to create a Multi-Multi-Ai-Ai-Ai-Ai-Ai-Ai-Ai-Ai-Ai-Ai-User and Gemini
In this lesson, we show the complete implementation, advanced for Girl Ai agent, which includes Gemini API to the power of consultation and automatic. By combining the default notte skills by exiting the Pydantic Skills using the Pydantic Models, it shows how AI Wei can do research products, monitoring the media, assessing markets, and more. The study is intended as a practical, crafts, duties, and the traveling, and the travel of the developers can obtain the Automation Automation Automation Automerice such as e-commerce. Look Full codes here.
!pip install notte python-dotenv pydantic google-generativeai requests beautifulsoup4
!patchright install --with-deps chromium
import os
import json
import time
from typing import List, Optional
from pydantic import BaseModel
import google.generativeai as genai
from dotenv import load_dotenv
GEMINI_API_KEY = "USE YOUR OWN API KEY HERE"
os.environ['GEMINI_API_KEY'] = GEMINI_API_KEY
genai.configure(api_key=GEMINI_API_KEY)
import notteWe start by installing all the required dependence, including Nottotte, Gemini, and supporting libraries, and prepares our Gemini API Gemini API for authentication. After setting up the environment, we import nototte to start building and using our web agent and launched the seams. Look Full codes here.
class ProductInfo(BaseModel):
name: str
price: str
rating: Optional[float]
availability: str
description: str
class NewsArticle(BaseModel):
title: str
summary: str
url: str
date: str
source: str
class SocialMediaPost(BaseModel):
content: str
author: str
likes: int
timestamp: str
platform: str
class SearchResult(BaseModel):
query: str
results: List[dict]
total_found: intIt describes the Psydic models that allow us to catch and confirm the data consistently. With ProductinFor, NewsRarkle, SocialMediate, and Searchresults, we ensure that the results of the outgoing Agu Agent, the well-organized details of products, social media, and search results. Look Full codes here.
class AdvancedNotteAgent:
def __init__(self, headless=True, max_steps=20):
self.headless = headless
self.max_steps = max_steps
self.session = None
self.agent = None
def __enter__(self):
self.session = notte.Session(headless=self.headless)
self.session.__enter__()
self.agent = notte.Agent(
session=self.session,
reasoning_model="gemini/gemini-2.5-flash",
max_steps=self.max_steps
)
return self
def __exit__(self, exc_type, exc_val, exc_tb):
if self.session:
self.session.__exit__(exc_type, exc_val, exc_tb)
def research_product(self, product_name: str, website: str = "amazon.com") -> ProductInfo:
"""Research a product and extract structured information"""
task = f"Go to {website}, search for '{product_name}', click on the first relevant product, and extract detailed product information including name, price, rating, availability, and description."
response = self.agent.run(
task=task,
response_format=ProductInfo,
url=f"
)
return response.answer
def news_aggregator(self, topic: str, num_articles: int = 3) -> List[NewsArticle]:
"""Aggregate news articles on a specific topic"""
task = f"Search for recent news about '{topic}', find {num_articles} relevant articles, and extract title, summary, URL, date, and source for each."
response = self.agent.run(
task=task,
url="
response_format=List[NewsArticle]
)
return response.answer
def social_media_monitor(self, hashtag: str, platform: str = "twitter") -> List[SocialMediaPost]:
"""Monitor social media for specific hashtags"""
if platform.lower() == "twitter":
url = "
elif platform.lower() == "reddit":
url = "
else:
url = f"
task = f"Go to {platform}, search for posts with hashtag '{hashtag}', and extract content, author, engagement metrics, and timestamps from the top 5 posts."
response = self.agent.run(
task=task,
url=url,
response_format=List[SocialMediaPost]
)
return response.answer
def competitive_analysis(self, company: str, competitors: List[str]) -> dict:
"""Perform competitive analysis by gathering pricing and feature data"""
results = {}
for competitor in [company] + competitors:
task = f"Go to {competitor}'s website, find their pricing page or main product page, and extract key features, pricing tiers, and unique selling points."
