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调研是所有知识工作中最耗时的任务之一。人类研究者需要 10 多个小时才能完成的工作,现在用 CrawlForge 的 deep_research 工具只需 10 分钟即可完成。本指南将向你展示具体做法。
调研难题
手动调研非常艰苦:
| 任务 | 手动耗时 | 手动步骤 |
|---|---|---|
| 主题调研 | 4-8 小时 | 搜索、阅读、记笔记、验证、综合 |
| 市场分析 | 6-12 小时 | 查找来源、提取数据、对比、分析 |
| 尽职调查 | 10-20 小时 | 公司调研、新闻、财务、验证 |
| 文献综述 | 20-40 小时 | 查找论文、阅读、引用、综合 |
模式总是相同的:
- 搜索相关来源
- 阅读并提取关键信息
- 跨多个来源验证
- 检测矛盾信息
- 综合为可执行的洞察
每一步都很繁琐。每一步都可以自动化。
Deep Research 解决方案
CrawlForge 的 deep_research 工具会处理整个调研流程:
// One command to research any topic:
await deepResearch({
topic: "Current state of quantum computing commercialization",
maxDepth: 5,
maxUrls: 50,
enableSourceVerification: true,
enableConflictDetection: true,
enableSynthesis: true,
credibilityThreshold: 0.3
});幕后发生了什么:
- 查询扩展 - 生成相关的搜索查询
- 多来源搜索 - 跨 Google、新闻和学术来源进行搜索
- 内容提取 - 抓取并清理相关页面
- 来源验证 - 对每个来源的可信度评分
- 冲突检测 - 识别来源之间的分歧
- 综合 - 生成带引用的全面摘要
真实案例:市场调研
我们来走一遍真实的调研任务。
需求
Research the web scraping tools market in 2026:
- Market size and growth
- Key players and market share
- Pricing trends
- Technology trends
- Predictions for 2026
手动方式(预估:8 小时)
- 在 Google 搜索 "web scraping market size 2026"(30 分钟)
- 查找 Statista、Gartner 或类似报告(30 分钟)
- 搜索竞争对手信息(1 小时)
- 访问每个竞争对手的网站(2 小时)
- 从每个网站提取定价(1 小时)
- 搜索技术趋势(1 小时)
- 跨来源验证信息(1 小时)
- 综合成报告(1 小时)
合计:约 8 小时
CrawlForge 方式(实际:8 分钟)
// In Claude Code:
"Use deep_research to analyze the web scraping tools market in 2026.
Include market size, key players, pricing trends, and technology trends.
Verify all claims across multiple sources and flag any conflicts."
// Configuration used:
{
"topic": "web scraping tools market analysis 2026",
"maxUrls": 50,
"researchApproach": "broad",
"enableSourceVerification": true,
"enableConflictDetection": true,
"outputFormat": "comprehensive"
}实际耗时:8 分钟 23 秒 消耗 credits:10
输出结果
# Web Scraping Tools Market Analysis 2026
## Executive Summary
The web scraping tools market reached $X billion in 2026, growing at
Y% CAGR. Key trends include AI integration, MCP protocol adoption,
and shift toward managed services.
## Market Size & Growth
- 2026 Market Size: $X billion [Source: Statista, Grand View Research]
- CAGR 2026-2030: Y% [Source: Markets and Markets]
- ⚠️ CONFLICT: Gartner estimates X+0.5B, while Statista estimates X-0.3B
## Key Players
| Company | Est. Market Share | Key Differentiator |
|---------|-------------------|-------------------|
| Bright Data | 25-30% | Largest proxy network |
| Apify | 15-20% | Actor marketplace |
| ScraperAPI | 10-15% | Simplicity |
| Firecrawl | 5-10% | AI focus |
| CrawlForge | New entrant | MCP native, 26 tools |
## Pricing Trends
- Average price per 1000 requests: $X-Y
- Trend: Moving toward credit-based pricing
- Enterprise deals increasingly common
## Technology Trends
1. AI/LLM Integration (mentioned in 23/50 sources)
2. MCP Protocol Adoption (mentioned in 12/50 sources)
3. Anti-bot evasion (mentioned in 34/50 sources)
4. Headless browser optimization (mentioned in 18/50 sources)
## 2026 Predictions
- Market expected to reach $X billion
- Consolidation expected among mid-tier players
- AI-native tools to capture 30%+ market share
---
## Source Credibility Scores
| Source | Credibility | Type |
|--------|-------------|------|
| Statista | 0.9 | Data Provider |
| Gartner | 0.95 | Analyst |
| TechCrunch | 0.7 | News |
| Company Websites | 0.6 | Commercial |
## Detected Conflicts
1. Market size estimates vary by ±$500M between Gartner and Statista
2. Growth rate estimates: Optimistic (15%) vs Conservative (8%)
## All Sources (50)
1. https://statista.com/...
2. https://gartner.com/...
