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
Study 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
Learning Objectives +
- โ In-depth understanding of the requirements and design challenges of deep research systems
- โ Master the layered architecture design and modular decomposition methods
- โ Proficient in using LangGraph for complex process modeling
- โ Possess the visual analysis and optimization capabilities of research systems
Day 2: Multi-Agent Collaboration Implementation
Complete Implementation from Theory to Practice
Study Content +
2.1 Practice Preparation
Project environment setup and dependency description
Code structure overview and course objective introduction
2.2 Multi-Agent Collaboration System Implementation
Question Agent: responsible for problem analysis and task decomposition
Web Search Agent: performs external information retrieval tasks
Summary Agent: integrates content and generates summaries of retrieval results
Reflect Agent: reflects on the research process and optimizes the path
Answer Agent: integrates all context to generate the final answer
2.3 Comprehensive Practical Exercises
Build a deep research Agent system from scratch
Practice typical business scenarios, such as competitive product analysis, policy analysis, academic review, etc.
Guide system tuning and problem diagnosis skills
2.4 Summary and Q&A
Review of course knowledge and summary of key points
Answer students' questions during practice
Subsequent advanced directions and recommended learning resources
Learning Objectives +
- โ Proficient in the development and integration technology of multi-Agent systems
- โ Possess the ability to build a complete deep research system
- โ Master the system design and implementation of typical business scenarios
- โ Possess the practical skills of system tuning and problem diagnosis
- โ Form a complete knowledge system of deep research Agent systems