d8f0a22233
- Add advanced fuzzy matching with 7+ strategies (exact, prefix, substring, acronym, initials, fuzzy, Levenshtein) - Create comprehensive database of 500+ common Unix commands for smart autocompletion - Implement intelligent Tab completion with @ prefix injection for agents and files - Add sophisticated input pattern recognition for commands like "dao", "gp5", "py3" - Enhance mention system with TaskProgressMessage component for better user feedback - Update documentation with comprehensive intelligent completion guide - Clean up 21 temporary markdown files to maintain repository cleanliness - Improve project structure and configuration documentation - Optimize completion system performance with advanced caching and scoring
342 lines
13 KiB
Markdown
342 lines
13 KiB
Markdown
# Kode - AI Assistant for Your Terminal
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[](https://www.npmjs.com/package/@shareai-lab/kode)
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[](https://opensource.org/licenses/ISC)
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[](https://agents.md)
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[中文文档](README.zh-CN.md) | [Contributing](CONTRIBUTING.md) | [Documentation](docs/)
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## 🤝 AGENTS.md Standard Support
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**Kode proudly supports the [AGENTS.md standard protocol](https://agents.md) initiated by OpenAI** - a simple, open format for guiding programming agents that's used by 20k+ open source projects.
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### Full Compatibility with Multiple Standards
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- ✅ **AGENTS.md** - Native support for the OpenAI-initiated standard format
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- ✅ **CLAUDE.md** - Full backward compatibility with Claude Code configurations
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- ✅ **Subagent System** - Advanced agent delegation and task orchestration
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- ✅ **Cross-platform** - Works with 20+ AI models and providers
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Use `# Your documentation request` to generate and maintain your AGENTS.md file automatically, while maintaining full compatibility with existing Claude Code workflows.
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## Overview
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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.
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## Features
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### Core Capabilities
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- 🤖 **AI-Powered Assistance** - Uses advanced AI models to understand and respond to your requests
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- 🔄 **Multi-Model Collaboration** - Flexibly switch and combine multiple AI models to leverage their unique strengths
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- 🦜 **Expert Model Consultation** - Use `@ask-model-name` to consult specific AI models for specialized analysis
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- 👤 **Intelligent Agent System** - Use `@run-agent-name` to delegate tasks to specialized subagents
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- 📝 **Code Editing** - Directly edit files with intelligent suggestions and improvements
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- 🔍 **Codebase Understanding** - Analyzes your project structure and code relationships
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- 🚀 **Command Execution** - Run shell commands and see results in real-time
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- 🛠️ **Workflow Automation** - Handle complex development tasks with simple prompts
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### 🎯 Advanced Intelligent Completion System
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Our state-of-the-art completion system provides unparalleled coding assistance:
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#### Smart Fuzzy Matching
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- **Hyphen-Aware Matching** - Type `dao` to match `run-agent-dao-qi-harmony-designer`
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- **Abbreviation Support** - `dq` matches `dao-qi`, `nde` matches `node`
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- **Numeric Suffix Handling** - `py3` intelligently matches `python3`
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- **Multi-Algorithm Fusion** - Combines 7+ matching algorithms for best results
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#### Intelligent Context Detection
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- **No @ Required** - Type `gp5` directly to match `@ask-gpt-5`
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- **Auto-Prefix Addition** - Tab/Enter automatically adds `@` for agents and models
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- **Mixed Completion** - Seamlessly switch between commands, files, agents, and models
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- **Smart Prioritization** - Results ranked by relevance and usage frequency
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#### Unix Command Optimization
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- **500+ Common Commands** - Curated database of frequently used Unix/Linux commands
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- **System Intersection** - Only shows commands that actually exist on your system
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- **Priority Scoring** - Common commands appear first (git, npm, docker, etc.)
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- **Real-time Loading** - Dynamic command discovery from system PATH
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### User Experience
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- 🎨 **Interactive UI** - Beautiful terminal interface with syntax highlighting
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- 🔌 **Tool System** - Extensible architecture with specialized tools for different tasks
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- 💾 **Context Management** - Smart context handling to maintain conversation continuity
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- 📋 **AGENTS.md Integration** - Use `# documentation requests` to auto-generate and maintain project documentation
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## Installation
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```bash
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npm install -g @shareai-lab/kode
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```
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After installation, you can use any of these commands:
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- `kode` - Primary command
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- `kwa` - Kode With Agent (alternative)
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- `kd` - Ultra-short alias
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## Usage
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### Interactive Mode
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Start an interactive session:
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```bash
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kode
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# or
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kwa
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# or
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kd
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```
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### Non-Interactive Mode
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Get a quick response:
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```bash
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kode -p "explain this function" main.js
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# or
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kwa -p "explain this function" main.js
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```
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### Using the @ Mention System
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Kode supports a powerful @ mention system for intelligent completions:
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#### 🦜 Expert Model Consultation
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```bash
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# Consult specific AI models for expert opinions
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@ask-claude-sonnet-4 How should I optimize this React component for performance?
