Sumanth用LangChain Deep Agents做了个研究智能体，能搜索、推理并生成带引用的报告，比普通搜索API更智能。
Deep Research Agent是一个基于LangChain Deep Agents构建的研究智能体，采用搜索优先架构。该智能体实时规划研究，将工作分解为小步骤，搜索网络资源，检索结果来自Liner平台。每个最终答案中的声明都有检索结果来源支持，完整报告可保存为Markdown文件。
Deep research is a complex, multi-step search and synthesis workflow This is exactly the kind of wo...
Deep research is a complex, multi-step search and synthesis workflow This is exactly the kind of workflow Deep Agents is meant to make easier to build Sumanth @Sumanth_077 A research agent is only as good as the search layer behind it! I built a Deep Research Agent using LangChain Deep Agents with a search-first architecture designed around grounded, source-backed research. The agent plans the research in real time, breaks the work into smaller steps, searches across the web, reasons over the retrieved sources, and produces a fully cited answer. For this project, I used Liner as the search and grounding layer. Liner returns raw, structured search results with titles, URLs, descriptions, and dates. The agent then handles the reasoning and synthesis itself instead of relying on the search API to generate the final answer. That separation matters. Search is responsible for finding the right information. The agent is responsible for deciding what matters, comparing sources, connecting the evidence, and producing the final response. The workflow looks like this: Research → Plan → Search → Collect sources → Synthesize → Cite → Save report Every claim in the final answer is backed by a source from the retrieved results, and the full report is saved as a downloadable Markdown artifact. You can also inspect the entire run in the Deep Agents UI, including the research plan, search calls, retrieved sources, and final synthesis. Liner handles retrieval, while the agent keeps control over reasoning, synthesis, and how the final answer is constructed. Github Repo: github.com/Sumanth077/Han… 🔗 View Quoted Tweet 💬 4 🔄 1 ❤️ 10 👀 1761 📊 4 ⚡