Claude Code Domestic Alternative: Low-Cost Deployment and Six Core Architecture Components Explained

One of the hottest events in the large language model space in 2025 has been the explosion of OpenAI Codex. If Codex is a versatile Swiss Army knife, then Claude Code is a precise surgical scalpel. Their architectures and philosophies both represent the most cutting-edge Agent paradigms globally.

The so-called Agent paradigm is one of the most important technological trends in the AI field today. Unlike traditional "question-and-answer" style AI conversations, Agents possess the ability to autonomously plan, invoke tools, and interact with environments. They can receive a high-level goal, automatically decompose it into multiple sub-steps, sequentially invoke different tools for execution, and dynamically adjust strategies based on intermediate results. The theoretical foundation of this paradigm traces back to the ReAct (Reasoning + Acting) framework, proposed by Google DeepMind in 2022, with the core idea of letting large models take actions while reasoning, forming a "think-act-observe" loop. OpenAI Codex and Claude Code are precisely the industrial-grade implementations of this paradigm in the software engineering domain.

However, Claude Code's hefty subscription fees have deterred many domestic developers. This article, based on a comprehensive step-by-step tutorial, will walk you through how to deploy a low-cost Claude Code alternative in a domestic environment and deeply dissect its core architecture.

Why Should You Care About Claude Code?

Many people feel Claude Code is far removed from their daily work, or even feel intimidated because it's a command-line tool. But real-world testing has proven that its performance on complex industrial-grade projects not only surpasses ordinary AI assistants but even produces better and faster results than 80% of human programmers.

Here's a key insight: Even if you never write a single line of code, mastering its underlying architecture will give you a significant advantage in the Agent era. Claude Code represents not just a programming tool, but an entirely new human-machine collaboration paradigm—driving intelligent agents to complete complex task chains through natural language instructions. Understanding this architecture has practical value for product managers, technical leaders, and even non-technical professionals.

Local Private Deployment: A Practical Path to Bypass Expensive Subscriptions

Deployment Strategy and Environment Configuration

Claude Code's official subscription is pricey, and domestic users face dual barriers of network access and payment. The core strategy of domestic alternatives is: using domestically available large models or third-party relay services with compatible API interfaces to replace the official Claude API calls, thereby running Claude Code's full functionality in a local environment.

Here we need to understand the technical principles of API adaptation. Under the hood, Claude Code makes HTTP requests to Anthropic's Claude API to obtain model inference capabilities. Many domestic large models (such as DeepSeek^Ad, Qwen) provide interface specifications compatible with the OpenAI API format, including identical request paths (e.g., /v1/chat/completions), identical JSON request body structures, and streaming response formats. By modifying Claude Code's API request endpoint (Base URL) and authentication key (API Key) to redirect requests to these compatible services, the underlying model can be replaced without modifying the core code logic. Some solutions also use API gateways or intermediate proxy layers to handle subtle differences in request formats, achieving smoother adaptation.

The specific deployment process typically includes the following steps:

1. Environment Preparation: Install Node.js runtime environment and configure necessary dependencies
2. API Adaptation: Replace Claude Code's API request endpoint with domestically available large model services (such as DeepSeek, Qwen, or other compatible interfaces)
3. Local Launch: Start the service via command line or Web visualization interface
4. Function Verification: Test core features one by one to ensure they work properly

Web Visualization Interface: Lowering the Barrier to Entry

While command-line interfaces are efficient, they aren't user-friendly for many people. An important improvement in alternative solutions is providing a Web visualization interface, allowing users to interact with the Agent intuitively through a browser. This not only lowers the learning curve but also facilitates team collaboration and task management.

Deep Dive into Claude Code's Six Core Systems

The reason Claude Code is so powerful lies in its carefully designed underlying architecture. Understanding these six core systems is the key to mastering top-tier intelligent agent design principles.

Built-in Tools System

Claude Code comes with a suite of built-in tools including file read/write, code execution, terminal commands, and search-and-replace. These tools form the foundational capability layer for Agent interaction with the external environment. Unlike ordinary AI conversations, Claude Code can directly operate on the file system, run code, and receive feedback, forming a complete "perception-decision-execution" closed loop.

This closed loop originates from classical agent theory (Agent Loop) and is the core pattern of autonomous system design. Specifically, "perception" refers to the Agent's information-gathering behaviors such as reading file contents, obtaining terminal output, and parsing error logs; "decision" refers to the large model reasoning based on current context to determine what operation to execute next; "execution" refers to invoking built-in tools to actually complete actions like file modifications and command execution. The key is that execution results feed back to the Agent as new perceptual input, forming a continuously iterating loop. This is fundamentally different from traditional AI conversations—traditional conversations are stateless text generation, while the Agent loop is a stateful, autonomous behavior chain capable of affecting the real environment.

Hierarchical Memory System

This is one of Claude Code's most elegant designs. Its memory system is divided into three levels:

• Session Memory: Short-term memory within the current conversation context
• Project Memory: Project-level long-term memory stored through configuration files like CLAUDE.md
• Global Memory: Cross-project user preferences and general knowledge

Among these, CLAUDE.md is Claude Code's original project memory mechanism—essentially a Markdown-format configuration file placed in the project root directory. The Agent automatically reads this file at each startup to obtain long-term knowledge about the project's tech stack, coding conventions, architectural agreements, and common issue handling approaches. This design borrows from the software engineering convention of README.md, but the intended reader shifts from human developers to AI Agents. Its elegance lies in: it makes explicit and persistent the implicit knowledge that would otherwise be lost when a session ends, enabling the Agent to maintain consistent behavioral patterns across sessions. This approach of "using the file system as external memory" also aligns with the "Extended Mind" theory in cognitive science.