[...]ROI 计算
| 指标 | 手动 | CrawlForge |
|---|---|---|
| 时间 | 8 小时 | 8 分钟 |
| 成本(按 $50/小时) | $400 | $0.10(10 credits) |
| 检查的来源数 | 10-15 | 50 |
| 冲突检测 | 手动 | 自动 |
| 引用 | 手动 | 自动 |
时间节省:60 倍 成本节省:4,000 倍
配置选项
调研方式
// Broad research (default) - good for market analysis
{ "researchApproach": "broad" }
// Focused research - good for specific questions
{ "researchApproach": "focused" }
// Academic research - prioritizes scholarly sources
{ "researchApproach": "academic" }
// Current events - prioritizes recent news
{ "researchApproach": "current_events" }
// Comparative - good for X vs Y analysis
{ "researchApproach": "comparative" }按来源类型筛选
{
"sourceTypes": ["academic", "news", "government"]
// Options: academic, news, government, commercial, blog, wiki, any
}可信度阈值
{
"credibilityThreshold": 0.5 // Only include sources scoring > 0.5
// Range: 0.0 (all sources) to 1.0 (only highest credibility)
}输出格式
// Full report with all details
{ "outputFormat": "comprehensive" }
// Quick summary only
{ "outputFormat": "summary" }
// Just the sources and citations
{ "outputFormat": "citations_only" }
// Focus on disagreements between sources
{ "outputFormat": "conflicts_focus" }使用场景
1. 尽职调查
"Research [Company Name] for due diligence:
- Company history and founding team
- Funding history and investors
- Product/service overview
- Recent news and press
- Customer reviews and complaints
- Competitors and market position
Flag any red flags or concerning information."2. 竞争分析
"Deep research on [Competitor] vs [Our Company]:
- Feature comparison
- Pricing comparison
- Customer sentiment
- Recent developments
- Market positioning"3. 技术评估
"Research the current state of [Technology]:
- How it works (technical overview)
- Current adoption levels
- Key players and implementations
- Limitations and challenges
- Future outlook"4. 投资调研
"Research [Stock/Crypto] for investment:
- Fundamental analysis
- Recent news and developments
- Analyst opinions (aggregate bull/bear cases)
- Risk factors
- ⚠️ Important: Flag conflicting analyst opinions"5. 学术文献综述
"Literature review on [Topic]:
- Use academic source filter
- Key papers and authors
- Main findings and consensus
- Open questions and debates
- Recent developments (past 2 years)"
{
"researchApproach": "academic",
"sourceTypes": ["academic"],
"credibilityThreshold": 0.7
}深入了解冲突检测
deep_research 最有价值的功能之一就是自动冲突检测:
{
"enableConflictDetection": true
}它能检测什么
| 冲突类型 | 示例 |
|---|---|
| 数值分歧 | "Market size $5B" vs "$7B" |
| 日期差异 | "Founded 2020" vs "Founded 2019" |
| 事实矛盾 | "Supports X" vs "Does not support X" |
| 观点分歧 | "Will succeed" vs "Will fail" |
工作原理
- 从每个来源提取论断
- 规范化论断格式
- 跨来源对比
- 标记分歧
- 展示每个立场的来源
输出示例
## Detected Conflicts
### Conflict 1: Market Size Estimates
- **Position A:** $5.2 billion (Statista, Forbes)
- **Position B:** $7.1 billion (Grand View Research)
- **Resolution:** Difference may be due to market definition scope
### Conflict 2: Company Valuation
- **Position A:** Valued at $500M (TechCrunch, 2024)
- **Position B:** Valued at $750M (Company press release, 2026)
- **Resolution:** Valuation increased between reports最佳实践
1. 主题要具体
// Too broad (may return unfocused results):
"Research AI"
// Better:
"Research the current state of AI code generation tools,
focusing on GitHub Copilot, Cursor, and Claude Code"2. 设置合适的深度
// Quick overview (faster, cheaper):
{ "maxDepth": 2, "maxUrls": 20 } // ~5 credits worth
// Comprehensive research (thorough):
{ "maxDepth": 5, "maxUrls": 50 } // 10 credits
// Exhaustive (for critical decisions):
{ "maxDepth": 10, "maxUrls": 100 } // May exceed 10 credits3. 按时效性筛选
{
"includeRecentOnly": true // Focus on recent sources
}4. 验证关键信息
对于高风险决策,务必验证关键论断:
"Verify the claim that [specific claim] by checking:
1. Primary source (if cited)
2. At least 2 independent sources
3. Any official documentation"与其他工具搭配
Deep research 作为工作流的一部分时效果最佳:
// 1. Deep research for initial findings (10 credits)
"Deep research on [topic]"
// 2. Follow up with specific URL scraping (1-2 credits each)
"Fetch the full report from [url mentioned in research]"
// 3. Monitor for updates (3 credits)
"Track changes on [source url] and alert me to updates"局限性
请注意 deep_research 无法做到的事情:
| 局限 | 变通办法 |
|---|---|
| 无法访问付费墙内容 | 如有访问权限,使用直接 URL |
| 实时数据(股票等) | 使用专用 API |
| 极近期事件(< 1 小时) | 使用新闻 API |
| 私有公司数据 | 结合官方备案文件 |
| 主观判断 | 作为人类决策的输入 |
开始使用
准备好试用 deep research 了吗?这是最快的路径:
# 1. Sign up for free (1,000 credits)
# Visit: https://crawlforge.dev/signup
# 2. Set up CrawlForge MCP
npm install -g crawlforge-mcp-server
npx crawlforge-setup
# 3. In Claude Code, try:
"Deep research on [your topic of interest]"你的免费套餐包含 100 次 deep research 查询(每次 10 credits)。
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