web-research

langchain-ai/deepagents · updated Apr 8, 2026

$npx skills add https://github.com/langchain-ai/deepagents --skill web-research
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summary

Orchestrates multi-source web research by delegating to subagents, synthesizing findings, and producing cited reports.

  • Breaks research questions into 2–5 distinct subtopics, creates a research plan file, and spawns up to 3 subagents in parallel for efficient investigation
  • Each subagent performs 3–5 web searches per subtopic and writes findings to local files with key facts, quotes, and source URLs
  • Synthesizes results by reading local findings files, integrating insights across subtop
skill.md

Web Research Skill

Research Process

Step 1: Create and Save Research Plan

Before delegating to subagents, you MUST:

  1. Create a research folder - Organize all research files in a dedicated folder relative to the current working directory:

    mkdir research_[topic_name]
    

    This keeps files organized and prevents clutter in the working directory.

  2. Analyze the research question - Break it down into distinct, non-overlapping subtopics

  3. Write a research plan file - Use the write_file tool to create research_[topic_name]/research_plan.md containing:

    • The main research question
    • 2-5 specific subtopics to investigate
    • Expected information from each subtopic
    • How results will be synthesized

Planning Guidelines:

  • Simple fact-finding: 1-2 subtopics
  • Comparative analysis: 1 subtopic per comparison element (max 3)
  • Complex investigations: 3-5 subtopics

Step 2: Delegate to Research Subagents

For each subtopic in your plan:

  1. Use the task tool to spawn a research subagent with:

    • Clear, specific research question (no acronyms)
    • Instructions to write findings to a file: research_[topic_name]/findings_[subtopic].md
    • Budget: 3-5 web searches maximum
  2. Run up to 3 subagents in parallel for efficient research

Subagent Instructions Template:

Research [SPECIFIC TOPIC]. Use the web_search tool to gather information.
After completing your research, use write_file to save your findings to research_[topic_name]/findings_[subtopic].md.
Include key facts, relevant quotes, and source URLs.
Use 3-5 web searches maximum.

Step 3: Synthesize Findings

After all subagents complete:

  1. Review the findings files that were saved locally:

    • First run list_files research_[topic_name] to see what files were created
    • Then use read_file with the file paths (e.g., research_[topic_name]/findings_*.md)
    • Important: Use read_file for LOCAL files only, not URLs
  2. Synthesize the information - Create a comprehensive response that:

    • Directly answers the original question
    • Integrates insights from all subtopics
    • Cites specific sources with URLs (from the findings files)
    • Identifies any gaps or limitations
  3. Write final report (optional) - Use write_file to create research_[topic_name]/research_report.md if requested

Note: If you need to fetch additional information from URLs, use the fetch_url tool, not read_file.

Best Practices

  • Plan before delegating - Always write research_plan.md first
  • Clear subtopics - Ensure each subagent has distinct, non-overlapping scope
  • File-based communication - Have subagents save findings to files, not return them directly
  • Systematic synthesis - Read all findings files before creating final response
  • Stop appropriately - Don't over-research; 3-5 searches per subtopic is usually sufficient

Discussion

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general reviews

Ratings

4.765 reviews
  • Ama Gupta· Dec 28, 2024

    Keeps context tight: web-research is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Fatima Johnson· Dec 20, 2024

    Solid pick for teams standardizing on skills: web-research is focused, and the summary matches what you get after install.

  • Chen Abbas· Dec 16, 2024

    web-research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Ganesh Mohane· Dec 12, 2024

    web-research reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Aarav Tandon· Nov 19, 2024

    web-research has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Henry Harris· Nov 11, 2024

    web-research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Aanya Garcia· Nov 7, 2024

    Solid pick for teams standardizing on skills: web-research is focused, and the summary matches what you get after install.

  • Sakshi Patil· Nov 3, 2024

    I recommend web-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Aanya Liu· Oct 26, 2024

    web-research has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Chaitanya Patil· Oct 22, 2024

    Useful defaults in web-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

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