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@ask-gpt-5 What are the security implications of this authentication method?
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@ask-o1-preview Analyze the complexity of this algorithm
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```
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#### 👤 Specialized Agent Delegation
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```bash
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# Delegate tasks to specialized subagents
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@run-agent-simplicity-auditor Review this code for over-engineering
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@run-agent-architect Design a microservices architecture for this system
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@run-agent-test-writer Create comprehensive tests for these modules
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```
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#### 📁 Smart File References
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```bash
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# Reference files and directories with auto-completion
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@src/components/Button.tsx
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@docs/api-reference.md
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@.env.example
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```
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The @ mention system provides intelligent completions as you type, showing available models, agents, and files.
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### AGENTS.md Documentation Mode
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Use the `#` prefix to generate and maintain your AGENTS.md documentation:
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```bash
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# Generate setup instructions
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# How do I set up the development environment?
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# Create testing documentation
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# What are the testing procedures for this project?
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# Document deployment process
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# Explain the deployment pipeline and requirements
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```
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This mode automatically formats responses as structured documentation and appends them to your AGENTS.md file.
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### Commands
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- `/help` - Show available commands
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- `/model` - Change AI model settings
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- `/config` - Open configuration panel
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- `/cost` - Show token usage and costs
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- `/clear` - Clear conversation history
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- `/init` - Initialize project context
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## Multi-Model Intelligent Collaboration
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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.
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### 🏗️ Core Technical Architecture
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#### 1. **ModelManager Multi-Model Manager**
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We designed a unified `ModelManager` system that supports:
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- **Model Profiles**: Each model has an independent configuration file containing API endpoints, authentication, context window size, cost parameters, etc.
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- **Model Pointers**: Users can configure default models for different purposes in the `/model` command:
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- `main`: Default model for main Agent
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- `task`: Default model for SubAgent
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- `reasoning`: Reserved for future ThinkTool usage
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- `quick`: Fast model for simple NLP tasks (security identification, title generation, etc.)
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- **Dynamic Model Switching**: Support runtime model switching without restarting sessions, maintaining context continuity
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#### 2. **TaskTool Intelligent Task Distribution**
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Our specially designed `TaskTool` (Architect tool) implements:
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- **Subagent Mechanism**: Can launch multiple sub-agents to process tasks in parallel
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- **Model Parameter Passing**: Users can specify which model SubAgents should use in their requests
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- **Default Model Configuration**: SubAgents use the model configured by the `task` pointer by default
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#### 3. **AskExpertModel Expert Consultation Tool**
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We specially designed the `AskExpertModel` tool:
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- **Expert Model Invocation**: Allows temporarily calling specific expert models to solve difficult problems during conversations
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- **Model Isolation Execution**: Expert model responses are processed independently without affecting the main conversation flow
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- **Knowledge Integration**: Integrates expert model insights into the current task
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#### 🎯 Flexible Model Switching
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- **Tab Key Quick Switch**: Press Tab in the input box to quickly switch the model for the current conversation
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- **`/model` Command**: Use `/model` command to configure and manage multiple model profiles, set default models for different purposes
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- **User Control**: Users can specify specific models for task processing at any time
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#### 🔄 Intelligent Work Allocation Strategy
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**Architecture Design Phase**
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- Use **o3 model** or **GPT-5 model** to explore system architecture and formulate sharp and clear technical solutions
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- These models excel in abstract thinking and system design
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**Solution Refinement Phase**
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- Use **gemini model** to deeply explore production environment design details
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- Leverage its deep accumulation in practical engineering and balanced reasoning capabilities
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**Code Implementation Phase**
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- Use **Qwen Coder model**, **Kimi k2 model**, **GLM-4.5 model**, or **Claude Sonnet 4 model** for specific code writing
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- These models have strong performance in code generation, file editing, and engineering implementation
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- Support parallel processing of multiple coding tasks through subagents
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**Problem Solving**
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- When encountering complex problems, consult expert models like **o3 model**, **Claude Opus 4.1 model**, or **Grok 4 model**
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- Obtain deep technical insights and innovative solutions
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#### 💡 Practical Application Scenarios
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```bash
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# Example 1: Architecture Design
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"Use o3 model to help me design a high-concurrency message queue system architecture"
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# Example 2: Multi-Model Collaboration
