What is TRAE?
TRAE is an AI coding assistant platform that helps developers write, debug, review, and test code across individual and team workflows. It covers the full development cycle, from AI-powered code generation and auto-fix to test creation and documentation indexing. TRAE is available as a standalone IDE, as a plugin for VS Code and JetBrains, and includes a mobile app for on-the-go use. An enterprise tier adds large-scale repo indexing, productivity dashboards, and flexible deployment options for teams of any size.
TRAE Video
Features & Benefits
- AI Code Generation: Generate code from natural language prompts directly inside the editor, reducing the time spent writing boilerplate and routine logic.
- Autocomplete: Predict and complete code at the line and block level as you type, informed by the context of your current file and repository.
- Cue-Pro Edit Predictions: Analyze repository-level edit sequences to surface multi-file change predictions and keep development in flow across complex codebases.
- AI Debugging and Auto-Fix: Detect errors and suggest or apply fixes automatically, cutting the back-and-forth between identifying and resolving issues.
- Code Review: Review code changes with AI assistance to catch logic errors, inconsistencies, and style violations before they reach production.
- Test Generation: Generate test cases from existing code, including support for bulk test creation and backfilling coverage across large codebases.
- Documentation Indexing: Index and connect project documentation so the AI coding assistant can reference it when generating or explaining code.
- SOLO Coding Agent: Run an autonomous agent mode that handles multi-step engineering tasks end-to-end, from reading requirements to writing and verifying code.
- TRAE Work: Access an AI work assistant product that extends beyond coding to tasks like literature reviews, research, and presentation generation.
- @Agent System with MCP: Define custom agents using rules and tools, and connect them via the Model Context Protocol for multi-agent collaboration on complex workflows.
- Context Management: Attach web sources, documents, and repository rules as context inputs so the AI coding assistant works with the most relevant information available.
- Max Mode: Enable extended context windows and additional tool calls for deep, multi-round work on complex or long-running engineering tasks.
- TRAE Rules: Set persistent project-level and team-level instructions that shape how the AI responds across every session.
- Multi-Model Support: Connect to frontier AI models or configure custom enterprise models depending on team requirements.
- Massive Repo Indexing: Index up to 100,000 files and 150 million lines of code with real-time incremental updates for enterprise codebases.
- Productivity Dashboard: Track AI code generation rate, AI-authored lines, and other engineering metrics in real time across an organization.
- Cost Controls: Set organization-wide spending caps and monitor token consumption as it happens.
- Usage Transparency: View member activation, activity levels, and tool usage across the entire team.
- Role-Based Access Controls: Manage permissions at the role level to prevent unauthorized access to codebases and AI features.
- Zero Data Retention: Encrypt code in transit and avoid storing it on TRAE servers at any point.
- No Model Training on Your Code: Keep code private by design, with no logs retained and no use of your data to train models.
What can TRAE do?
- Write code from a text description
- Generate unit tests for existing functions
- Fix bugs in code automatically
- Review a pull request with AI
- Complete code as you type
- Refactor code across multiple files
- Index a large codebase for AI context
- Generate test cases in bulk
- Run an autonomous coding agent on a multi-step task
- Connect a custom AI agent to external tools
- Track AI code contribution metrics for a team
- Set spending limits on AI token usage
Real-World Applications
Solo developers building production software may find the AI coding assistant handles large portions of routine implementation work. TRAE SOLO can take a task description and carry it through to working, tested code. Small founding teams have used this to build and ship full applications within weeks, without needing to scale headcount alongside the product.
Engineering teams working inside existing editors can add the AI coding assistant through the TRAE plugin for VS Code or JetBrains. Intelligent completion, auto-fix, and rule checking layer into the workflow developers already use. For teams with strict security requirements, the zero-retention model and SOC 2 certification make it viable to run AI-assisted development on sensitive codebases.
Organizations managing large-scale software systems can apply TRAE’s AI coding assistant capabilities to high-volume tasks like test backfilling, code migration, and large-scale refactoring. The productivity dashboard gives engineering leads visibility into AI adoption rates, code generation metrics, and tool usage across the team, which is useful for measuring the actual impact of AI on output.
Mobile development teams and developers who move between devices may use the TRAE mobile app to stay in context while away from a workstation. The TRAE Work product extends the AI assistant beyond code to research, document analysis, and content generation, making it applicable in situations where engineering and non-engineering work overlap.