try:
response = self.agent.run(
task=task,
url=f"
)
results[competitor] = response.answer
time.sleep(2)
except Exception as e:
results[competitor] = f"Error: {str(e)}"
return results
def job_market_scanner(self, job_title: str, location: str = "remote") -> List[dict]:
"""Scan job market for opportunities"""
task = f"Search for '{job_title}' jobs in '{location}', extract job titles, companies, salary ranges, and application URLs from the first 10 results."
response = self.agent.run(
task=task,
url="
)
return response.answer
def price_comparison(self, product: str, websites: List[str]) -> dict:
"""Compare prices across multiple websites"""
price_data = {}
for site in websites:
task = f"Search for '{product}' on this website and find the best price, including any discounts or special offers."
try:
response = self.agent.run(
task=task,
url=f"
)
price_data[site] = response.answer
time.sleep(1)
except Exception as e:
price_data[site] = f"Error: {str(e)}"
return price_data
def content_research(self, topic: str, content_type: str = "blog") -> dict:
"""Research content ideas and trending topics"""
if content_type == "blog":
url = "
task = f"Search for '{topic}' articles, analyze trending content, and identify popular themes, engagement patterns, and content gaps."
elif content_type == "video":
url = "
task = f"Search for '{topic}' videos, analyze view counts, titles, and descriptions to identify trending formats and popular angles."
else:
url = "
task = f"Search for '{topic}' content across the web and analyze trending discussions and popular formats."
response = self.agent.run(task=task, url=url)
return {"topic": topic, "insights": response.answer, "platform": content_type}We wrap not in the best techntnottegent situation that sets no headache and a powerful-language model, which allows us to change multi-step duties reliably. We have included high quality methods, including product research, news integration, audio, scanning, job searches, pricing, and productive, planned. This allows us to flow the actual webpage function while storing the interface is simple and variable. Look Full codes here.
def demo_ecommerce_research():
"""Demo: E-commerce product research and comparison"""
print("🛍️ E-commerce Research Demo")
print("=" * 50)
with AdvancedNotteAgent(headless=True) as agent:
product = agent.research_product("wireless earbuds", "amazon.com")
print(f"Product Research Results:")
print(f"Name: {product.name}")
print(f"Price: {product.price}")
print(f"Rating: {product.rating}")
print(f"Availability: {product.availability}")
print(f"Description: {product.description[:100]}...")
print("n💰 Price Comparison:")
websites = ["amazon.com", "ebay.com", "walmart.com"]
prices = agent.price_comparison("wireless earbuds", websites)
for site, data in prices.items():
print(f"{site}: {data}")
def demo_news_intelligence():
"""Demo: News aggregation and analysis"""
print("📰 News Intelligence Demo")
print("=" * 50)
with AdvancedNotteAgent() as agent:
articles = agent.news_aggregator("artificial intelligence", 3)
for i, article in enumerate(articles, 1):
print(f"nArticle {i}:")
print(f"Title: {article.title}")
print(f"Source: {article.source}")
print(f"Summary: {article.summary}")
print(f"URL: {article.url}")
def demo_social_listening():
"""Demo: Social media monitoring and sentiment analysis"""
print("👂 Social Media Listening Demo")
print("=" * 50)
with AdvancedNotteAgent() as agent:
posts = agent.social_media_monitor("#AI", "reddit")
for i, post in enumerate(posts, 1):
print(f"nPost {i}:")
print(f"Author: {post.author}")
print(f"Content: {post.content[:100]}...")
print(f"Engagement: {post.likes} likes")
print(f"Platform: {post.platform}")
def demo_market_intelligence():
"""Demo: Competitive analysis and market research"""
print("📊 Market Intelligence Demo")
print("=" * 50)
with AdvancedNotteAgent() as agent:
company = "openai"
competitors = ["anthropic", "google"]
analysis = agent.competitive_analysis(company, competitors)
for comp, data in analysis.items():
print(f"n{comp.upper()}:")
print(f"Analysis: {str(data)[:200]}...")
