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Editorial snapshot from September 2026. Prices, models and interface labels may change; this is a practical guide, not a live product feed.
TRAE’s professional AI work assistant, accessible across desktop, web and mobile.
AI work assistant · TRAE / ByteDance · macOS · Windows · Web
Compare TraeWork and TraeCode , or visit the official download guide .
Suitable workflows
Task-oriented work where a conversation and deliverable matter more than navigating source files.
Limitations and billing
Check whether your task needs a full IDE, debugger or extension before replacing your development environment.
Capabilities we verified
Agent workflow · Cloud agents · Parallel agents
This is an evidence-based shortlist, not an exhaustive feature inventory. Omitted capabilities have not been checked for this record.
Pricing & usage
Paid from $20/mo
TRAE membership + metered usage. The cost of metered usage or an external agent can be separate from the subscription.
Models & access
Model access depends on the selected plan, region and current product surface.
Project evaluation checklist
- Open a small, familiar project and confirm that your essential language tooling works.
- Give the agent one bounded change with an observable acceptance criterion.
- Review the diff, run the project’s checks and compare the result with your current workflow.
- Inspect usage after the task. Estimate a month of similar work, including any separate model or agent bill.
A complete first task: change, verify and review
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Choose a small repository that already builds. Save a clean Git checkpoint and write down the command that currently passes. This gives you a baseline for distinguishing an existing problem from a generated regression.
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Describe one observable result: for example, add a required field to an existing form and show an error without submitting invalid data. Include the relevant files, existing validation helper and the behavior that must stay unchanged.
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Ask for a short plan before editing. Check that it names the affected components, data flow and verification command. Resolve missing context before allowing a broad refactor.
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Review the diff in small batches. Check dependencies, generated files, environment variables and error handling as well as the visible result. Run the project checks and exercise both the successful and failing paths.
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Inspect the usage record after the task. Record the accepted outcome, review time and usage consumed. Repeat on a second representative task before choosing a paid plan or moving a team workflow.
A task brief you can adapt
Goal: describe the user-visible change.
Context: list the relevant files and existing implementation.
Constraints: keep the public API and use the existing dependencies.
Verification: run the repository's documented checks and test the failure path.
Completion: summarize changed files, checks performed and remaining limitations.When the result is not usable
A successful request does not prove the code works. If the agent edits the wrong files, reduce the scope and supply the entry point explicitly. If a command fails, reproduce it in the same terminal environment before changing the prompt. If an MCP connection fails, separate server startup, credentials and client configuration. If usage is exhausted, check the account balance and selected model before repeating the same request. Keep a failed patch small enough to revert without losing unrelated work.
Common questions before you switch
Does a paid subscription mean unlimited agent work?
No. Subscription price, included usage, model access and additional usage are different things. A plan with unlimited autocomplete does not imply unlimited model requests or cloud tasks.
Should I connect every MCP server at once?
Start with the one integration needed by your current task. Confirm that it starts, exposes the expected tools and receives only the intended project context before adding another.
How should I compare two AI editors?
Use the same repository, task and acceptance checks. Compare correct changes, review effort, setup friction and recorded usage. A long feature list does not establish which editor produces better code for your project.
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