Skip to main content
Browser Use excels at gathering information from multiple websites, analyzing content, and synthesizing research findings.

Basic Research Task

Gather information on a specific topic:
import asyncio
from browser_use import Agent, ChatBrowserUse

async def main():
    task = """
    Research the latest developments in quantum computing:
    1. Search for recent news articles (last 30 days)
    2. Find 3-5 key developments or breakthroughs
    3. Summarize each finding with:
       - Title
       - Source
       - Date
       - Key points
    4. Identify common themes across articles
    """
    
    agent = Agent(task=task, llm=ChatBrowserUse())
    result = await agent.run()

if __name__ == '__main__':
    asyncio.run(main())

Multi-Source Research

Gather information from specific sources:
task = """
Research electric vehicle market trends:

Sources to check:
1. Tesla investor relations page
2. Bloomberg automotive section
3. TechCrunch transportation category

For each source, extract:
- Latest articles/reports about EV sales
- Market share data
- Future predictions

Compile findings into a comprehensive summary comparing 
perspectives from each source.
"""

agent = Agent(
    task=task,
    llm=ChatBrowserUse(),
    max_steps=100  # Allow more steps for thorough research
)

Social Media Research

Find and analyze social media profiles:
import asyncio
from pydantic import BaseModel
from browser_use import Agent, ChatOpenAI, Tools
from browser_use.agent.views import ActionResult

class Profile(BaseModel):
    platform: str
    profile_url: str

class Profiles(BaseModel):
    profiles: list[Profile]

tools = Tools(exclude_actions=['search'], output_model=Profiles)

@tools.registry.action('Search the web for a specific query')
async def search_web(query: str):
    """Custom search implementation for better results"""
    # Your search API implementation here
    pass

async def main():
    task = """
    Find social media profiles for the TikTok user from this video:
    https://www.tiktok.com/share/video/7470981717659110678/
    
    1. Open the TikTok video
    2. Extract the @username from the URL or page
    3. Search the web for this username
    4. Find their profiles on:
       - Instagram
       - Twitter/X
       - YouTube
       - LinkedIn
    5. Return the profile URLs with platform names
    """
    
    agent = Agent(task=task, llm=ChatOpenAI(model='gpt-4.1-mini'), tools=tools)
    history = await agent.run()
    
    # Access structured output
    if history and history.structured_output:
        profiles = history.structured_output
        for profile in profiles.profiles:
            print(f'{profile.platform}: {profile.profile_url}')

if __name__ == '__main__':
    asyncio.run(main())

Competitive Analysis

Compare competitors across multiple dimensions:
from pydantic import BaseModel, Field

class CompanyInfo(BaseModel):
    name: str
    website: str
    pricing_model: str
    key_features: list[str]
    target_market: str
    latest_news: str

class CompetitiveAnalysis(BaseModel):
    competitors: list[CompanyInfo]
    market_leader: str
    key_differentiators: dict[str, str]

async def analyze_competitors(industry: str):
    task = f"""
    Research top 5 companies in the {industry} industry:
    
    For each company:
    1. Visit their website
    2. Extract:
       - Company name
       - Pricing model
       - Key features/products
       - Target market
       - Recent news or product launches
    
    Analysis:
    - Identify the market leader
    - Compare key differentiators
    - Note unique selling propositions
    """
    
    agent = Agent(
        task=task,
        llm=ChatBrowserUse(),
        output_model_schema=CompetitiveAnalysis
    )
    
    return await agent.run()

News Aggregation

Collect news from multiple sources on a topic:
task = """
Aggregate news about artificial intelligence from:
1. TechCrunch (https://techcrunch.com)
2. The Verge (https://www.theverge.com)
3. Hacker News (https://news.ycombinator.com)

For each site:
- Find the top 3 AI-related articles from today
- Extract: headline, summary, publication time, URL

Organize by publication time (newest first) and 
identify any overlapping stories across sources.
"""

agent = Agent(task=task, llm=ChatBrowserUse())

Academic Research

Gather information from academic sources:
task = """
Research papers on machine learning optimization:

1. Go to Google Scholar (https://scholar.google.com)
2. Search for "machine learning optimization techniques" 
   published in last 2 years
3. Extract top 10 papers with:
   - Title
   - Authors
   - Publication venue
   - Citation count
   - Abstract (first 200 words)
   - PDF link if available

4. Identify the most cited papers
5. Find common methodologies across papers
"""

agent = Agent(
    task=task,
    llm=ChatBrowserUse(),
    max_steps=80
)

Product Research

Research product specifications and reviews:
task = """
Research the iPhone 15 Pro:

Information to gather:
1. Official specs from Apple.com:
   - Display size and technology
   - Processor
   - Camera specifications
   - Battery life
   - Storage options
   - Price for each variant

2. Reviews from:
   - CNET
   - The Verge
   - TechRadar

3. For each review, extract:
   - Overall rating
   - Pros and cons
   - Verdict summary

4. Compare findings and create a comprehensive summary
   highlighting consensus opinions and outliers.
"""

Market Research

Analyze market trends and consumer sentiment:
task = """
Market research for sustainable fashion brands:

1. Identify top 10 sustainable fashion brands
2. For each brand visit their website and extract:
   - Sustainability practices
   - Price range
   - Target demographic
   - Unique value proposition

3. Check social media presence:
   - Instagram follower count
   - Recent post engagement
   - Brand sentiment in comments

4. Search for industry reports on sustainable fashion market size

5. Compile findings into market overview with:
   - Market leaders
   - Price positioning
   - Growth trends
   - Consumer preferences
"""

agent = Agent(
    task=task,
    llm=ChatBrowserUse(),
    max_steps=150  # Complex research needs more steps
)

Wikipedia Research

Extract structured information from Wikipedia:
task = """
Research quantum computing on Wikipedia:

1. Go to Wikipedia article on Quantum Computing
2. Extract:
   - Introduction summary
   - Key principles (from "Principles" section)
   - Current applications
   - Major companies/organizations involved
   - Timeline of developments
   - References to notable papers

3. Follow links to related articles:
   - Quantum supremacy
   - Quantum algorithm
   
4. Create a comprehensive overview with sources
"""

Research Best Practices

1

Define Clear Objectives

Specify exactly what information you need and from which sources
2

Structure Your Output

Use Pydantic models to ensure consistent data format across sources
3

Verify Information

Cross-reference facts from multiple sources for accuracy
4

Save Your Progress

Use save_conversation_path to preserve research history
Performance Tip: For extensive research, increase max_steps to allow thorough investigation across multiple sites.
Always respect website terms of service and robots.txt when conducting research at scale.

Advanced Research Patterns

Iterative Deep Dive

task = """
Start with broad topic "renewable energy", then:
1. Identify the 3 most promising subtopics
2. For each subtopic, find top 3 authoritative sources
3. From those sources, extract key statistics and trends
4. Synthesize findings into a structured report
"""

Comparative Analysis

task = """
Compare cloud providers (AWS vs Azure vs GCP):
1. Visit pricing pages for each
2. Compare equivalent services (compute, storage, database)
3. Check documentation quality
4. Review customer case studies
5. Create side-by-side comparison table
"""