Web Research

Overview

Web Research transforms the hours-long process of manual web research into a structured, automated workflow. Give it a research question, and it searches the web, reads and extracts key information from multiple sources, cross-references findings, and compiles everything into a cited report.

Unlike simple search aggregators, this skill understands nuance. It identifies conflicting information across sources, highlights consensus vs. disagreement, and provides confidence levels for each finding. Every claim is linked back to its source.

Built on LangChain’s agent framework with tool use for web search, page extraction, and structured output generation.

How It Works

1. Query decomposition — Breaks complex questions into searchable sub-queries
2. Multi-source search — Searches across web, news, academic databases, and forums
3. Content extraction — Reads and extracts relevant passages from each source
4. Synthesis — Cross-references findings, identifies consensus and conflicts
5. Report generation — Produces a structured report with citations and confidence levels

Use Cases

• Market research — Analyze competitors, pricing, and market trends
• Due diligence — Research companies, technologies, or investment opportunities
• Content creation — Gather background research for articles and blog posts
• Decision support — Compare options with evidence-based analysis
• Trend analysis — Track emerging trends across industries

Getting Started

from web_research import ResearchAgent

agent = ResearchAgent(search_depth="deep")
report = agent.research("What are the top AI agent frameworks in 2026?")
print(report.summary)
print(report.sources)

Example

Query: "Compare LangChain vs CrewAI for multi-agent systems"

Report:
- LangChain focuses on composable chains with tool use (12 sources)
- CrewAI specializes in role-based multi-agent collaboration (8 sources)
- LangChain has larger ecosystem (3x more integrations)
- CrewAI has simpler API for multi-agent workflows
- Both support major LLM providers

Sources: [1] langchain.dev, [2] crewai.com, [3] github.com/...
Confidence: High (consistent across 20 sources)

Alternatives

• Perplexity — AI-powered search with inline citations
• Tavily — Search API optimized for AI agents
• Exa — Neural search engine for AI applications