Kode-cli/docs/develop/overview.md

Kode System Overview

Introduction

Kode is an AI-powered terminal assistant that brings the power of Claude and other language models directly to your command line. It's designed as a sophisticated development tool that understands your codebase, executes commands, and handles complex workflows through natural language interaction.

Core Design Philosophy

1. Tool-First Architecture

Everything in Kode is abstracted as a "Tool" - a self-contained unit of functionality with standardized interfaces for:

• Input validation through Zod schemas
• Permission checking and security
• Async execution with progress reporting
• Result formatting for both AI and human consumption

This design allows unlimited extensibility while maintaining consistency and security.

2. Terminal-Native Experience

Unlike web-based AI assistants, Kode is built specifically for terminal workflows:

• React components render directly to terminal using Ink
• Keyboard shortcuts and vim-like bindings for power users
• Streaming responses with real-time progress indicators
• Syntax highlighting and diff visualization in terminal

3. Context-Aware Intelligence

The AI automatically understands your project through:

• Git status and recent commits
• Directory structure analysis
• AGENTS.md and CLAUDE.md project documentation
• Custom command definitions in .claude/commands/ and .kode/commands/
• Previous conversation history and forked conversations

4. Security Through Permissions

Two-tier security model balancing safety and usability:

• Permissive Mode (default): Streamlined workflow with minimal interruptions
• Safe Mode (--safe flag): Granular permission requests for every operation

5. Provider Agnostic

Support for multiple AI providers through unified interface:

• Anthropic Claude (primary)
• OpenAI and compatible APIs
• Custom endpoints and local models
• Automatic model switching based on context size

6. Extensibility via MCP

Model Context Protocol (MCP) integration enables:

• Third-party tool integration
• Custom server connections (stdio/SSE)
• Import from Claude Desktop configuration
• Project-scoped server management

Technology Stack

Core Technologies

• Language: TypeScript with relaxed strict mode for pragmatic development
• Runtime: Node.js 18+ with Bun for development, TSX for production
• UI Framework: React + Ink for terminal rendering
• CLI Framework: Commander.js for argument parsing
• Validation: Zod for runtime type checking

AI Integration

• Anthropic SDK: Native integration with Claude models
• OpenAI SDK: Support for GPT models and compatible APIs
• Streaming: Server-sent events for real-time responses
• Context Management: Smart token counting and compaction

Development Tools

• Build System: Custom Bun-based build scripts
• Testing: Bun test runner with mocking support
• Formatting: Prettier with consistent code style
• Error Tracking: Sentry integration for production monitoring
• Analytics: Statsig for feature flags and usage metrics

System Architecture Layers

1. Presentation Layer

React components rendered to terminal, providing:

• Interactive prompts and selections
• Syntax-highlighted code display
• Progress indicators and spinners
• Message formatting with markdown support

2. Command & Control Layer

Orchestrates user interactions through:

• Slash command system for quick actions
• Natural language processing for AI conversations
• Tool selection and execution pipeline
• Context injection and management

3. Tool Execution Layer

Standardized tool interface enabling:

• File operations (read, write, edit)
• Shell command execution
• Code searching and analysis
• Web search and content analysis
• Task management and planning
• External tool integration via MCP

4. Service Integration Layer

Connects to external services:

• AI model providers (Anthropic, OpenAI)
• MCP servers for extended functionality
• Git for version control integration
• File system for project analysis

5. Infrastructure Layer

Foundational services including:

• Configuration management (global/project)
• Permission system and security
• Logging and error handling
• Session and history management
• Cost tracking and analytics

Key Design Patterns

Async Generators for Streaming

Tools use async generators to yield progress updates, enabling real-time feedback during long operations while maintaining cancellation support.

Component-Based UI Architecture

Every UI element is a React component, from simple text displays to complex interactive dialogs, ensuring consistency and reusability.

Configuration Cascading

Settings flow from global → project → runtime with validation at each level, allowing flexible customization without complexity.

Error Boundaries

Comprehensive error handling ensures graceful degradation, with user-friendly messages and automatic error reporting.

Context Injection

Relevant project context is automatically injected into AI conversations, improving response quality without manual specification.

Development Principles

1. User Experience First

Every feature is designed for terminal workflow efficiency, with keyboard-first interaction and minimal context switching.

2. Progressive Disclosure

Complex features are hidden behind simple interfaces, with advanced options available when needed.

3. Fail Gracefully

Errors are handled gracefully with helpful messages, fallback options, and recovery suggestions.

4. Performance Conscious

Caching, lazy loading, and streaming ensure responsive interaction even with large codebases.

5. Security by Default

Permission checks are mandatory for potentially dangerous operations, with clear user consent flows.

System Capabilities

Core Capabilities

• Code Understanding: Analyzes project structure and relationships
• File Manipulation: Read, write, and edit files with validation
• Command Execution: Run shell commands with output capture
• Search & Analysis: Find code patterns and dependencies
• Web Search & Content: Search the web and analyze web content with AI
• Task Management: Plan and track development tasks

AI-Enhanced Features

• Natural Language Commands: Describe what you want in plain English
• Context-Aware Suggestions: AI understands your project's specifics
• Multi-Step Workflows: Complex tasks broken down automatically
• Code Generation: Create new code following project patterns
• Refactoring Support: Safely modify existing code

Integration Features

• MCP Servers: Connect to external tools and services
• Custom Commands: Define reusable markdown-based commands
• Git Integration: Understand and work with version control
• Multiple AI Models: Switch between models based on task

Future Extensibility

The architecture is designed for future expansion:

• New tools can be added by implementing the Tool interface
• Additional AI providers can be integrated through ModelManager
• Custom commands enable user-defined workflows
• MCP support allows third-party extensions
• Plugin system could be added with minimal changes

This modular, extensible design ensures Kode can evolve with user needs while maintaining stability and security.