This hierarchical design allows the Agent to maintain contextual consistency at different granularities, completely eliminating the "goldfish memory" pain point of traditional AI assistants.

Multi-Agent Collaboration System

When facing complex tasks, a single Agent's capabilities are often insufficient. Claude Code supports multi-Agent collaboration mode, decomposing large tasks into sub-tasks assigned to different sub-Agents for parallel processing. The main Agent handles task orchestration and result integration, while sub-Agents each handle their respective responsibilities.

From an architectural design perspective, this adopts a design pattern similar to the "Orchestrator-Worker Pattern." The main Agent (Orchestrator) is responsible for understanding the user's high-level intent, formulating task plans, breaking complex tasks into independent sub-tasks, and integrating results after sub-tasks are completed. Sub-Agents (Workers) execute specific tasks in independent context environments, each with their own tool sets and context windows. The advantages of this architecture are: first, it breaks through the context window limitation of a single Agent, as each sub-Agent only needs to focus on its assigned module; second, sub-tasks can be executed in parallel, significantly improving processing speed; third, task isolation reduces the risk of error propagation. This pattern has deep theoretical foundations in distributed system design—it essentially applies microservice architecture thinking to the AI Agent domain.

This architecture shows very significant efficiency improvements when handling complex engineering projects.

Permission and Security System

As a tool that can directly manipulate file systems and execute commands, security is paramount. Claude Code has designed fine-grained permission control mechanisms, allowing users to precisely control which directories the Agent can access and which commands it can execute, preventing risks from accidental operations at the source.

Context Management System

A large model's context window refers to the maximum text length the model can process in a single inference, typically measured in token count. Claude 3.5 Sonnet's context window is 200K tokens, which is already very large but still insufficient when dealing with large codebases. A medium-sized project might contain hundreds of thousands of lines of code, far exceeding context capacity.

Claude Code maximizes information utilization within a limited token budget through intelligent context compression and management strategies, ensuring the Agent doesn't lose critical information during long-running tasks. Specific strategies include: intelligent summary compression (compressing processed conversation history into concise summaries), on-demand loading (reading relevant files only when needed rather than loading all code at once), and context priority ranking (ensuring the most critical information always remains within the window). The combination of these strategies enables the Agent to efficiently handle tasks far exceeding its capacity within limited "working memory."

Extended Tools System (MCP Protocol)

Through the MCP (Model Context Protocol), Claude Code can connect to external tools and services such as database queries, API calls, and browser operations, significantly expanding the Agent's capability boundaries.

MCP is a standardized protocol open-sourced by Anthropic in late 2024, designed to establish unified specifications for interactions between large models and external tools/data sources. Before MCP, every AI application needed to write dedicated integration code for each external service, resulting in massive duplicate work and a fragmented ecosystem. MCP defines standard tool description formats, invocation protocols, and response specifications—similar to the HTTP protocol in the Web domain—it doesn't concern itself with specific tool implementation details, only specifying the "language" of communication. Currently, MCP has gained support from mainstream AI programming tools including Cursor, Windsurf, and Cline, and is becoming the de facto standard for Agent tool invocation. Developers only need to write an MCP Server once for it to be callable by all MCP-supporting Agents.

This also means developers can flexibly customize the Agent's toolbox according to their own needs, building intelligent agent systems truly suited to their business scenarios.

Industrial-Grade Practice: Building an AI Assistant from Scratch

Theory must ultimately be grounded in practice. Through a complete industrial-grade practical project, the knowledge of the six systems described above can be integrated and applied.

Key Steps in Practical Development

A typical practical workflow includes:

1. Requirements Analysis: Describe project requirements in natural language so the Agent understands the objectives
2. Architecture Design: The Agent automatically plans the technical architecture and file structure based on requirements
3. Iterative Development: Gradually refine features through multiple rounds of conversation, with the Agent automatically writing, testing, and debugging code
4. Project Memory Configuration: Set up the CLAUDE.md file to let the Agent remember project specifications and tech stack preferences
5. Multi-Agent Collaboration: For complex modules, enable multi-Agent parallel development mode

Throughout this process, the developer acts more like a "technical director," responsible for maintaining direction and reviewing quality, while the actual coding work is delegated to the Agent. This collaboration model is redefining the software development workflow. It's worth noting that this doesn't mean developers can completely step back—understanding architecture, precisely describing requirements, and judging code quality—these "meta-abilities" become even more important in the Agent era. Agents amplify human judgment and decision-making capabilities rather than replacing them.

Conclusion: Understanding Architecture Matters More Than Writing Code

Claude Code and its alternatives represent the cutting edge of AI-assisted development. Its core value lies not only in improving coding efficiency but also in the Agent architecture design philosophy it demonstrates—hierarchical memory, multi-Agent collaboration, tool invocation, permission management—these are all foundational paradigms for building next-generation AI applications.

For domestic developers, experiencing this architecture at low cost through alternative solutions and understanding its design philosophy is worth investing time to study deeply, whether for actual development or learning Agent design principles. In the Agent era, understanding architecture matters more than writing code.