Recreate Claude Code’s Core Features with LangGraph – A Hands‑On Python Tutorial

Introduction

This guide demonstrates how to implement the core modules of Claude Code with the LangGraph (Python) framework. It is intended for developers who want to understand the architecture and inner workings of a powerful coding agent.

Why Learn This Course

Explore the underlying principles – decode the mechanisms behind a strong coding agent.

Transferable patterns – acquire reusable architectural patterns for other agent scenarios.

Deep usage – gain a thorough grasp of internal logic to leverage Claude Code more efficiently.

Prerequisites

Required

Python basics (familiarity with async/await)

Basic understanding of LLM concepts

Some experience with Jupyter notebooks

Helpful

Knowledge of the ReAct framework

Prior use of LangGraph

Experience developing agents

Course Outline

Chapter 1 – Basic ReAct Agent (01_basic_react_agent.ipynb)

StateGraph, MessagesState, ToolNode basics

Manual vs. pre‑built agent comparison

Full tool‑call workflow

Graph visualization

Three practical test cases

Chapter 2 – Human‑in‑the‑Loop (02_human_in_the_loop.ipynb)

interrupt() mechanism

Dynamic command routing

MemorySaver state persistence

Hard‑coded vs. LLM‑driven review

Time‑travel (state rollback)

Production‑grade recommendations

Chapter 3 – SubAgent Implementation (03_subagent_implementation.ipynb)

SubAgent configuration and creation

TaskTool design

Three specialized SubAgents (generic / code analysis / document writing)

Context isolation demo

Tool filtering mechanism

Complete multi‑agent collaboration example

Chapter 4 – TodoList Task Management (04_todo_task_management.ipynb)

State extension for a todo list

TodoRead / TodoWrite tools

Prompt‑engineering details

LLM‑driven autonomous task management

Complex task auto‑decomposition demo

Chapter 5 – Context Compression (05_context_compression.ipynb)

Token monitoring (reverse‑search optimization)

Smart trigger at 92% token threshold

Eight‑segment compression prompt (full version)

Compression node design and implementation

Message retention strategy

Long‑dialogue simulation test

Chapter 6 – Streaming Output & Interruption (06_streaming_steering.ipynb)

Three stream_mode variants (values / updates / messages)

Event‑level streaming (astream_events)

Real‑time token streaming

Asynchronous stream handling (astream)

Interruption and resume demo

Streaming progress tracking

Steering demo

Real Steering implementation

Comprehensive Demo – claude_code_demo

Integration of all six core modules

Modular design (core/tools/nodes/utils/prompts)

Multiple execution modes (stand‑alone / module import / debugging)

Full example code

Installation

# 1. Install dependencies
pip install -r requirements.txt
# or (recommended)
uv venv
source .venv/bin/activate   # Linux/Mac
.venv\Scripts\activate    # Windows
uv pip install -r requirements.txt

# 2. Set API key (choose one)
export OPENAI_API_KEY="your-key-here"
# or use DashScope
export DASHSCOPE_API_KEY="your-key-here"

# 3. (Optional) Enable LangSmith tracing
export LANGSMITH_API_KEY="your-key"
export LANGSMITH_TRACING="true"

Running the Notebooks

# Launch Jupyter
jupyter notebook
# Or use VS Code Jupyter extension
code .

Learning Objectives

Theoretical

Deep understanding of LangGraph core concepts (StateGraph, MessagesState, Checkpointer)

Working principle of ReAct agents

Design of human‑machine collaboration and interruption mechanisms

Architecture of multi‑agent systems

Practical

Build a basic ReAct agent

Implement human‑in‑the‑loop control (interrupt, Command)

Create multi‑agent collaboration (SubAgent, tool filtering)

Manage complex task flows (TodoList, state management)

Optimize context and performance (compression, streaming)

Implement basic interruption control (Steering demo)

Engineering

Understand Claude Code’s core architecture

Adopt best practices for agent development

Gain foundational ability to develop similar applications

Additional Resources

LangGraph official documentation – https://langchain-ai.github.io/langgraph/

LangChain Python documentation – https://python.langchain.com/

Claude Code reverse analysis – https://github.com/shareAI-lab/analysis_claude_code

Project repository – https://github.com/shenzhongchao/dive-into-claude-code

FAQ

Agent execution fails

Switch to a more capable LLM. For complex tasks such as TodoList and context compression, many open‑source models fall short; the tutorial recommends using gpt‑5‑mini, which is inexpensive and stable for most cases.

License

The project is released under the MIT License, allowing commercial use, modification, distribution, and private use while requiring preservation of the original copyright notice.