Connecting Claude Code to DeepSeek: A Guide to Setting Up an AI-Driven Testing Environment

Introduction

The field of software testing is undergoing a profound transformation—traditional manual testing and scripted testing are gradually being supplemented, and even replaced, by AI-driven intelligent testing.

The core pain points of traditional software testing lie in: test case design relying on individual experience, heavy repetitive workload in regression testing, and limited coverage in exploratory testing. AI-Driven Testing is changing this landscape on three levels—the first is automatic test case generation, where AI produces test scenarios based on requirement documents and code changes; the second is intelligent defect analysis, where AI assists in root cause identification through log analysis and pattern recognition; the third is adaptive test strategies, where AI dynamically adjusts test priorities and coverage based on historical defect data. According to Gartner's predictions, by 2027, over 40% of testing activities will be AI-assisted.

This article provides a detailed guide on how to use Claude Code connected to the DeepSeek large language model to set up an AI-driven testing development environment, helping beginners get started quickly.

What is Claude Code?

Core Positioning

Claude Code is a command-line programming tool (CLI Agent) developed by Anthropic. Unlike conversational AI tools such as DeepSeek or Doubao that most people are familiar with, Claude Code is a professional-grade Terminal Agent primarily designed for development and testing.

Anthropic was co-founded in 2021 by former OpenAI Research VP Dario Amodei and Daniela Amodei. The company's core philosophy is building safe, interpretable AI systems. The Claude model series employs a unique Constitutional AI training method, guiding model behavior through a set of explicit principles rather than relying solely on Reinforcement Learning from Human Feedback (RLHF). Claude Opus 4 is their latest flagship model, delivering outstanding performance on authoritative coding benchmarks like SWE-bench, with particular strengths in long-context understanding and complex code reasoning.

Additionally, Anthropic pioneered and promoted MCP (Model Context Protocol), which carries significant industry importance. MCP was officially open-sourced in late 2024, aiming to solve the fragmentation problem of connecting large models with external tools and data sources. Before MCP, every AI application needed custom integration code to interface with different tools (such as databases, file systems, and API services), resulting in high development costs and poor reusability. MCP defines a unified client-server architecture: AI applications act as MCP Clients, various tools and data sources act as MCP Servers, and both sides communicate through standardized JSON-RPC messages. Similar to how the USB protocol unified hardware interfaces, MCP is becoming the universal connection layer for the AI tool ecosystem. Choosing Claude Code as an Agent tool is based precisely on its powerful underlying technology and ecosystem influence.

Two Major Categories of Agent Tools

Before understanding Claude Code, it's necessary to understand the two major categories of Agent tools, as this directly affects your technology selection in real projects.

Terminal Agent

• Runs in the command-line terminal
• Manages work on a per-project/directory basis
• Users maintain the highest decision-making authority over the entire device
• AI can only work within the specified project scope
• High security, suitable for enterprise environments

Representative tools: Claude Code, Codex (OpenAI), Gemini CLI (Google), Open Code, Open Claude (open-source solutions)

The core operating mechanism of Terminal Agents is the "perceive-plan-execute" loop. When a user enters a natural language instruction in the terminal, the Agent first perceives the current project's contextual environment (including directory structure, code files, configuration information, etc.), then sends the user's intent along with the context to the backend large model for reasoning and planning. The large model returns specific operational steps (such as creating files, modifying code, executing commands, etc.), and finally the Agent executes these operations step by step in the local environment. Throughout the process, critical operations typically require user confirmation before execution—this is the so-called Human-in-the-Loop mechanism, ensuring humans always retain ultimate decision-making authority.

Device Agent

• Interacts through IM tools (WeChat, Feishu, DingTalk, etc.)
• Takes over full permissions of the entire device
• Low barrier to entry, but higher risk
• More suitable for personal use; not recommended for enterprise project deployment

Representative tools: Open Crab, Elmas

Why Choose Terminal Agent Over Device Agent?

Enterprise-Level Security Considerations

In enterprise environments, a computer itself is an asset that cannot be casually handed over entirely to AI. The design philosophy of Terminal Agents is: hand over a directory or project on the computer to AI for processing, while humans always maintain overall control.

