Deep Research System
This course is for developers and researchers who have completed the LLM Basic, RAG, and Agent in Depth training. It provides in-depth study of the design and implementation of deep research systems based on multi-agent collaboration. Through systematic theoretical learning and practical exercises, you will master the core skills of building complex research Agent systems using LangGraph, including key links such as problem decomposition, information retrieval, content integration, and reflection and optimization.
Day 1: Deep Research Agent Theory and Architecture
Systematic Design from Requirements to Architecture
π Today's Content
1.1 LangGraph and Agent Review
Review of Agent architecture in LangChain / LangGraph
Analysis of the core components, workflow modeling, and collaboration modes of Agent
1.2 Deep Research System Architecture Design
Analysis of typical requirements for deep research tasks
Layered architecture and module responsibility decomposition of research-oriented Agent systems
Research process modeling and visual analysis based on LangGraph
π‘ Learning Objectives
- β In-depth understanding of the requirements and design challenges of deep research systems
- β Master the layered architecture design and modular decomposition methods
- β Proficient in using LangGraph for complex process modeling
- β Possess the visual analysis and optimization capabilities of research systems