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Skills/langchain-ai/deepagents/web-research
web-research logo

web-research

langchain-ai/deepagents
3K installs25K stars
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Installation

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

Summary

Searches multiple web sources, synthesizes findings, and produces cited research reports using delegated subagents. Use when the user asks to research a topic online, search the web, look something up, find current information, compare options, or produce a research report.

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.

  1. Analyze the research question - Break it down into distinct, non-overlapping subtopics
  1. 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
  1. 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
  1. 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
  1. 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

Score

0–100
71/ 100

Grade

B

Popularity23/30

2,958 installs — solid traction. Source repo has 24,620 GitHub stars.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Web Research skill score badge previewScore badge

Markdown

[![Web Research skill](https://www.remoteopenclaw.com/skills/langchain-ai/deepagents/web-research/badges/score.svg)](https://www.remoteopenclaw.com/skills/langchain-ai/deepagents/web-research)

HTML

<a href="https://www.remoteopenclaw.com/skills/langchain-ai/deepagents/web-research"><img src="https://www.remoteopenclaw.com/skills/langchain-ai/deepagents/web-research/badges/score.svg" alt="Web Research skill"/></a>

Web Research FAQ

How do I install the Web Research skill?

Run “npx skills add https://github.com/langchain-ai/deepagents --skill web-research” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Web Research skill do?

Searches multiple web sources, synthesizes findings, and produces cited research reports using delegated subagents. Use when the user asks to research a topic online, search the web, look something up, find current information, compare options, or produce a research report. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Web Research skill free?

Yes. Web Research is a free, open-source skill published from langchain-ai/deepagents. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Web Research work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Web Research works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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