web-research▌
langchain-ai/deepagents · updated Apr 8, 2026
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
Web Research Skill
Research Process
Step 1: Create and Save Research Plan
Before delegating to subagents, you MUST:
-
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.
-
Analyze the research question - Break it down into distinct, non-overlapping subtopics
-
Write a research plan file - Use the
write_filetool to createresearch_[topic_name]/research_plan.mdcontaining:- 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:
-
Use the
tasktool 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
-
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:
-
Review the findings files that were saved locally:
- First run
list_files research_[topic_name]to see what files were created - Then use
read_filewith the file paths (e.g.,research_[topic_name]/findings_*.md) - Important: Use
read_filefor LOCAL files only, not URLs
- First run
-
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
-
Write final report (optional) - Use
write_fileto createresearch_[topic_name]/research_report.mdif 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
Ratings
4.5★★★★★10 reviews- ★★★★★Shikha Mishra· Oct 10, 2024
web-research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Piyush G· Sep 9, 2024
Keeps context tight: web-research is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Chaitanya Patil· Aug 8, 2024
Registry listing for web-research matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Sakshi Patil· Jul 7, 2024
web-research reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Ganesh Mohane· Jun 6, 2024
I recommend web-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Oshnikdeep· May 5, 2024
Useful defaults in web-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Dhruvi Jain· Apr 4, 2024
web-research has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Rahul Santra· Mar 3, 2024
Solid pick for teams standardizing on skills: web-research is focused, and the summary matches what you get after install.
- ★★★★★Pratham Ware· Feb 2, 2024
We added web-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Yash Thakker· Jan 1, 2024
web-research fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.