2026 In-Depth Review of Chinese AI Coding Tools: From Project Scaffolding to Engineering Implementation

In 2026, the landscape of Chinese AI coding tools has entered a highly competitive phase. While international AI coding assistants maintain a strong presence, domestic solutions have made significant strides in IDE interaction design, Large Language Model (LLM) capabilities, cloud resource scheduling, and alignment with specific business scenarios. However, there remains a distinct gap between “usable” and “truly effective” when these tools are applied to real-world business scenarios.

To evaluate their true capabilities, I designed a representative practical task: building a fully functional frontend e-commerce site from scratch using React. The requirement included 10 key pages: Homepage (product list), Product Details, Checkout Flow, Payment Success Receipt, User Login and Registration, and User Center (Dashboard, Orders, Wallet/Coupons, Settings). This was not a simple assembly of static pages, but a comprehensive test of site completeness, business logic closure, code maintainability, and UI consistency.

After several hours of in-depth testing, I gained a comprehensive understanding of four mainstream Chinese AI coding tools (from Tencent, ByteDance, Baidu, and Alibaba) that currently operate smoothly without requiring network proxies. This review sets aside benchmark spectacles and focuses directly on their actual performance when handling real development tasks.

1. Interaction Experience and IDE Foundation: The Starting Point of Efficiency

For developers, the IDE shell of an AI coding tool determines the “feel” of collaborating with the AI. In this dimension, the design philosophies of these tools differ significantly.

Tencent CodeBuddy left the deepest impression on me regarding interaction experience. Its interface design clearly focuses on user-friendly aesthetics, moving away from the cold, pixel-heavy feel of traditional development tools. The visual elements and layout are closer to modern lightweight applications, significantly lowering the psychological barrier for first-time users. In terms of operational feedback, its unique “Craft Mode”—as opposed to the common “Auto Mode” seen in the market—means that the AI does not run autonomously to completion. Instead, it tends to build the project skeleton step-by-step and modularly. This mode is highly friendly for understanding the AI’s construction logic and intervening for course correction when necessary.

ByteDance Trae chose a different path. Its interaction logic pays deep tribute to mainstream overseas products; for developers accustomed to Cursor or VS Code, the learning curve is practically zero. Its built-in privacy mode is a major plus for handling enterprise-sensitive code. In task execution, Trae heavily promotes the “Builder Mode,” which emphasizes starting from the overall architecture to resolve project dependencies and directory structures in one go. However, during testing, excessive automation initially brought up some unexpected errors, requiring developers to have a certain level of debugging patience.

Baidu Comate leans towards a “toolbox” mindset in its IDE experience. It integrates multiple specialized Agent entry points in the sidebar for on-demand invocation. This design grants developers finer control, but it also introduces a selection cost—faced with multiple agents, novices might be confused about where to start.

2. Task Execution: The Trade-off Between One-Shot Generation and Multi-Turn Repair

This is the core segment of the review, directly determining whether an AI tool can shoulder the responsibility from requirement to delivery.

1. Tencent CodeBuddy: A Stable and Complete “First Impression”
In Craft Mode, CodeBuddy’s performance was surprising. The site it generated in one go was end-to-end runnable without any fatal runtime crashes. The completed interface had a consistent style, with a cohesive color tone (brand-like blue) throughout, and featured basic responsive adaptation. Although the login and registration interfaces had slight stretching issues on mobile, it did not affect core functionalities.
Worth praising is that its code quality ranked among the top of the four. Styles and business logic were separated (though using CSS files rather than CSS Modules), and a clear component tree was constructed. However, the project architecture was relatively simple; the data layer was coupled within business components without using a state management library (like Redux or Zustand), leaving room for refactoring in subsequent feature iterations. The detail pages lacked rich information, and product images all used placeholders, indicating that it prioritized “process closure” over “visual richness.”

