Kode proudly supports the AGENTS.md standard protocol initiated by OpenAI - a simple, open format for guiding programming agents that's used by 20k+ open source projects.

Full Compatibility with Multiple Standards

✅ AGENTS.md - Native support for the OpenAI-initiated standard format
✅ CLAUDE.md - Full backward compatibility with Claude Code configurations
✅ Subagent System - Advanced agent delegation and task orchestration
✅ Cross-platform - Works with 20+ AI models and providers

Use # Your documentation request to generate and maintain your AGENTS.md file automatically, while maintaining full compatibility with existing Claude Code workflows.

Overview

Kode is a powerful AI assistant that lives in your terminal. It can understand your codebase, edit files, run commands, and handle entire workflows for you.

Features

Core Capabilities

🤖 AI-Powered Assistance - Uses advanced AI models to understand and respond to your requests
🔄 Multi-Model Collaboration - Flexibly switch and combine multiple AI models to leverage their unique strengths
🦜 Expert Model Consultation - Use @ask-model-name to consult specific AI models for specialized analysis
👤 Intelligent Agent System - Use @run-agent-name to delegate tasks to specialized subagents
📝 Code Editing - Directly edit files with intelligent suggestions and improvements
🔍 Codebase Understanding - Analyzes your project structure and code relationships
🚀 Command Execution - Run shell commands and see results in real-time
🛠️ Workflow Automation - Handle complex development tasks with simple prompts

🎯 Advanced Intelligent Completion System

Our state-of-the-art completion system provides unparalleled coding assistance:

Smart Fuzzy Matching

Hyphen-Aware Matching - Type dao to match run-agent-dao-qi-harmony-designer
Abbreviation Support - dq matches dao-qi, nde matches node
Numeric Suffix Handling - py3 intelligently matches python3
Multi-Algorithm Fusion - Combines 7+ matching algorithms for best results

Intelligent Context Detection

No @ Required - Type gp5 directly to match @ask-gpt-5
Auto-Prefix Addition - Tab/Enter automatically adds @ for agents and models
Mixed Completion - Seamlessly switch between commands, files, agents, and models
Smart Prioritization - Results ranked by relevance and usage frequency

Unix Command Optimization

500+ Common Commands - Curated database of frequently used Unix/Linux commands
System Intersection - Only shows commands that actually exist on your system
Priority Scoring - Common commands appear first (git, npm, docker, etc.)
Real-time Loading - Dynamic command discovery from system PATH

User Experience

🎨 Interactive UI - Beautiful terminal interface with syntax highlighting
🔌 Tool System - Extensible architecture with specialized tools for different tasks
💾 Context Management - Smart context handling to maintain conversation continuity
📋 AGENTS.md Integration - Use # documentation requests to auto-generate and maintain project documentation

Installation

npm install -g @shareai-lab/kode

After installation, you can use any of these commands:

kode - Primary command
kwa - Kode With Agent (alternative)
kd - Ultra-short alias

Usage

Interactive Mode

Start an interactive session:

kode
# or
kwa
# or
kd

Non-Interactive Mode

Get a quick response:

kode -p "explain this function" main.js
# or
kwa -p "explain this function" main.js

Using the @ Mention System

Kode supports a powerful @ mention system for intelligent completions:

🦜 Expert Model Consultation

# Consult specific AI models for expert opinions
@ask-claude-sonnet-4 How should I optimize this React component for performance?
@ask-gpt-5 What are the security implications of this authentication method?
@ask-o1-preview Analyze the complexity of this algorithm

👤 Specialized Agent Delegation

# Delegate tasks to specialized subagents
@run-agent-simplicity-auditor Review this code for over-engineering
@run-agent-architect Design a microservices architecture for this system
@run-agent-test-writer Create comprehensive tests for these modules

📁 Smart File References

# Reference files and directories with auto-completion
@src/components/Button.tsx
@docs/api-reference.md
@.env.example

The @ mention system provides intelligent completions as you type, showing available models, agents, and files.

AGENTS.md Documentation Mode

Use the # prefix to generate and maintain your AGENTS.md documentation:

# Generate setup instructions
# How do I set up the development environment?

# Create testing documentation  
# What are the testing procedures for this project?

# Document deployment process
# Explain the deployment pipeline and requirements

This mode automatically formats responses as structured documentation and appends them to your AGENTS.md file.

Commands

/help - Show available commands
/model - Change AI model settings
/config - Open configuration panel
/cost - Show token usage and costs
/clear - Clear conversation history
/init - Initialize project context

Multi-Model Intelligent Collaboration

Unlike official Claude which supports only a single model, Kode implements true multi-model collaboration, allowing you to fully leverage the unique strengths of different AI models.

