Published: 2026-07-22

Plan, Build and Ship a Full-Stack App With Multi-Agent AI in TRAE IDE

Chapters / key moments (click to jump — plays here on the page)

Anya Kubow takes a habit-tracker app from a single sentence to a deployed full-stack product inside TRAE IDE, spending the first stretch on planning rather than code. The walkthrough covers generating a product requirements document from a plain-English prompt, trimming it to an MVP, building a custom brand-aware agent, then running two agents in parallel — one restyling the front end while the other adds a Node/PostgreSQL back end — before deploying to Vercel.

Source video

"AI-Assisted Development – Multi-Agent Coding & Deployment with TRAE IDE" by freeCodeCamp — Watch on YouTube →

Disclosure

freeCodeCamp states in the video that TRAE provided a grant to fund the course. The workflow shown is still reproducible, but treat the tool choice as sponsored rather than as a recommendation over alternatives.

Key Takeaways

Plan before you generate. The first prompt asks the AI to turn a one-sentence idea into a software specification rather than to write code. TRAE returns a product requirements document with core features, a suggested stack (React + TypeScript + Tailwind front end, Node/Express + PostgreSQL back end), user stories, a database design, API endpoints and a phased roadmap.
Cut scope conversationally. Instead of accepting the first PRD, Kubow asks the agent what is realistic for version one. It strips the plan back to an MVP — simple auth, habit management, daily tracking, streaks, weekly stats, no back end initially — which is what actually gets built first.
Two development modes. IDE mode behaves like a traditional editor with AI assistance and per-change review. Solo mode is the autonomous path where you describe a goal and the agent plans, codes, tests, previews and prepares deployment. Multi-agent work requires solo mode.
Custom agents are configured, not coded. A "brand UI developer" agent is created from a plain description of the brand palette; TRAE generates the agent's name, prompt and settings. That agent then handles all styling work while a separate built-in execution agent handles the back end — both running as concurrent tasks.
MCP extends the agents. TRAE supports Model Context Protocol servers, letting agents reach custom APIs, databases, documentation and internal tooling beyond code generation. See our MCP glossary entry for how the protocol works.
Safety defaults worth checking. Privacy mode keeps chats and snippets out of model training and leaves project files local; AI-generated terminal commands can be run in a sandbox. Both are opt-in settings, not assumptions — verify them before pointing an agent at real work.

The build sequence shown

Install TRAE IDE (macOS 12+, Windows 10/11 64-bit, or Linux x64/arm64 via Debian/RPM), optionally importing existing VS Code or Cursor settings.
Describe the app in the AI panel and ask for a software specification; approve or refine the generated PRD.
Ask for a task list to get a phased, step-by-step development plan.
Scaffold the front-end MVP with local storage — roughly 110 generated tasks, reviewed change by change.
Create a custom agent for brand styling; switch to solo mode.
Run the styling agent and a back-end agent as two parallel tasks.
Deploy through the Vercel integration (requires an existing Vercel account and granting read/write permission).

What you can actually set up from this

Extracted from the video's own transcript — the specifics the original summary left out.

Configuration shown

Planning prompt used (typed into the AI panel)

I want to build a modern full-stack habit tracker. Users should be able to register, log in, create habits, mark them as complete each day, view their streaks, and see weekly statistics. Help me turn this into a software specification.

Brand agent prompt (Settings > Agents > create)

A front-end developer agent that works with my brand colors of green, base navy, accent blue, highlight orange, error red; other colors are allowed sparingly when a subject needs them. Generated name: brand UI developer.

Platform support stated in the video

macOS 12 or later (Apple silicon and Intel), Windows 10 and 11 64-bit, Linux x64 and arm64 via Debian and RPM packages. Older macOS needs an earlier release.

Template shown in the video. Adapt to your project — do not copy verbatim without reviewing each line.

Reproducible steps

1. Install and open a project
Download TRAE IDE from the TRAE website, finish the first-run setup (language, theme, optional import of VS Code or Cursor settings, optional command-line shortcuts), sign in, then open a folder or clone a Git repository.
2. Plan before coding
Describe the app in the AI panel with the prompt above. TRAE creates a project folder and a product requirements document (here React with TypeScript, Tailwind and React Context on the front end; Node.js, Express and PostgreSQL on the back end) and asks you to approve it. He then asked what is realistic for version one, accepted an MVP scope (simple auth, habit management, daily tracking, streaks, weekly stats, no back end yet), and asked for a task list broken into five phases.
3. Build the MVP
Ask it to scaffold the app with React, Tailwind and local storage only. It created folders and files, installed dependencies, started the dev server and previewed the app in the IDE after about 110 tasks. Suggestions can be reviewed, rejected or edited by hand; its Q completion feature predicts edits and handles imports and renames.
4. Two modes
IDE mode is a normal editor with AI help while you stay in control. Solo mode lets you describe a goal and the AI plans, codes, tests and previews. Multi-agent runs only work in Solo mode.
5. Run agents in parallel
Settings > Agents has two built-in agents (chat with the codebase; end-to-end task execution). He created a custom brand UI agent, then in Solo mode added two tasks at once: refine the app to the brand colours with the custom agent, and build a Node.js and PostgreSQL back end with the end-to-end agent. Both finished (shown sped up). He then signed up again because the app moved from local storage to a database.
6. Deploy
Switch back to Solo mode and ask TRAE to deploy. It checks environment variables and configuration, then used a Vercel integration (requires a Vercel account and granting read and read-write permissions) and returned a live URL.

Gotchas

Sponsored: the video says TRAE provided a grant for the course.
Privacy mode (stated in the video) keeps chats and code out of model training; AI-generated terminal commands can run in a sandbox.
TRAE supports MCP servers, so agents can reach custom APIs and databases.
The agent runs were sped up in the recording, so real build times are not shown.