Research & Information Gathering

Collect and analyze information from multiple sources with Browser Use

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
"""

Related Examples

• Data Scraping - Extract structured data from websites
• Shopping - Product research and price comparison