The core advantages of this approach are:

1. Controllable permissions: AI can only access the specified project directory and cannot touch system files or other sensitive data
2. Multi-project management: The same computer can have different Agents and parameters configured for different projects
3. Risk isolation: Even if AI makes an operational error, the impact is confined to a single project

Risk Warnings for Device Agents

Although Device Agents are convenient to use, they present obvious security risks. Multiple cases have demonstrated that issues such as AI accidentally deleting system files causing system crashes and confidential data leaks occur from time to time. National security departments have also issued relevant warnings, recommending caution when using such tools in company projects.

For test engineers, daily work involves handling large amounts of business data and system configurations. Choosing a Terminal Agent with controllable permissions is clearly the safer option.

Complete Claude Code Environment Setup Process

Step 1: Install Node.js

Claude Code depends on the Node.js runtime environment. If your computer already has Node.js installed, you can skip this step.

Installation key points:

1. Visit the Node.js official website (nodejs.org)
2. Click the download button—the website will automatically detect your operating system and platform architecture
3. Choose the "Installer" method; Docker deployment is not recommended
4. After downloading, double-click to run and follow the prompts to complete installation

Why not use Docker? Docker is currently the most mainstream containerization technology, packaging applications and their dependencies into independent container images to achieve "build once, run anywhere." However, for Agent tools like Claude Code that need deep access to the local file system, containerization actually introduces additional complexity. Docker containers run in an isolated file system by default, and accessing host files requires Volume mounting, which not only adds configuration steps but may also cause compatibility issues with file permissions, path mapping, and file watching. Therefore, installing directly on the operating system (i.e., bare-metal deployment) avoids these problems and provides a smoother user experience.

Step 2: Install Claude Code

After Node.js installation is complete, open a terminal (Windows users can use PowerShell or CMD) and install Claude Code globally via npm:

npm install -g @anthropic-ai/claude-code

Once installed, navigate to your project directory and type claude to launch it.

Step 3: Connect to the DeepSeek Model

Claude Code uses Anthropic's own Claude model by default, but you can connect to third-party models like DeepSeek by configuring environment variables.

DeepSeek is a large language model series developed by DeepSeek AI. Its technical approach has several notable characteristics: first, it adopts a Mixture of Experts (MoE) architecture, activating only a portion of parameters during inference to significantly reduce computational costs; second, its training data includes large-scale, high-quality Chinese corpora and code data, giving it excellent performance in Chinese understanding and code generation; third, DeepSeek offers open API access with extremely competitive pricing—input token prices are only a fraction of mainstream international models.

Choosing DeepSeek as the backend model offers several practical benefits:

• Lower cost: DeepSeek's API pricing is more affordable compared to Claude's native model, suitable for daily high-frequency use
• Faster access: In domestic (Chinese) network environments, DeepSeek's response speed is noticeably better than overseas models
• Strong Chinese capabilities: Excels in scenarios like Chinese test case generation and defect description

The specific connection method requires configuring environment variables to point the API endpoint to DeepSeek's service address and entering the corresponding API Key.

Terminal Agent vs. Device Agent: Technical Selection Comparison

Dimension	Terminal Agent (Claude Code)	Device Agent (Open Crab, etc.)
Usage method	Command-line terminal	IM chat tools
Permission scope	Single project/directory	Entire device
Security	High	Relatively low
Applicable scenarios	Enterprise project development & testing	Personal assistant
Learning curve	Medium	Low

As shown in the table above, if your use case involves enterprise-level automated testing or project development, Terminal Agents have clear advantages in security and controllability.

Final Thoughts

The Claude Code + DeepSeek combination balances tool professionalism with model cost-effectiveness, making it one of the mainstream technical solutions for AI-driven testing today. For test engineers, mastering this toolchain not only improves daily work efficiency but is also an important skill investment for staying competitive in the AI era.

In future articles, we will dive deeper into practical applications such as automated test case generation, test script writing, and defect analysis using this environment. Stay tuned.