analysis_claude_code: a library for reverse engineering Claude Code.

analysis_claude_code is a public GitHub project whose core content is for the Claude Code A complete research and analysis material for reverse engineering version v1.0.33. This project reveals in detail the core architectural design, internal implementation mechanism, and overall operation logic of a modern AI programming assistant (Claude Code) through systematic and in-depth technical analysis of over 50,000 lines of obfuscated source code. The main research results of the project include the discovery and analysis of key technologies such as real-time Steering mechanism, hierarchical multi-agent architecture, intelligent context management, and enhanced security protection. In addition, the project provides complete system architecture diagrams, code analysis scripts, and detailed technical documents, which provide valuable technical references and engineering implementation blueprints for developers and researchers in the field of AI to understand and build complex AI Agent systems.

Function List

• Deep Code Analysis: Provides the results of a complete analysis of over 50,000 lines of obfuscated source code for Claude Code v1.0.33, covering the entire technology stack from the user interface to the core engine.
• Core Technology Revealed: Documented in detail four core technical findings, including real-time steering mechanisms, hierarchical multi-agent architecture, intelligent context management, and enhanced security.
• System Architecture Documentation: Contains a detailed panorama of the system architecture, visualizing the relationships between the user interaction layer, the Agent core scheduling layer, the tool execution and management layer, and the storage and persistence layer.
• Detailed analysis of methodology: Disclosed the reverse engineering methodology used in the project, including static code analysis (e.g., code preprocessing, LLM-assisted analysis) and dynamic behavioral verification (e.g., runtime analysis, integration testing).
• Open Source Reconstruction Guide: A set of TypeScript framework templates, core component interface definitions, and phased implementation guidelines for rebuilding similar systems are provided.
• Extensive technical documentation: Includes in-depth documentation analyzing multiple dimensions of the core mechanism, special features (e.g., UI, image processing), and cross-validation reports.

Using Help

The GitHub repository itself is a technical research repository rather than a software tool that can be installed and run directly. Its primary use is to provide developers and researchers with in-depth insights and technical references on the design of modern AI Agent systems. Below is a how-to guide on how to utilize this repository for study and research.

1. Understanding the project structure

First, you need to familiarize yourself with how the repository's files and directories are organized in order to quickly find the information you need.

• claude_code_v_1.0.33/: This is the core directory and contains the complete analysis information for version v1.0.33.
  • stage1_analysis_workspace/: Stores the results of the first stage of static analysis.
    • Claude_Code_Agent系统完整技术解析.md: This is the core technical analysis document and is recommended to be read as a priority.
    • chunks/:: Stores 102 de-obfuscated and partitioned code block files (.mjs(Format).
    • scripts/: Contains scripts for analysis, such as code beautification (beautify.js) and segmentation (split.js), etc.
    • docs/: Contains more detailed technical specific documentation.
• work_doc_for_this/: Documentation of project workflows, such as standard operating procedures (SOPs) for reverse engineering.
• README.md: The main description document for the project, which provides key information such as an overview of the project, core findings, and architecture diagrams.

2. Learning core technical findings

sports eventREADME.mdThe document details four groundbreaking technological innovations. You can start here to get a macro view of Claude Code's design philosophy.

• Real-time Steering Mechanism: ReadREADME.mdconsultations onh2AA description of the double-buffered asynchronous message queue to understand how it enables zero-latency messaging.
• Hierarchical Multi-Agent Architecture: Review the system architecture diagram to understand how the Master Agent, SubAgent, and Task Agent work together and segregate permissions.
• Intelligent Context Management: Learn how the system automatically triggers context compression when Token usage reaches 92%, and how to pass theCLAUDE.mdfiles for long-term memory storage.
• Enhanced security: Understand the 6-layer permission validation chain from UI to tool execution and how sandbox isolation mechanisms secure tool execution.

