Deep Research Agent with LangChain

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 ⚡