# LangChain and LangGraph Crash Course: A Guide from Basics to Advanced Agent Workflow Construction

> A practical tutorial on LangChain and LangGraph for developers, covering complete workflows such as RAG systems, intelligent search agents, and text classifiers, providing directly runnable Python scripts instead of Jupyter notebooks

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-02T10:14:01.000Z
- 最近活动: 2026-05-02T10:18:29.694Z
- 热度: 150.9
- 关键词: LangChain, LangGraph, RAG, Agent, LLM应用开发, 教程, Python, 工作流编排
- 页面链接: https://www.zingnex.cn/en/forum/thread/langchainlanggraph-agent
- Canonical: https://www.zingnex.cn/forum/thread/langchainlanggraph-agent
- Markdown 来源: floors_fallback

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## Introduction: Core Overview of the LangChain and LangGraph Crash Course

This course is a practical tutorial for AI application developers, presented in pure Python script form. It covers complete content such as LangChain basic components, LangGraph workflow orchestration, and advanced RAG systems, helping developers quickly master the core capabilities of both frameworks and cultivate engineering development habits.

## Course Background and Design Philosophy

The LangChain LangGraph Crash Course (LLCC) is positioned as a practical tutorial for mastering the frameworks from scratch. Unlike the Jupyter Notebooks in official documentation, it uses fully annotated pure Python scripts that can be run directly. The learning path is: first master LangChain basic components, then orchestrate complete workflows via LangGraph. It is suitable for engineers with Python basics who want to quickly get started with large model application development.

## Hands-on Practice with LangChain Basic Components

### Chat Models and Prompt Templates
The first example `simple_llm_application.py` demonstrates the basic LLM calling pattern, building a chat application via Chat Models and Prompt Templates. It emphasizes the declarative chain assembly syntax of LangChain Expression Language (LCEL) to improve readability and extensibility.

### Semantic Search Engine Construction
The second example `semantic_search_engine.py` introduces basic RAG components, demonstrating content loading from PDFs, vector embedding (text-embedding-3-large), and vector database storage. It reveals the advantage of semantic search in capturing semantic relationships through vector similarity.

### Text Classification and Structured Output
`text_classifier.py` uses large model structured output to implement text classification; `data_extractor.py` extracts structured data via Few-Shot Prompting, demonstrating LangChain's ability to build text understanding pipelines.

## Core Capabilities of LangGraph Workflow Orchestration

### Intelligent Agent and Tool Calling
`agent.py` builds an intelligent agent with web search capabilities, defining tools, system prompts, and decision loops, and autonomously decides whether to call tools to integrate external information. LangGraph provides clearer architectural abstraction and state management capabilities.

### Human-Machine Collaboration Mode
`agent_human_assistance.py` implements the Human-in-the-loop mode, where the agent pauses to wait for human input when encountering uncertain situations, ensuring controllability and safety in high-risk scenarios.

### Time Travel and State Backtracking
`agent_time_travel.py` demonstrates state history management functions, supporting viewing execution trajectories, backtracking to any state, and modifying decisions to re-execute, helping debug complex agent behaviors.

## Advanced Practice of RAG Systems

### Basic RAG and Self-Query
`rag.py` implements the standard RAG process (document loading → chunking → embedding → indexing → retrieval → generation), introducing Self-Query technology that allows large models to automatically generate retrieval filters to improve accuracy (e.g., filtering by extracted year and topic).

### Multi-step Reasoning and ReAct Mode
`rag_delegation.py` uses the ReAct mode (Reasoning + Acting), where the agent alternates between thinking (analyzing information, planning actions) and acting (retrieval/computation), enhancing the ability to handle complex multi-hop problems.

## Recommended Learning Resources and Version Notes

Advanced resources recommended by the course:
- LLM Course by Maxime Labonne (systematic LLM theory and practice)
- LLM Engineer's Handbook (LLM engineering practice in production environments)
- Hugging Face Agents Course (free agent building course)
- Official documentation (LangChain and LangGraph tutorials and APIs)

Version reminder: The tutorial is based on pre-1.0 versions of LangChain. The core logic remains unchanged, but a few syntax adjustments are needed by referring to the migration guide.

## Practice Suggestions and Summary

Learning suggestions: Practice step by step in the course order—first master the independent use of basic components, then learn complex workflow orchestration. The pure Python script form is more in line with engineering code organization methods, cultivating good development habits.

Summary: LLCC focuses on practicality, quickly building hands-on experience through runnable code. It is a high-quality resource for building applications after understanding the principles of large models.