def demo_job_market_analysis():
"""Demo: Job market scanning and analysis"""
print("💼 Job Market Analysis Demo")
print("=" * 50)
with AdvancedNotteAgent() as agent:
jobs = agent.job_market_scanner("python developer", "san francisco")
print(f"Found {len(jobs)} job opportunities:")
for job in jobs[:3]:
print(f"- {job}")
def demo_content_strategy():
"""Demo: Content research and trend analysis"""
print("✍️ Content Strategy Demo")
print("=" * 50)
with AdvancedNotteAgent() as agent:
blog_research = agent.content_research("machine learning", "blog")
video_research = agent.content_research("machine learning", "video")
print("Blog Content Insights:")
print(blog_research["insights"][:300] + "...")
print("nVideo Content Insights:")
print(video_research["insights"][:300] + "...")We use Demos Suite indicating the original Web Retomation at the end, including research products and compare prices, to integrate new stories, and to view social conversation. We also conduct competitive SCAs, analyze work market, and follow the blog / video markets, allowing a formal, appropriate for use in each work. Look Full codes here.
class WorkflowManager:
def __init__(self):
self.agents = []
self.results = {}
def add_agent_task(self, name: str, task_func, *args, **kwargs):
"""Add an agent task to the workflow"""
self.agents.append({
'name': name,
'func': task_func,
'args': args,
'kwargs': kwargs
})
def execute_workflow(self, parallel=False):
"""Execute all agent tasks in the workflow"""
print("🚀 Executing Multi-Agent Workflow")
print("=" * 50)
for agent_task in self.agents:
name = agent_task['name']
func = agent_task['func']
args = agent_task['args']
kwargs = agent_task['kwargs']
print(f"n🤖 Executing {name}...")
try:
result = func(*args, **kwargs)
self.results[name] = result
print(f"✅ {name} completed successfully")
except Exception as e:
self.results[name] = f"Error: {str(e)}"
print(f"❌ {name} failed: {str(e)}")
if not parallel:
time.sleep(2)
return self.results
def market_research_workflow(company_name: str, product_category: str):
"""Complete market research workflow"""
workflow = WorkflowManager()
workflow.add_agent_task(
"Product Research",
lambda: research_trending_products(product_category)
)
workflow.add_agent_task(
"Competitive Analysis",
lambda: analyze_competitors(company_name, product_category)
)
workflow.add_agent_task(
"Social Sentiment",
lambda: monitor_brand_sentiment(company_name)
)
return workflow.execute_workflow()
def research_trending_products(category: str):
"""Research trending products in a category"""
with AdvancedNotteAgent(headless=True) as agent:
task = f"Research trending {category} products, find top 5 products with prices, ratings, and key features."
response = agent.agent.run(
task=task,
url="
)
return response.answer
def analyze_competitors(company: str, category: str):
"""Analyze competitors in the market"""
with AdvancedNotteAgent(headless=True) as agent:
task = f"Research {company} competitors in {category}, compare pricing strategies, features, and market positioning."
response = agent.agent.run(
task=task,
url="
)
return response.answer
def monitor_brand_sentiment(brand: str):
"""Monitor brand sentiment across platforms"""
with AdvancedNotteAgent(headless=True) as agent:
task = f"Search for recent mentions of {brand} on social media and news, analyze sentiment and key themes."
response = agent.agent.run(
task=task,
url="
)
return response.answerWe design a work workager who touches many Ai Agent jobs in one pipe orchestreat. By adding modular duties such as product research, competitive analysis, sensitivity, can make the total efficiency of market research in respect (or similarity). This changes some demos have a combined integrated system that gives a complete understanding of making decisions about real real decisions. Look Full codes here.
def main():
"""Main function to run all demos"""
print("🚀 Advanced Notte AI Agent Tutorial")
print("=" * 60)
print("Note: Make sure to set your GEMINI_API_KEY above!")
print("Get your free API key at:
print("=" * 60)
if GEMINI_API_KEY == "YOUR_GEMINI_API_KEY":
print("❌ Please set your GEMINI_API_KEY in the code above!")