2. ByteDance Trae: High Potential but Requires “Secondary Development”
Trae’s Builder Mode encountered obvious errors during its first execution. The project could start, but the console threw errors, and UI elements were incompletely rendered. Only after several rounds of instruction-based fixes did the final effect emerge. Once successfully running, the completeness of its form validation logic was commendable, and the information density on the product detail page was higher than the former.
However, the problems were equally obvious: low UI completion, almost non-existent mobile adaptation, and visible UI bugs (such as white text on a white background making buttons invisible). The color scheme lacked an aesthetic baseline. At the code level, Trae did reserve directory slots for hooks and context, but the internal logic was empty, reflecting that the generated “skeleton” was adequate but the “flesh” was insufficient. The development speed was relatively slow, consuming a lot of the developer’s waiting time. Currently, the tool relies entirely on its self-developed LLM, offers only a free version, and has relatively limited computing resources.

3. Baidu Comate: Initial Exploration of Multi-Agent Collaboration
Comate’s multi-Agent mode diversified task processing paths. Its “Zulu Mode” was fast, but the generated project structure was chaotic, making a basic error of nesting folders with the same name in the same directory, which directly caused the project build to fail—the most fatal flaw. On the other hand, the “Architect Mode” attempted to handle complex tasks, but the server response was slightly sluggish and time-consuming.
In the final project output, the Architect Mode generated a runnable project, but the console still reported numerous error warnings. I tried to let it self-repair, but after multiple rounds of conversation, the issues remained unresolved, and I ultimately had to give up. This indicates that Comate still has a long way to go in multi-agent collaboration stability for large tasks. Nevertheless, its concept of separating task planning (Architect) from execution (Zulu) truly represents a future direction for AI coding.

3. Code Quality and Engineering Mindset: Determining Project Longevity

AI coding tools must not only “generate” but also “generate with quality.”

In terms of code readability, the output of all four tools was better than the average junior developer, but still fell short of a senior engineer’s standards. CodeBuddy and Trae both achieved separation of styles and logic, but Trae’s styles were not placed in independent files; instead, it used inline styles or Styled Components, which is slightly more intrusive.

From an engineering mindset perspective, none of them automatically introduced a data request layer (like axios/fetch wrappers) or a state management solution. When testing complexity rises, the lack of a robust data persistence solution becomes apparent. However, considering they only generated frontend static sites, this seems reasonable. For developers expecting AI to directly generate “production-ready” code, manual additions for advanced engineering configurations—such as route lazy loading, code splitting, and Error Boundaries—are still required. At this stage, AI tools serve more as “accelerators” rather than “replacements.”

4. Selection Recommendations and Final Thoughts

After this rigorous practical test, the following conclusions can be drawn:

If you pursue a zero-barrier stable experience and overall consistency, or need to demonstrate a Demo to non-technical stakeholders, Tencent CodeBuddy is currently the optimal choice. Its stability prevents embarrassing demo failures, and its interface is most likely to impress non-technical audiences.

If you value code logic completeness and subsequent fine-tuning space, and don’t mind investing time in debugging initially, ByteDance Trae has potential worth exploring. Its depth of thought at the logic level is profound; once its models iterate and initial bugs are fixed, it will be a strong competitor.

Baidu Comate’s multi-Agent concept is highly inspiring, but its execution stability needs polishing. It is recommended for developers to re-evaluate it after subsequent version updates; relying on it for complex core business development at its current stage carries a higher risk.

Overall, Chinese AI coding tools in 2026 have developed the “foundational capabilities” to complete medium-sized business loops. However, they currently resemble a “junior engineer with fast execution speed but lacking senior experience,” needing a knowledgeable architect for guidance. Forget the fantasy of AI completely replacing programmers; the most realistic and efficient usage philosophy today is to treat these tools as assistants that raise the baseline and release us from repetitive labor. A developer’s true core competitiveness remains rooted in understanding the business essence and strictly pursuing architectural quality.