How to Build a Research Assistant using Deep Agents

LangChain's Deep Agents provide a new way to build structured, multi-agent systems that can plan, delegate and reason across multiple steps. It comes with planning, a filesystem for context and subagent spawning built in. But connecting that agent to a live frontend and actually showing what’s happening behind the scenes in real time is still surprisingly hard. Today, we will build a Deep Agents powered research assistant using Tavily and connect it to a live Next.js UI with CopilotKit , so every step the agent takes streams to the frontend in real time. You will find architecture, the key patterns, how state flows between the UI ↔ agent and the step-by-step guide to building this from scratch. Let's build it. ### What is covered? In summary, we are covering these topics in detail. What are Deep Agents? Core Components What are we building? Building Frontend Building Backend (FastAPI + Deep Agents + AG-UI) Running Application Data flow (frontend ↔ Agent) Here is the GitHub Repository ,