return
try:
print("n1. E-commerce Research Demo")
demo_ecommerce_research()
print("n2. News Intelligence Demo")
demo_news_intelligence()
print("n3. Social Media Listening Demo")
demo_social_listening()
print("n4. Market Intelligence Demo")
demo_market_intelligence()
print("n5. Job Market Analysis Demo")
demo_job_market_analysis()
print("n6. Content Strategy Demo")
demo_content_strategy()
print("n7. Multi-Agent Workflow Demo")
results = market_research_workflow("Tesla", "electric vehicles")
print("Workflow Results:")
for task, result in results.items():
print(f"{task}: {str(result)[:150]}...")
except Exception as e:
print(f"❌ Error during execution: {str(e)}")
print("💡 Tip: Make sure your Gemini API key is valid and you have internet connection")
def quick_scrape(url: str, instructions: str = "Extract main content"):
"""Quick scraping function for simple data extraction"""
with AdvancedNotteAgent(headless=True, max_steps=5) as agent:
response = agent.agent.run(
task=f"{instructions} from this webpage",
url=url
)
return response.answer
def quick_search(query: str, num_results: int = 5):
"""Quick search function with structured results"""
with AdvancedNotteAgent(headless=True, max_steps=10) as agent:
task = f"Search for '{query}' and return the top {num_results} results with titles, URLs, and brief descriptions."
response = agent.agent.run(
task=task,
url=",
response_format=SearchResult
)
return response.answer
def quick_form_fill(form_url: str, form_data: dict):
"""Quick form filling function"""
with AdvancedNotteAgent(headless=False, max_steps=15) as agent:
data_str = ", ".join([f"{k}: {v}" for k, v in form_data.items()])
task = f"Fill out the form with this information: {data_str}, then submit it."
response = agent.agent.run(
task=task,
url=form_url
)
return response.answer
if __name__ == "__main__":
print("🧪 Quick Test Examples:")
print("=" * 30)
print("1. Quick Scrape Example:")
try:
result = quick_scrape(" "Extract the top 3 post titles")
print(f"Scraped: {result}")
except Exception as e:
print(f"Error: {e}")
print("n2. Quick Search Example:")
try:
search_results = quick_search("latest AI news", 3)
print(f"Search Results: {search_results}")
except Exception as e:
print(f"Error: {e}")
print("n3. Custom Agent Task:")
try:
with AdvancedNotteAgent(headless=True) as agent:
response = agent.agent.run(
task="Go to Wikipedia, search for 'artificial intelligence', and summarize the main article in 2 sentences.",
url="
)
print(f"Wikipedia Summary: {response.answer}")
except Exception as e:
print(f"Error: {e}")
main()
print("n✨ Tutorial Complete!")
print("💡 Tips for success:")
print("- Start with simple tasks and gradually increase complexity")
print("- Use structured outputs (Pydantic models) for reliable data extraction")
print("- Implement rate limiting to respect API quotas")
print("- Handle errors gracefully in production workflows")
print("- Combine scripting with AI for cost-effective automation")
print("n🚀 Next Steps:")
print("- Customize the agents for your specific use cases")
print("- Add error handling and retry logic for production")
print("- Implement logging and monitoring for agent activities")
print("- Scale up with Notte's hosted API service for enterprise features")We threaten everything for the main work () working all the demons in the end, and adds immediate assistance, including Quick_Scrape, fast_search_fill, doing less functions. It also includes quick tests of guaranteeing assistance and Wikipedia work in customization before requesting a full performance travel, verifying that we can use it as soon as we use the complete agent.
In conclusion, the lesson indicates how much more, when combined with Gemini, it can come from a strong web agent, with many AI identity for research, monitoring, and analysis. It does not only show demo demomes, news, and social media but also scales in the advanced travel of many functions. In terms of this guide, enhancements may quickly associate ColoBs, expand the custom activities, and adapt to the business, automated, and creative cases.
Look Full codes here. Feel free to look our GITHUB page for tutorials, codes and letters of writing. Also, feel free to follow it Sane and don't forget to join ours 100K + ml subreddit Then sign up for Our newspaper.
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