web-research

作者: langchain-ai

Searches multiple web sources, synthesizes findings, and produces cited research reports using delegated subagents. Use when the user asks to research a topic…

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

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

来自 langchain-ai 的更多技能

langgraph-docs
langchain-ai
访问LangGraph文档,构建有状态代理和多代理工作流。获取官方LangGraph Python文档,涵盖状态机、基于图的代理设计以及人机协同模式。根据查询类型优先提供相关文档:实现指南解答操作问题,概念页面讲解理论,教程提供端到端示例,API参考提供技术细节。自动选择2–4个最相关的文档URL并检索其内容以回答...
official
langgraph-human-in-the-loop
langchain-ai
暂停图执行以进行人工审查、批准或验证,随后根据其输入恢复执行。需要三个组件:检查点存储器(InMemorySaver 或 PostgresSaver)、配置中的线程 ID 以及 JSON 可序列化的中断负载。interrupt(value) 暂停执行并展示数据;Command(resume=value) 恢复执行并将该值返回给暂停的节点。恢复时,interrupt() 之前的所有代码会重新执行,因此副作用必须具有幂等性(使用 upsert 而非 insert)。支持审批工作流,...
official
web-research
langchain-ai
用于处理与网络研究相关的请求;它提供了一种结构化的方法来进行全面的网络研究
official
langchain-oss-primer
langchain-ai
任何LangChain、Deep Agents或LangGraph代理构建项目都请始终从这里开始。在选择其他技能或编写任何内容之前,这是必需的起点。
official
skill-creator
langchain-ai
创建有效技能的指南,通过专业知识、工作流程或工具集成来扩展代理能力。当用户……时使用此技能。
official
social-media
langchain-ai
根据研究内容起草特定平台的社交媒体帖子,并生成配套图片。支持领英帖子(1300字符,专业语气)和推特/X话题(每条推文280字符,采用1/🧵格式)。需在撰写前将研究任务委托给子代理,随后阅读研究结果以确保准确性和相关性。使用generate_social_image工具自动生成引人注目的社交图片,采用粗体高对比度构图,针对小屏幕进行优化...
official
deep-agents-memory
langchain-ai
为Deep Agents提供可插拔的内存与文件后端,支持临时、持久化和混合路由选项。四种后端类型:StateBackend(线程作用域,临时)、StoreBackend(跨会话持久化)、FilesystemBackend(本地开发时真实磁盘访问)和CompositeBackend(将不同路径路由到不同后端)。FilesystemMiddleware提供六种文件操作工具:ls、read_file、write_file、edit_file、glob、grep。CompositeBackend使用最长前缀匹配进行路由...
official
deep-agents-orchestration
langchain-ai
编排子代理,规划多步骤任务,并对敏感操作要求人工审批。通过任务工具将工作委派给专业子代理;自定义子代理支持独立的工具集和系统提示,而默认的“通用”子代理继承主代理配置。使用write_todos规划并跟踪复杂工作流,将任务组织为待处理、进行中和已完成状态;需要thread_id以实现跨调用的持久化。实现...
official