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TRAE-Tips: My Advanced TRAE Workflow & Agent Engineering

🏆 Winner of the 2025 TRAE Global Best Practice Challenge

Author: Marco — Full-Stack Developer & AI Workflow Architect

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📌 Description

This repository documents the full engineering workflow I use to build, automate and scale software development tasks through TRAE, combined with custom-built AI Agents, strict Rule Systems, and an optimized model selection strategy based on performance benchmarks.

The core of this work is detailed in our Main Whitepaper, which explains how TRAE becomes a real AI Engineering Team, how to orchestrate multi-agent execution, and how leveraging GLM-5 (z.ai) inside TRAE dramatically reduces cost while increasing output efficiency.


📖 Detailed Guides

A complete comparison of models (Gemini, GPT, Kimi) and which one to choose for frontend, backend, or refactoring.

Comprehensive deep-dive on the most cost-effective TRAE setup using GLM-5 to save up to 100x on credits.

Ready-to-use rule templates and system prompts for deterministic agent behavior.

Quick, actionable tips for immediate implementation and workflow optimization.

Detailed strategies for saving tokens, reducing costs, and maximizing your requests.


📡 Platform Status & News

Everything you need to know about the transition from request-based to token-based usage.

Real-world technical data, model efficiency, and token burn analysis from intensive usage.

🤖 Model Availability

Latest info on the upcoming (and currently in internal testing) Linux release.


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About

TRAE-Tips is a curated collection of practical tips, best practices, and advanced insights for working efficiently with the TRAE AI ecosystem. It includes usage guides, optimization strategies, common pitfalls, and real-world workflows to help developers and power users get the most out of TRAE tools.

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