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    LangChain CEO Retweets Deep Research Agent Post

    Sumanth described building an agent that plans queries and pulls web sources via LangChain Deep Agents.

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    SU
    3 Sources, 29d ago, first seen 29d ago

    TLDR

    Harrison Chase, co-founder and CEO of LangChain, quoted and retweeted a post by Sumanth. Sumanth stated he built a Deep Research Agent using LangChain Deep Agents with a search-first design. The agent plans research in real time, breaks tasks into smaller steps, searches the web, and reasons over sources. Chase wrote that deep research is a complex multi-step workflow and that this matches the intended use of Deep Agents.

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    3 Sources, first seen 29d ago

    Combined views

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    3 Sources, first seen 29d ago

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    25 comments
    565 saves
    89 reposts

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    Positive——Negative

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    3 Sources

    @Sumanth_077A 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: https://github.com/Sumanth077/Hands-On-AI-Engineering/tree/main/ai_agents/deep_research_assistant
    @hwchase17RT @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…

    3 Sources

    @Sumanth_077A 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: https://github.com/Sumanth077/Hands-On-AI-Engineering/tree/main/ai_agents/deep_research_assistant
    @hwchase17RT @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…