🏗️ Core Technical Architecture

1. ModelManager Multi-Model Manager

We designed a unified ModelManager system that supports:

Model Profiles: Each model has an independent configuration file containing API endpoints, authentication, context window size, cost parameters, etc.
Model Pointers: Users can configure default models for different purposes in the /model command:
- main: Default model for main Agent
- task: Default model for SubAgent
- reasoning: Reserved for future ThinkTool usage
- quick: Fast model for simple NLP tasks (security identification, title generation, etc.)
Dynamic Model Switching: Support runtime model switching without restarting sessions, maintaining context continuity

2. TaskTool Intelligent Task Distribution

Our specially designed TaskTool (Architect tool) implements:

Subagent Mechanism: Can launch multiple sub-agents to process tasks in parallel
Model Parameter Passing: Users can specify which model SubAgents should use in their requests
Default Model Configuration: SubAgents use the model configured by the task pointer by default

3. AskExpertModel Expert Consultation Tool

We specially designed the AskExpertModel tool:

Expert Model Invocation: Allows temporarily calling specific expert models to solve difficult problems during conversations
Model Isolation Execution: Expert model responses are processed independently without affecting the main conversation flow
Knowledge Integration: Integrates expert model insights into the current task

🎯 Flexible Model Switching

Tab Key Quick Switch: Press Tab in the input box to quickly switch the model for the current conversation
/model Command: Use /model command to configure and manage multiple model profiles, set default models for different purposes
User Control: Users can specify specific models for task processing at any time

🔄 Intelligent Work Allocation Strategy

Architecture Design Phase

Use o3 model or GPT-5 model to explore system architecture and formulate sharp and clear technical solutions
These models excel in abstract thinking and system design

Solution Refinement Phase

Use gemini model to deeply explore production environment design details
Leverage its deep accumulation in practical engineering and balanced reasoning capabilities

Code Implementation Phase

Use Qwen Coder model, Kimi k2 model, GLM-4.5 model, or Claude Sonnet 4 model for specific code writing
These models have strong performance in code generation, file editing, and engineering implementation
Support parallel processing of multiple coding tasks through subagents

Problem Solving

When encountering complex problems, consult expert models like o3 model, Claude Opus 4.1 model, or Grok 4 model
Obtain deep technical insights and innovative solutions

💡 Practical Application Scenarios

# Example 1: Architecture Design
"Use o3 model to help me design a high-concurrency message queue system architecture"

# Example 2: Multi-Model Collaboration
"First use GPT-5 model to analyze the root cause of this performance issue, then use Claude Sonnet 4 model to write optimization code"

# Example 3: Parallel Task Processing
"Use Qwen Coder model as subagent to refactor these three modules simultaneously"

# Example 4: Expert Consultation
"This memory leak issue is tricky, ask Claude Opus 4.1 model separately for solutions"

# Example 5: Code Review
"Have Kimi k2 model review the code quality of this PR"

# Example 6: Complex Reasoning
"Use Grok 4 model to help me derive the time complexity of this algorithm"

# Example 7: Solution Design
"Have GLM-4.5 model design a microservice decomposition plan"

🛠️ Key Implementation Mechanisms

Configuration System

// Example of multi-model configuration support
{
  "modelProfiles": {
    "o3": { "provider": "openai", "model": "o3", "apiKey": "..." },
    "claude4": { "provider": "anthropic", "model": "claude-sonnet-4", "apiKey": "..." },
    "qwen": { "provider": "alibaba", "model": "qwen-coder", "apiKey": "..." }
  },
  "modelPointers": {
    "main": "claude4",      // Main conversation model
    "task": "qwen",         // Task execution model
    "reasoning": "o3",      // Reasoning model
    "quick": "glm-4.5"      // Quick response model
  }
}

Cost Tracking System

Usage Statistics: Use /cost command to view token usage and costs for each model
Multi-Model Cost Comparison: Track usage costs of different models in real-time
History Records: Save cost data for each session

Context Manager

Context Inheritance: Maintain conversation continuity when switching models
Context Window Adaptation: Automatically adjust based on different models' context window sizes
Session State Preservation: Ensure information consistency during multi-model collaboration

🚀 Advantages of Multi-Model Collaboration

Maximized Efficiency: Each task is handled by the most suitable model
Cost Optimization: Use lightweight models for simple tasks, powerful models for complex tasks
Parallel Processing: Multiple models can work on different subtasks simultaneously
Flexible Switching: Switch models based on task requirements without restarting sessions
Leveraging Strengths: Combine advantages of different models for optimal overall results

📊 Comparison with Official Implementation

Feature	Kode	Official Claude
Number of Supported Models	Unlimited, configurable for any model	Only supports single Claude model
Model Switching	✅ Tab key quick switch	❌ Requires session restart
Parallel Processing	✅ Multiple SubAgents work in parallel	❌ Single-threaded processing
Cost Tracking	✅ Separate statistics for multiple models	❌ Single model cost
Task Model Configuration	✅ Different default models for different purposes	❌ Same model for all tasks
Expert Consultation	✅ AskExpertModel tool	❌ Not supported

This multi-model collaboration capability makes Kode a true AI Development Workbench, not just a single AI assistant.

Development

Kode is built with modern tools and requires Bun for development.

Install Bun

# macOS/Linux
curl -fsSL https://bun.sh/install | bash

# Windows
powershell -c "irm bun.sh/install.ps1 | iex"

Setup Development Environment

# Clone the repository
git clone https://github.com/shareAI-lab/kode.git
cd kode

# Install dependencies
bun install

# Run in development mode
bun run dev

Build

bun run build

Testing

# Run tests
bun test

# Test the CLI
./cli.js --help

Contributing

We welcome contributions! Please see our Contributing Guide for details.

License

ISC License - see LICENSE for details.

Thanks

Some code from @dnakov's anonkode
Some UI learned from gemini-cli
Some system design learned from claude code