Code Completion

Trae IDE's intelligent code completion system leverages AI to provide context-aware suggestions that accelerate your coding workflow. Unlike traditional autocomplete, Trae's AI understands your code context, project structure, and coding patterns to deliver highly relevant suggestions.

Overview

The code completion system in Trae IDE offers:

• AI-Powered Suggestions: Context-aware completions based on your code
• Multi-Language Support: Works across different programming languages
• Real-Time Analysis: Instant suggestions as you type
• Pattern Recognition: Learns from your coding style and project patterns
• Smart Imports: Automatically suggests and adds necessary imports

How It Works

Intelligent Context Analysis

Trae's AI analyzes multiple factors to provide relevant suggestions:

1. Current Code Context: Understanding the function, class, or module you're working in
2. Variable Types: Knowing the types of variables and their available methods
3. Project Dependencies: Awareness of installed packages and libraries
4. Coding Patterns: Recognition of common patterns in your codebase
5. Language Conventions: Following best practices for the specific language

Real-Time Suggestions

As you type, the AI continuously analyzes your code and provides suggestions for:

• Method and Function Names: Based on context and naming conventions
• Variable Names: Meaningful names that follow your project's style
• Code Snippets: Common patterns and boilerplate code
• API Calls: Correct syntax for library and framework methods
• Error Handling: Appropriate try-catch blocks and error handling patterns

Features

Smart Autocomplete

Traditional Autocomplete vs. AI-Powered Completion

Traditional	AI-Powered (Trae)
Simple keyword matching	Context-aware suggestions
Limited to current file	Project-wide understanding
Static suggestions	Dynamic, learning-based
No code generation	Generates complete code blocks

Multi-Line Completions

Trae can suggest entire code blocks, including:

// You type: "async function fetchUser"
// Trae suggests:
async function fetchUserData(userId) {
  try {
    const response = await fetch(`/api/users/${userId}`);
    if (!response.ok) {
      throw new Error(`HTTP error! status: ${response.status}`);
    }
    const userData = await response.json();
    return userData;
  } catch (error) {
    console.error('Error fetching user data:', error);
    throw error;
  }
}

Language-Specific Features

JavaScript/TypeScript

• Promise Handling: Automatic async/await patterns
• Type Inference: Smart type suggestions in TypeScript
• React Components: JSX completion with proper prop types
• Node.js APIs: Built-in module suggestions

Python

• Import Suggestions: Automatic import statements
• Class Methods: Context-aware method completions
• Data Science Libraries: NumPy, Pandas, Matplotlib completions
• Type Hints: Modern Python type annotation suggestions

Java

• Class Hierarchies: Interface and inheritance-aware completions
• Exception Handling: Proper try-catch-finally blocks
• Spring Framework: Annotation and configuration completions
• Maven/Gradle: Dependency and build script suggestions

Other Languages

• C#: .NET framework and LINQ completions
• Go: Goroutine and channel patterns
• Rust: Memory safety and ownership patterns
• PHP: Laravel and Symfony framework completions

Configuration

Completion Settings

Customize the code completion behavior through Settings:

1. Open Settings: Ctrl+, (Windows/Linux) or Cmd+, (macOS)
2. Navigate to: Editor > Code Completion
3. Configure Options:
   • Completion trigger delay
   • Maximum suggestions shown
   • Auto-import behavior
   • Suggestion ranking preferences

Trigger Settings

{
  "editor.codeCompletion": {
    "enabled": true,
    "triggerDelay": 100,
    "maxSuggestions": 10,
    "autoImport": true,
    "showDocumentation": true,
    "rankByRelevance": true
  }
}

Language-Specific Configuration

Customize completion behavior for specific languages:

{
  "[javascript]": {
    "editor.codeCompletion.triggerCharacters": [".", "(", "["],
    "editor.codeCompletion.includeSnippets": true
  },
  "[python]": {
    "editor.codeCompletion.autoImport": true,
    "editor.codeCompletion.showTypeHints": true
  }
}

Advanced Features

Context-Aware Snippets

Trae generates code snippets based on your current context:

Example: React Component Creation

// You type: "const UserProfile = "
// Trae suggests:
const UserProfile = ({ user, onEdit }) => {
  return (
    <div className="user-profile">
      <img src={user.avatar} alt={user.name} />
      <h2>{user.name}</h2>
      <p>{user.email}</p>
      <button onClick={() => onEdit(user.id)}>
        Edit Profile
      </button>
    </div>
  );
};

Smart Import Management

Automatic import suggestions and management:

• Auto-Import: Automatically adds import statements for suggested completions
• Import Optimization: Removes unused imports and organizes import statements
• Relative Path Resolution: Suggests correct relative paths for local modules
• Package Discovery: Finds and suggests imports from installed packages

Error-Driven Completions

When Trae detects errors, it provides targeted completions:

# Error: 'list' object has no attribute 'append_item'
my_list.append_item("new_item")  # ❌ Error

# Trae suggests:
my_list.append("new_item")  # ✅ Correct method

Best Practices

Maximizing Completion Effectiveness

1. Use Descriptive Names: Better variable names lead to better suggestions
2. Maintain Consistent Patterns: AI learns from your coding style
3. Keep Dependencies Updated: Ensures accurate API completions
4. Use Type Annotations: Helps AI provide more precise suggestions
5. Review Suggestions: Always verify AI-generated code before accepting

Keyboard Shortcuts

Action	Windows/Linux	macOS
Trigger Completion	Ctrl+Space	Cmd+Space
Accept Suggestion	Tab or Enter	Tab or Enter
Next Suggestion	↓	↓
Previous Suggestion	↑	↑
Dismiss Completions	Esc	Esc
Show Documentation	Ctrl+Q	Cmd+Q

Performance Optimization

Completion Performance

Trae optimizes completion performance through:

• Incremental Analysis: Only analyzes changed code sections
• Caching: Stores frequently used completions
• Background Processing: Prepares suggestions in advance
• Smart Filtering: Shows most relevant suggestions first

Large Project Handling

For large codebases, Trae:

• Indexes Project Structure: Maintains a searchable index of your code
• Prioritizes Local Context: Focuses on immediately relevant code
• Lazy Loading: Loads completions on-demand
• Memory Management: Efficiently manages completion data

Troubleshooting

Common Issues

Completions not appearing

• Check if code completion is enabled in settings
• Verify the file type is supported
• Ensure the project is properly indexed

Irrelevant suggestions

• Clear the completion cache
• Check project dependencies
• Verify import statements are correct

Slow completion performance

• Reduce the number of maximum suggestions
• Increase the trigger delay
• Check for large files that might slow indexing

Performance Tips

1. Exclude Large Directories: Add node_modules, build folders to exclusion list
2. Optimize Project Structure: Keep related files organized
3. Regular Cache Clearing: Clear completion cache periodically
4. Update Dependencies: Keep project dependencies current

Integration with Other Features

AI Assistant Integration

Code completion works seamlessly with other AI features:

• Chat Context: Completions consider your chat history with AI
• Error Fixing: Integrates with AI error detection and fixing
• Code Generation: Builds upon AI-generated code patterns

Version Control Integration

• Branch Awareness: Completions adapt to different code branches
• Commit History: Learns from your coding evolution
• Collaboration: Adapts to team coding patterns

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Trae's intelligent code completion system is designed to make you more productive by understanding your code and providing relevant, timely suggestions that fit naturally into your development workflow.