ByteBuddy Feature Overview

ByteBuddy is a highly configurable AI programming assistant that can adapt to your specific development workflow, preferences, and requirements. This section covers ByteBuddy's core features and configuration options.

Configuration Options

Core Configuration

ByteBuddy can be configured through:

1. Configuration Files

   • config.yaml in your project root
   • Global configuration in ~/.bytebuddy/config.yaml

2. IDE Integration

   • VS Code settings
   • JetBrains preferences

Configuration Structure

A typical ByteBuddy configuration includes:

# config.yaml
name: My ByteBuddy Config
version: 0.0.1
schema: v1

models:
  - name: "gpt-4"
    provider: "openai"
    model: "gpt-4"
    apiKey: "${OPENAI_API_KEY}"
    roles:
      - chat
      - edit
      - apply

  - name: "claude-3"
    provider: "anthropic"
    model: "claude-3-sonnet"
    apiKey: "${ANTHROPIC_API_KEY}"
    roles:
      - chat
      - autocomplete

context:
  - provider: "codebase"
  - provider: "diff"
  - provider: "docs"
  - provider: "terminal"

rules:
  - "Always include type annotations"
  - "Follow existing code style"
  - name: "comment-complex"
    rule: "Add comments for complex logic"
    description: "Add comments for complex functions over 5 lines"

mcpServers:
  - name: "filesystem"
    command: "npx"
    args: ["@modelcontextprotocol/server-filesystem", "/path/to/project"]

Key Customization Areas

1. AI Models and Providers

Support for multiple AI models and service providers:

• Mainstream Models: GPT-4, Claude, Gemini, etc.
• Local Models: Ollama, LM Studio, LLaMA.cpp
• Custom Providers: Create your own model providers
• Model Roles: Assign specific roles for different models

2. Context Providers

Control the scope of information AI can access:

• Codebase Context: Include relevant files and directories
• Diff Context: Show current changes
• Docs Context: Include documentation and materials
• Terminal Context: Access terminal output
• Folder Context: Browse file structure
• URL Context: Fetch web content
• Database Context: Query databases (via MCP)

3. Rules System

Guide AI behavior patterns:

• String Rules: Simple rule definitions
• Object Rules: Rules with names and descriptions
• File Matching: Rules based on file types and paths
• Callable Rules: Rules users can actively trigger

4. MCP Server Integration

Extend functionality through MCP protocol:

• Filesystem Tools: File read/write and management
• Browser Automation: Use Playwright
• Database Tools: Various database connections
• Git Tools: Version control operations
• Custom MCP Servers: Create your own extensions

Getting Started with Customization

Basic Customization

1. Create a configuration file in your project:

   touch config.yaml

2. Add your first model:

   models:
     - name: "gpt-4"
       provider: "openai"
       model: "gpt-4"
       apiKey: "your-api-key-here"
       roles:
         - chat

3. Add context providers:

   context:
     - provider: "codebase"
     - provider: "diff"

4. Validate your configuration: Check the syntax and validity of your configuration file.

Advanced Customization

As you become more familiar with ByteBuddy, you can:

• Configure multiple models: Assign different models for different tasks
• Set model roles: chat, edit, apply, autocomplete, etc.
• Configure rules system: Set coding standards and conventions
• Integrate MCP servers: Extend functionality scope
• Customize context providers: Control AI's information access scope

Best Practices

1. Security

• Use environment variables to store sensitive data
• Carefully review model permissions
• Validate external tool inputs
• Protect database and SSH connection credentials

2. Performance

• Optimize context windows for your models
• Use appropriate models for different tasks
• Cache frequently used context
• Monitor token usage

3. Team Collaboration

• Share configurations through version control
• Document your customizations
• Use standard naming conventions
• Test configurations across different environments

4. Maintenance

• Keep configurations updated
• Regularly review model performance
• Monitor API usage and costs
• Backup important configurations

Team Configuration Example

# Team shared configuration
name: Team Standard Config
version: 1.0.0
schema: v1

models:
  - name: "team-model"
    provider: "openai"
    model: "gpt-4"
    apiKey: "${TEAM_API_KEY}"
    roles:
      - chat
      - edit

rules:
  - "Follow team coding standards"
  - name: "documentation"
    rule: "Include appropriate documentation comments"
    description: "Add JSDoc comments for all public functions"
  - name: "test-coverage"
    rule: "Ensure test coverage is not below 80%"
    globs: ["**/*.ts", "**/*.js"]

context:
  - provider: "codebase"
  - provider: "docs"
  - provider: "diff"

Common Configuration Patterns

1. Environment-Specific Configuration

# Development environment
models:
  - name: "gpt-3.5-turbo"
    provider: "openai"

# Production environment
models:
  - name: "gpt-4"
    provider: "openai"

2. Language-Specific Settings

# Python project
context:
  - provider: "codebase"
    params:
      dirs: ["src", "tests"]
      fileRegex: "\\.py$"

# JavaScript project
context:
  - provider: "codebase"
    params:
      dirs: ["src", "lib"]
      fileRegex: "\\.(js|ts|jsx|tsx)$"

rules:
  - "Follow PEP 8 style guide"
  - "Include type hints"
  - "Write docstrings for all functions"

3. Team Configuration

# Shared team configuration
models:
  - name: "team-gpt-4"
    provider: "openai"
    apiKey: "${{ secrets.TEAM_API_KEY }}"

rules:
  - "Use company coding standards"
  - "Include security considerations"
  - "Document API changes"

Start customizing ByteBuddy to meet your exact needs and unlock its full potential!