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"First use GPT-5 model to analyze the root cause of this performance issue, then use Claude Sonnet 4 model to write optimization code"
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# Example 3: Parallel Task Processing
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"Use Qwen Coder model as subagent to refactor these three modules simultaneously"
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# Example 4: Expert Consultation
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"This memory leak issue is tricky, ask Claude Opus 4.1 model separately for solutions"
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# Example 5: Code Review
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"Have Kimi k2 model review the code quality of this PR"
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# Example 6: Complex Reasoning
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"Use Grok 4 model to help me derive the time complexity of this algorithm"
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# Example 7: Solution Design
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"Have GLM-4.5 model design a microservice decomposition plan"
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```
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### 🛠️ Key Implementation Mechanisms
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#### **Configuration System**
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```typescript
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// Example of multi-model configuration support
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{
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"modelProfiles": {
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"o3": { "provider": "openai", "model": "o3", "apiKey": "..." },
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"claude4": { "provider": "anthropic", "model": "claude-sonnet-4", "apiKey": "..." },
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"qwen": { "provider": "alibaba", "model": "qwen-coder", "apiKey": "..." }
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},
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"modelPointers": {
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"main": "claude4", // Main conversation model
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"task": "qwen", // Task execution model
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"reasoning": "o3", // Reasoning model
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"quick": "glm-4.5" // Quick response model
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}
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}
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```
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#### **Cost Tracking System**
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- **Usage Statistics**: Use `/cost` command to view token usage and costs for each model
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- **Multi-Model Cost Comparison**: Track usage costs of different models in real-time
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- **History Records**: Save cost data for each session
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#### **Context Manager**
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- **Context Inheritance**: Maintain conversation continuity when switching models
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- **Context Window Adaptation**: Automatically adjust based on different models' context window sizes
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- **Session State Preservation**: Ensure information consistency during multi-model collaboration
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### 🚀 Advantages of Multi-Model Collaboration
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1. **Maximized Efficiency**: Each task is handled by the most suitable model
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2. **Cost Optimization**: Use lightweight models for simple tasks, powerful models for complex tasks
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3. **Parallel Processing**: Multiple models can work on different subtasks simultaneously
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4. **Flexible Switching**: Switch models based on task requirements without restarting sessions
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5. **Leveraging Strengths**: Combine advantages of different models for optimal overall results
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### 📊 Comparison with Official Implementation
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| Feature | Kode | Official Claude |
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|---------|------|-----------------|
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| Number of Supported Models | Unlimited, configurable for any model | Only supports single Claude model |
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| Model Switching | ✅ Tab key quick switch | ❌ Requires session restart |
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| Parallel Processing | ✅ Multiple SubAgents work in parallel | ❌ Single-threaded processing |
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| Cost Tracking | ✅ Separate statistics for multiple models | ❌ Single model cost |
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| Task Model Configuration | ✅ Different default models for different purposes | ❌ Same model for all tasks |
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| Expert Consultation | ✅ AskExpertModel tool | ❌ Not supported |
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This multi-model collaboration capability makes Kode a true **AI Development Workbench**, not just a single AI assistant.
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## Development
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Kode is built with modern tools and requires [Bun](https://bun.sh) for development.
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### Install Bun
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```bash
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# macOS/Linux
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curl -fsSL https://bun.sh/install | bash
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# Windows
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powershell -c "irm bun.sh/install.ps1 | iex"
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```
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### Setup Development Environment
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```bash
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# Clone the repository
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git clone https://github.com/shareAI-lab/kode.git
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cd kode
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# Install dependencies
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bun install
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# Run in development mode
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bun run dev
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```
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### Build
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```bash
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bun run build
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```
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### Testing
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```bash
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# Run tests
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bun test
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# Test the CLI
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./cli.js --help
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```
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## Contributing
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We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
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## License
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ISC License - see [LICENSE](LICENSE) for details.
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## Thanks
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- Some code from @dnakov's anonkode
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- Some UI learned from gemini-cli
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- Some system design learned from claude code
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## Support
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- 📚 [Documentation](docs/)
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- 🐛 [Report Issues](https://github.com/shareAI-lab/kode/issues)
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- 💬 [Discussions](https://github.com/shareAI-lab/kode/discussions) |