- Main Whitepaper - Core concepts and architecture
- 10 Best Tips - Quick practical tips for immediate implementation
- SOLO + GLM 4.7 Best Combo - Deep dive into cost optimization
- 📢 Get 10% Discount on GLM 4.7 - Use this referral link for 10% off GLM 4.7 Coding Plan
- Agents Guide - Detailed guide to specialized agents
- Rulesets Template - Ready-to-use ruleset examples
- TRAE-Agents Collection - Production-ready AI agents for TRAE ecosystem
This whitepaper 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 from aicompar.com and llmarena.ai.
I detail how TRAE becomes a real AI Engineering Team, how I orchestrate multi-agent execution, and how leveraging GLM 4.7 (z.ai) inside TRAE dramatically reduces cost while increasing output efficiency — resulting in a setup up to 10× more cost-effective than standard usage of TRAE credits.
This best practice is based on real production workflows I use daily as a full-stack developer and team lead.
As a developer handling full projects end-to-end — backend, frontend, APIs, system design, cloud, and debugging — I needed an environment capable of:
- Replacing repetitive tasks
- Assisting with complex refactoring
- Debugging large codebases
- Designing architectures faster
- Acting consistently based on rules
- Allowing multiple specialized AI agents
- Reducing model cost without losing performance
TRAE provided the perfect operational layer, but its real power emerges only when paired with:
- Custom Agents
- Strict Rulesets (for consistency and deterministic behavior)
- External model benchmarking
- Cost-performance optimization
- Third-party API models integrated into TRAE
This whitepaper explains the entire ecosystem.
My workflow is built on four pillars:
I created a collection of TRAE Agents, each with a defined responsibility:
- Architect Agent → system design, diagrams, patterns
- Frontend Agent → React/Next.js, Tailwind, UI flows
- Backend Agent → Node.js, PHP, Python, APIs, services
- Debugger Agent → log analysis, error deduction, patch generation
- Refactor Agent → restructuring, dependency analysis
- Documentation Agent → READMEs, API docs, comments
Each agent follows strict rules that ensure:
- Deterministic responses
- No hallucinations
- No rewriting of working code unless specified
- Predictable formatting
- Compliance with the scope of the task
TRAE handles agent-to-agent context sharing and file operations, acting like a real AI engineering team.
I maintain a centralized Rule System that defines how agents behave:
- Output formatting rules
- Language constraints
- Forbidden behaviors
- Mandatory checks (linting, security, best practices)
- Step verification before approving code
- “If uncertain → ask for clarification”
- Use of chain-of-thought internally but not exposed in output
These rules provide stability and remove randomness.
Before assigning a model to each agent, I run objective benchmarks:
- aicompar.com → high-level comparison, outputs, reasoning quality
- llmarena.ai → competitive leaderboard, coding tests, stress tests
These platforms allow me to compare:
- speed
- intelligence
- factual accuracy
- coding reliability
- API latency
- output stability
The goal is simple:
Assign the best model to each agent role, not just one “good model” for everything.
Example:
- Debugger Agent → model with high reasoning depth
- Frontend Agent → model with stable code generation + layout consistency
- Architect Agent → model strong in reasoning and planning
This is one of the core insights of my workflow.
TRAE internally uses its own credit system — and heavy tasks can consume 200+ credits quickly.
However:
Use TRAE with external API keys, especially:
- SOLO architecture in TRAE
- paired with
- GLM 4.7 (z.ai) purchased via the Coding Plan (using my referral link)
This brings 3 benefits:
- perfect structure for long workflows
- ideal for iterative development
- stable formatting
- predictable agent responses
Comparable to:
- GPT-4.1
- Claude 3.7
- DeepSeek R1
But at a much lower cost.
Using TRAE credit system:
A single heavy refactor can burn 200–350 credits.
Using GLM 4.7 API:
The same task costs up to 10× less, with stronger performance.
Result:
TRAE (SOLO) + GLM 4.7 API = Best performance/cost ratio available in 2025.
This single optimization alone increased my effective productivity dramatically.
Below is a concrete workflow example I use internally.
Using aicompar.com + llmarena.ai:
- Compare the top 5 models for the needed task
- Choose the best (usually GLM 4.7 for code-heavy work)
Open the repository inside TRAE:
- Files synced
- Agents activated
- Rules applied
Example:
- “Backend Agent: analyze src/routes/auth.js and refactor it following the security ruleset #SEC-2”
The agent:
- reads the file
- suggests improvements
- applies changes
- writes code
- validates with the Debugger Agent
- waits for confirmation
I run:
- security agent
- documentation agent
- formatting agent
- integration agent
This ensures:
- structure
- quality
- clarity
- maintainability
I approve or request changes.
TRAE handles everything like a senior dev team.