3. In-depth study of code implementation

If you want to dive into the specific technical implementation details, you can follow the steps below:

• Read the technical analysis document: Enterclaude_code_v_1.0.33/stage1_analysis_workspace/directory, open theClaude_Code_Agent系统完整技术解析.mddocument, this document explains in detail the main components of the system and how it works.
• View de-obfuscated code: Inchunks/directory, you can find the de-obfuscated JavaScript code split into 102 modules. These files are still complex, but in conjunction with the technical documentation can help you understand their exact logic. For example.cli.chunks.mjsis the main file chunk of the command line interface.
• Running analysis scripts (advanced): If you have a Node.js environment, try using thescripts/scripts in the directory. For example, using thebeautify.jscan help you format your code to make it more readable. An example of a command to run a script is usually as follows:

  node scripts/beautify.js source/cli.mjs
  

4. Design with reference to the system architecture

For system architects or AI application developers, the most valuable part of this repository is its architectural design and implementation patterns.

• Research Architecture Diagram:: ScrutinizingREADME.mdA panoramic view of the system architecture in the The diagram clearly shows each layer from user interaction to data storage. You can borrow this layered, decoupled design idea to build your own applications.
• Implementation framework for learning tools:: The "6-stage execution pipeline" described in the analysis document, which covers the complete process from tool discovery, parameter validation, permission checking to concurrent execution and result recovery, provides an excellent model for designing a robust plug-in system.
• Borrowing from memory management strategies: Claude Code's intelligent compression algorithm and dynamic context window adjustment strategy are important references for optimizing Token consumption and memory footprint of large language models.

5. Reconstruction reference as an open source project

The project also provides guidelines for developers who wish to build a Claude Code-like system from scratch.

• View the rebuild guide: README.mdThe "Open Source Rebuild Guidelines" section in theOpen-Claude-Code/Project template and phased implementation steps.
• carry out in stages:: The guide divides the rebuilding process into five phases: project initialization, core engine implementation, advanced feature development, integration and extension, and testing and release. This provides a clear roadmap for managing complex software projects.

application scenario

1. AI Agent Architecture Learning
   For students, researchers, or developers new to the AI field, this is a complete example of a modern AI Agent system implementation. By learning its layered architecture, asynchronous message queuing, multi-agent collaboration, and security design, you can quickly master the design principles and engineering practices of advanced AI systems.
2. High Performance Asynchronous System Design Reference
   Revealed in the projecth2AAsynchronous message queuing, which achieves zero-latency message delivery and efficient backpressure control through a double buffering mechanism. This provides a valuable asynchronous programming design reference for applications that need to handle highly concurrent, real-time streaming data (e.g., online chats, real-time collaboration tools).
3. Security Architecture Design for Complex Systems
   The report analyzes in detail the 6-layer permission verification chain and sandbox isolation technology from UI to tool execution. When designing complex systems (such as open platforms and plug-in systems) that require the execution of external code or the handling of untrustworthy data, enterprise developers can learn from its multi-layer security protection implementation scheme to enhance the security of the system.
4. Open source project development guidance
   The repository not only analyzes existing systems, but also provides a complete set of open source rebuild guides and project templates. For teams looking to launch a complex AI Agent open source project or commercial product, this resource can serve as a solid foundation for technical blueprints and architectural design in the early stages of the project, effectively avoiding common technical pitfalls.

QA

1. Is this repository a working Claude Code client?
   Not. This repository is a research repository containing the results of reverse engineering analysis, technical documentation, and code snippets for Claude Code v1.0.33, and its primary purpose is for educational and academic research, not to provide an executable program.
2. Can the code in the repository be used directly in my project?
   The code in the repository is reverse-engineered, analyzed and de-obfuscated, and is mainly used to demonstrate and explain the inner workings of Claude Code. While you can learn from its design ideas and algorithms, copying and pasting directly into a production project is not recommended. You should refer to its "Open Source Rebuild Guide" and implement your own core components in a modern programming language (e.g. TypeScript).
3. How accurate are the analytical results of this program?
   According to the project documentation of theFINAL_VALIDATION_REPORT.mdThe accuracy of its core architecture design reaches 95%, and the accuracy of key mechanism implementation reaches 98%. The project team ensures the accuracy and consistency of the analysis results through multiple rounds of iterative analysis, cross-validation, and source code comparison.
4. What kind of technical background do I need to read this program?
   For a deep understanding of this project, you should ideally have a basic knowledge of JavaScript (especially asynchronous programming and modularity). Also, an understanding of the basic concepts of AI Agent, Large Language Model (LLM), system architecture design, and software reverse engineering will greatly help you understand the core elements in the documentation and code.