AI Coding Tools in China 2026: From Smart Generation to Full-Stack Delivery

1. The Rise of Domestic AI Coding Tools: Background and Value

By 2026, the domestic AI coding tool market has entered a phase of widespread diversification. As large model technology continues to evolve and computing costs decline, intelligent coding assistants are no longer just code completion plugins. They have evolved into full-stack development partners capable of requirement understanding, architecture design, code generation, and debugging and repair. For domestic developers, tools that can be used smoothly without network acceleration are a hard requirement—whether due to data compliance requirements, access stability, or consistency of user experience, localized tools demonstrate significant advantages.

In terms of product form, current mainstream domestic AI coding tools are generally deeply integrated into IDEs or provide standalone clients, supporting conversational requirement description, multi-file project generation, and automatic error repair. Some tools have also introduced multi-agent collaboration mechanisms for parallel processing of tasks across different modules. The common goal of these tools is to lower the development barrier, enabling individual developers or small teams to complete full delivery from frontend pages to backend services with fewer resources.

2. Practical Validation: An Evaluation Framework for Building an E-Commerce Site from Scratch

To objectively evaluate the actual performance of each tool, I designed a unified evaluation task: using React to build a complete frontend e-commerce site from scratch, requiring 10 pages in total, covering the core user shopping flow and account management modules.

2.1 Page Requirements List

• Home page (product listing page): display product cards, category filter entry, recommendation slots.
• Product detail page: product images, specifications, price, inventory, and add-to-cart button.
• Product purchase page: order confirmation, shipping address, payment method selection.
• Purchase success page: order number display, countdown return-to-home logic.
• User login/registration page: form validation, password encryption storage indication, navigation logic.
• User center - dashboard: statistics cards, recent order summary.
• User center - orders page: order list, status labels, detail entry.
• User center - wallet (coupon package): coupon list, available/unavailable status.
• User center - settings: personal profile editing, preference settings.

2.2 Core Evaluation Dimensions

• Site completeness: whether pages are complete and routing is coherent.
• Overall consistency: UI style uniformity and interaction pattern uniformity.
• Code quality: rationality of component splitting, style organization approach, data flow clarity.
• Build success rate: whether the project can run directly after one generation, error rate, and repair cost.

This evaluation framework can comprehensively reflect the productivity level of AI tools in real business scenarios.

3. Hands-On Testing and In-Depth Analysis of Representative Tools

3.1 Tencent CodeBuddy (Domestic Version): Balancing Interface Affinity and First-Pass Completion Rate

In actual testing, the tool’s interface design left a strong impression—warm, intuitive, and guiding, shedding the cold engineering feel of traditional development tools. Its unique working mode can complete the entire process from requirement parsing to project generation in one pass, reducing the number of repeated iterations. In terms of server response, thanks to domestic infrastructure deployment, the entire generation process felt nearly latency-free.

Generated Output Analysis

The generated interface leans toward a mobile-style design but has basic responsive adaptation capability. It fully completes the core loop of “browse products - view details - place order - success redirect” and includes basic form validation logic (such as required field checks and email format validation). The overall visual uses brand blue as the primary color, simple and practical.

Code Quality Assessment

At the code level, styles and logic are separated with clear structure. However, the project architecture is relatively simplified and does not reflect modular design for complex applications; mock data is directly coupled in business code, and replacing it with real APIs later requires a certain amount of refactoring work. Overall, it is suitable for rapid prototype validation.

3.2 ByteDance Trae (Domestic Version): Trade-Offs Between Complete Workflow and Post-Generation Repair Cost

ByteDance Trae clearly inherits the mainstream AI IDE interaction paradigm in terms of operation habits, while also providing a privacy mode (suitable for sensitive project development). Its Builder mode follows a relatively conservative development strategy, not generating too much code at once but building step by step, which has two implications: on one hand, each generation is more controllable in logic; on the other hand, overall completion speed is slower and requires sufficient patience.

Generated Output Analysis

The project generated by this tool has a complete form validation system, and the product detail page has better information density than other tools. However, the overall presentation of the project is relatively rough: missing mobile adaptation causes page element misalignment, some buttons have a bug where white text appears on a white background due to color scheme issues, and all image resources use placeholders. The visual aesthetics lack a unified style definition, requiring significant time for post-generation refinement.

Code Quality Assessment

In terms of code structure, logic and styles are separated but not split into independent files, leaving room for organization. The project reserves directory structures such as hooks and context, but the interiors are not filled with actual content—more like a template framework. Developers need to supplement state management logic themselves.

3.3 Baidu Comate: Multi-Agent Parallelism and the Art of Mode Selection

Baidu Comate provides multiple Agent modes for users to choose from, which increases decision cost to some extent but offers greater flexibility. Zulu mode emphasizes development speed and is suitable for quickly producing runnable versions; Architect mode claims stronger complex task processing capability and is suitable for scenarios requiring in-depth architecture design.

Differences in Actual Testing

Zulu mode does have an advantage in generation speed, but the project structure is poorly standardized—there is an issue of repeatedly creating directories with the same name within folders, ultimately causing the project to fail to start properly. Although Architect mode responds slightly slower, the generated project can run, though several errors remain. After multiple attempts at automatic repair, residual issues persisted, and I ultimately had to abandon it. This phenomenon reflects that current AI tools still require human intervention for container and dependency configuration validation when handling complex dependency relationships.

4. Selection Recommendations and Usage Strategies

Based on the above hands-on testing, here are selection references:

4.1 Choosing Tools by Scenario

• For rapid prototype validation and emphasis on interface interaction experience: prioritize Tencent CodeBuddy (Domestic Version), which has a high first-pass generation success rate and is suitable for demos or internal tool development.
• For clear requirements on form validation and data interaction, with willingness to invest in post-generation repair time: consider ByteDance Trae (Domestic Version), whose “step-by-step generation” strategy is more stable at the logic level, but visuals and styles require additional refinement.
• For handling complex project architecture with multi-module collaborative development needs: Baidu Comate’s multi-agent mode provides more flexible options, but developers need clear architectural awareness to avoid code structure confusion caused by differences in generation strategies.

4.2 General Efficiency Improvement Strategies

1. Task decomposition: Do not attempt to have AI complete the entire project at once. Instead, decompose the project into three layers—“page components → module functions → API integration”—and generate and validate layer by layer.
2. Explicit constraints: Clearly specify technology stack versions, styling approaches (such as CSS Modules, Tailwind), state management libraries, etc., in prompts to reduce AI’s ambiguous judgments.
3. Hybrid programming: Combine AI generation with manual refactoring. For core business logic, it is recommended to write manually and use AI for unit test generation; for repetitive page components, AI can be trusted to complete them.

5. Future Outlook

By 2026, domestic AI coding tools have moved past the “usable” stage and are advancing toward “good and reliable.” Multi-agent collaboration, enhanced context memory, and private deployment capabilities are becoming new competitive focal points. However, there is still a gap before fully automated delivery of production-grade projects, especially in dependency management, deployment configuration, and performance optimization, where human intervention is still required. Developers’ core competitiveness will be reflected in two aspects: “how to describe problems more accurately” and “how to review AI output more efficiently.”

For team technology selection, it is recommended to adopt a “small steps, quick iterations” approach—trying AI coding tools in non-critical-path business first, accumulating experience and best practices, and then gradually expanding the scope of application. In the future, AI will not replace developers but will become an indispensable “digital member” of development teams.