By combining TRAE Agents + Rules + SOLO + GLM 4.7 API:
- 4× faster development time
- 10× cheaper than standard TRAE credit system
- 70% fewer manual debugging hours
- Rulesets ensure identical output formatting every time
- Zero hallucinations during code generation
- cleaner architecture
- predictable file structure
- fewer bugs at runtime
The multi-agent system gives me the equivalent power of:
- 1 architect
- 1 backend dev
- 1 frontend dev
- 1 debugger
- 1 documentation engineer
Working simultaneously.
🔥 TRAE Cost & Performance: The Hidden Pitfalls Nobody Talks About
If you're using TRAE without considering these critical bottlenecks, you're likely burning through credits 10× faster than necessary and missing out on massive productivity gains.
The Reality: A single heavy refactoring task inside TRAE can consume 200-350 credits in one shot.
What Most Developers Don't Know:
- TRAE's native credit system is extremely expensive for large tasks
- Each multi-step agent task burns credits exponentially
- Context window mismanagement can triple your costs
The Solution: This is exactly why the SOLO + GLM 4.7 combo works—GLM 4.7 API costs up to 10× less for identical coding tasks while maintaining superior performance.
User Reports: Many developers report:
- Initial queue delays lasting several hours before processing begins
- "High traffic" interruptions after 1-2 hours of processing
- Forced re-queuing for the same task, wasting time and credits
- Tasks marked as "nearly unusable" due to interruptions
Why It Happens: Scalability limitations during peak demand periods; TRAE's architecture struggles with large repository context windows.
The Issue: When working with large codebases, TRAE can only "see" a limited portion of your repository at once.
- AI agents miss critical relationships between files
- Refactoring suggestions break other parts of the codebase
- Context limitations force you to break large tasks into tiny chunks (more credit burn!)
Impact: A task that should take one run becomes 10+ iterations, multiplying costs.
What Users Report:
- Recent model performance has deteriorated compared to launch
- Simple prompts that used to work flawlessly now fail
- Responses feel slower and context is often ignored
- Developers need extreme specificity in prompts to get decent results
The Real Issue: Without external model benchmarking and proper agent configuration, you're left with unpredictable output quality.
The Problem: When facing intricate challenges like SQL migrations, TRAE produces:
- Incomplete or broken code
- Monorepo structures with only landing pages (no actual backend)
- Persistent "end of context" errors
- 138+ errors per run that require manual fixing
Why: Complex tasks require deterministic agent behavior and proper ruleset enforcement—not available by default.
Common Gaps:
- No built-in cost tracking per agent
- Limited external model integration
- No automatic model selection based on task type
- Rulesets not enforced across multi-agent workflows
This TRAE-Tips collection documents proven solutions to every problem listed above:
- SOLO + GLM 4.7 Best Combo → Cuts costs 10×
- Agents Guide → Prevents context window mismanagement
- Rulesets Template → Enforces deterministic output
- 10 Best Tips → Quick optimizations for immediate ROI
Result: Developers using these strategies report:
- ✅ 4× faster development
- ✅ 90% fewer credit wastage
- ✅ Zero hallucinations during code generation
- ✅ Team-level productivity from a single person
Event: 2025 TRAE Global Best Practice Challenge
Link: https://bytedance.larkoffice.com/share/base/form/shrcngbw4403LyFOD2bdEICRkE3
Author: Marco
Date: 17 December 2025
This document summarizes how combining TRAE’s multi-agent capabilities with an optimized model strategy enables a single developer to reach the output of an entire software team — with higher consistency, lower cost, and faster delivery.
This repository has been expanded with supplementary documentation for deeper learning:
Quick, actionable tips extracted from the whitepaper. Perfect for:
- Quick reference before starting a session
- Checklist format for workflow optimization
- Implementation shortcuts
Comprehensive deep-dive on the most cost-effective TRAE setup:
- Detailed SOLO architecture explanation
- GLM 4.7 benchmarks and comparisons
- Step-by-step integration guide
- Cost analysis and ROI calculations
- Real production metrics
Detailed reference for building and managing AI agents:
- Agent role definitions and responsibilities
- Prompt templates for each agent type
- Context management strategies
- Agent-to-agent communication patterns
- Scaling from single to multi-agent systems
🔗 For ready-to-use TRAE agents, visit: TRAE-Agents Repository
Ready-to-use rule templates and system prompts:
- Security ruleset (SEC-1, SEC-2, etc.)
- Quality assurance rulesets
- Output formatting rules
- Custom rule creation guide
- Rule versioning and management
- First time? Start with 10 Best Tips for quick wins
- Ready to optimize costs? Read SOLO + GLM 4.7 Best Combo
- Building a team? Reference Agents Guide
- Need consistency? Use Rulesets Template
- Want the full story? Read this whitepaper top-to-bottom
Want to know the difference between Agents, Skills, and Rules?
👉 Check out Agents VS Rules VS Skills.
Looking for a collection of Agents for TRAE?
👉 Read our TRAE Agents.
Looking for some tips on TRAE?
👉 Read our TRAE Skills.