# Web-enhanced AI answers with any of multiple models
answer = client.answer(
    query="What's happening in quantum computing?",
    model="llmlayer-web",  # Recommended - or openai/gpt-5.1, openai/gpt-4o-mini, etc.
    search_type="news",
    return_sources=True
)

# Search news
news = client.search_web(query="AI regulation", search_type="news", recency="day")

# Search academic papers
papers = client.search_web(query="transformer architecture", search_type="scholar")

# Search with domain filtering
results = client.search_web(
    query="machine learning",
    domain_filter=["arxiv.org", "nature.com", "-reddit.com"]
)

# Get clean markdown
content = client.scrape(url="https://example.com", formats=["markdown"])

# Capture screenshots
screenshot = client.scrape(url="https://example.com", formats=["screenshot"])

# Get Html of the page
html = client.scrape(url="https://example.com", formats=["html"])

# PDF text extraction
pdf_content = client.get_pdf_content(url="https://example.com/document.pdf")

# YouTube transcripts
transcript = client.get_youtube_transcript(
    url="https://youtube.com/watch?v=...",
    language="en"
)

# 1. Search for recent papers
papers = client.search_web(
    query="large language models optimization",
    search_type="scholar"
)

# 2. Extract content from PDFs
for paper in papers.results[:3]:
    if paper.get('pdfUrl'):
        content = client.get_pdf_content(url=paper['pdfUrl'])

# 3. Scrape related websites
websites = client.search_web(query="LLM optimization techniques", search_type="general")
for site in websites.results[:5]:
    page_content = client.scrape(url=site['link'], formats=["markdown"])

# 4. Generate comprehensive analysis
analysis = client.answer(
    query="Summarize the latest LLM optimization techniques",
    model="openai/gpt-5.1",
    system_prompt="You are a research analyst. Synthesize the provided information."
)

# Monitor news across multiple sources
news = client.search_web(
    query="renewable energy",
    search_type="news",
    recency="hour",
    location="us"
)

# Extract full articles
articles = []
for item in news.results:
    content = client.scrape(url=item['link'], formats=["markdown"])
    articles.append(content.markdown)

# Generate summary with citations
summary = client.answer(
    query="Summarize today's renewable energy news",
    model="llmlayer-fast",  # Fast answers
    citations=True
)