Building a Deep Research Agent: Separating Search from Reasoning
hwchase17 · x · 2026-09-02
This post details a Deep Research Agent built with LangChain Deep Agents using a search-first architecture. The workflow includes:
- Planning: Real-time task planning and step decomposition.
- Searching: Using Liner API to fetch raw, structured search results (titles, URLs, descriptions, dates) instead of relying on the search API to generate answers.
- Reasoning: The agent filters information, compares sources, connects evidence, and synthesizes a cited final answer.
Key Insight: Separating search (finding info) from reasoning (evaluating info) ensures grounded, verifiable results. Full open-source code is provided.
Related event: Developers Build Search-First Deep Research Agents with LangChain(2 posts)→
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