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Deep Research Agents

Build AI agents that conduct comprehensive, multi-source research operations at scale. Go beyond simple data extraction to create research workflows that combine real-time search, historical analysis, and complex site navigation. These capabilities form the foundation for competitive intelligence, market research, and investigative analysis systems.

Learn Research Patterns

Understand multi-source research workflows

Get Started

Explore research examples

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Research Challenges Handled

Handle research challenges that typically stop basic scraping:
  • Multi-step workflows - Require session persistence across multiple requests
  • Complex site interactions - Demand browser automation for JavaScript-heavy sites
  • Historical context - Need archive access for comprehensive research
  • Research depth - Require cross-source validation for accuracy
The infrastructure provides a full toolkit for comprehensive research operations.

Session Management

Maintain session persistence across multi-step workflows

Browser Automation

Handle complex site interactions with browser automation

Historical Data

Access historical context through web archive

Cross-Source Validation

Validate research across multiple sources

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Application and Purpose

From startup competitive analysis to enterprise market intelligence, research agents require infrastructure that can:
  • Navigate complex workflows
  • Maintain context across multiple sources
  • Provide both current and historical perspectives
Built for research patterns that require both breadth and depth.

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Multi-Source Research Patterns

Combine multiple data sources for comprehensive research:

Real-Time Search

Use SERP API for real-time search results across multiple search engines

Historical Analysis

Access historical data through web archive for trend analysis

Site-Specific Data

Extract data from specific sites using browser automation

Cross-Reference Validation

Validate findings across multiple sources for accuracy

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Historical Context with Web Archive

Access historical data for comprehensive research: 
// Search historical data
const response = await fetch('https://api.brightdata.com/datasets/v3/trigger?dataset_id=YOUR_ARCHIVE_DATASET_ID', {
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${apiKey}`,
    'Content-Type': 'application/json'
  },
  body: JSON.stringify([{
    url: 'https://example.com',
    date: '2023-01-01',
    archive_type: 'web_archive'
  }])
});

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Complex Site Navigation

Navigate complex sites with browser automation: 
// Multi-step research workflow
const response = await fetch('https://api.brightdata.com/browser_api/v1/run', {
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${apiKey}`,
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    url: 'https://example.com/research',
    browser: {
      headless: true,
      viewport: { width: 1920, height: 1080 }
    },
    actions: [
      { type: 'navigate', url: 'https://example.com/search' },
      { type: 'fill', selector: '#search', value: 'research topic' },
      { type: 'click', selector: '#submit' },
      { type: 'wait', timeout: 3000 },
      { type: 'extract', selector: '.results' },
      { type: 'navigate', url: 'https://example.com/details' },
      { type: 'extract', selector: '.content' }
    ]
  })
});

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Research Workflow Orchestration

Orchestrate complex research workflows:
1

Define Research Query

Define your research query and objectives. Identify the questions you need to answer.
{
  "query": "Market analysis for AI tools",
  "objectives": [
    "Identify key competitors",
    "Analyze pricing strategies",
    "Review customer feedback"
  ]
}
2

Search Multiple Sources

Search across multiple sources simultaneously:
  • Real-time search results (SERP API)
  • Historical data (Web Archive)
  • Site-specific data (Browser API)
const searchPromises = [
  searchSERP(query),
  searchArchive(query),
  searchSite(query)
];
const results = await Promise.all(searchPromises);
3

Extract and Structure

Extract relevant data from each source and structure it for analysis.
Use data validation to ensure data quality and consistency across sources.
4

Cross-Reference and Validate

Cross-reference findings across multiple sources and validate for accuracy.
Validated research findings are ready for analysis and reporting.
5

Generate Research Report

Compile findings into a comprehensive research report.
Include source attribution and validation status for transparency.

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Cross-Source Data Validation

Validate research findings across multiple sources: 
async function validateResearch(findings, sources) {
  const validationResults = await Promise.all(
    findings.map(finding => 
      validateAgainstSources(finding, sources)
    )
  );
  
  return validationResults.filter(result => result.confidence > 0.8);
}

async function validateAgainstSources(finding, sources) {
  // Cross-reference finding across sources
  const matches = await Promise.all(
    sources.map(source => checkMatch(finding, source))
  );
  
  const confidence = matches.filter(m => m).length / sources.length;
  return { finding, confidence, sources: matches };
}

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Enterprise Research Templates

Use pre-built templates for common research workflows:

Competitive Intelligence

Template for competitive analysis and market research

Market Analysis

Template for comprehensive market research

Investigator Research

Template for investigative research workflows

Trend Analysis

Template for historical trend analysis

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Examples

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Competitive Intelligence Research

Research competitors across multiple sources: 
async function researchCompetitor(competitorName) {
  // Search real-time data
  const serpResults = await searchSERP(`${competitorName} pricing features`);
  
  // Search historical data
  const archiveResults = await searchArchive(competitorName, '2023-01-01');
  
  // Extract site-specific data
  const siteData = await extractFromSite(`https://${competitorName}.com`);
  
  // Cross-reference findings
  const validated = await validateResearch([
    ...serpResults,
    ...archiveResults,
    siteData
  ]);
  
  return {
    competitor: competitorName,
    findings: validated,
    sources: ['serp', 'archive', 'site']
  };
}

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Market Research Workflow

Conduct comprehensive market research: 
async function conductMarketResearch(topic) {
  // Step 1: Search current trends
  const currentTrends = await searchSERP(`${topic} trends 2024`);
  
  // Step 2: Analyze historical trends
  const historicalTrends = await searchArchive(topic, '2020-01-01');
  
  // Step 3: Extract competitor data
  const competitors = await findCompetitors(topic);
  const competitorData = await Promise.all(
    competitors.map(c => researchCompetitor(c))
  );
  
  // Step 4: Validate and compile
  const researchReport = {
    topic,
    currentTrends,
    historicalTrends,
    competitors: competitorData,
    validated: true
  };
  
  return researchReport;
}

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Next Steps

SERP API Quickstart

Start collecting search results for research

Browser API Quickstart

Automate complex site navigation for research

Web Archive

Access historical data for trend analysis

Deep Lookup

Use Deep Lookup for comprehensive research
Need help? Check out our Research Examples or contact support.