Comparative Review of AI Code Editors: Trae, Cursor, and Augment (2025)

In this post the author shares personal experiences using various AI code editors, including early tools like Tongyi Lingma, MarsCode, and Copilot, and later tools such as Trae, Cursor, and Augment, highlighting how the perceived speed gains diminish over time.

The 2025 comparison focuses on three main editors: Trae, which offers a free tier but suffers from request limits and queuing; Cursor, which provides a short free trial, decent memory, and a useful agent auto‑run mode that shows code changes directly; and Augment, which after a recent trial feels much more polished, aligns closely with Cursor, and adds its own memory context.

Initially Augment performed poorly because the team prioritized SWE benchmark rankings, but they have shifted focus to user experience, resulting in faster alignment with Cursor and an “one‑shot” auto‑run experience.

The author compares the tools on feedback speed (Trae lags, Cursor and Augment similar, Augment can lag at night), functional details (Cursor shows code modifications in the chat, Augment requires manual clicks), cost (Trae free, Cursor and Augment have two‑week trials, Augment’s token limits are high), and MCP server support (Trae lacks it, Cursor and Augment support it).

Typical MCP tools used include Fetch, Tavily, Sequential‑thinking, and Software‑planning‑tool, with the author also writing a custom MCP server for local date‑time retrieval.

Scenario recommendations suggest that solo entrepreneurs can use any of the free tools to build simple web projects, while professional developers may prefer paid versions and treat AI as an “intern” using TDD to manage hallucinations.

The author’s own project is a Python‑based Markdown management system with over 8,000 lines of production code, 6,000 lines of tests, 86% coverage, and integrations with WeChat API and OpenRouter service; all comments were generated by AI.

Productivity tips include using Alfred on macOS to store frequently used prompts (e.g., run all tests, break down tasks, commit code) and leveraging Markdown documents to track progress, as well as employing voice input via the microphone for faster typing.

Finally, the author emphasizes the importance of TDD to contain AI hallucinations, advising developers to intervene when AI changes production code to satisfy tests, and to use clear directives to guide the AI’s behavior.