Kode CLI: A Multi-Model AI Terminal Assistant for Smarter Development

Kode redefines the terminal experience. It is far more than a place to type commands; it is a development partner that operates directly within your shell. Kode interprets your codebase, edits files, executes commands, and manages complex development sequences—all through a continuous, context-aware conversation.

AI-Driven Assistance
Built on the latest large language models, Kode accurately parses user intent. Whether you are asking a technical question or requesting a complex file operation, the tool provides a response tailored specifically to the task at hand.

Multi-Model Collaboration
Switch between various AI models on the fly or combine their strengths within a single workflow. You might use an architecture-focused model to draft a system design and then hand the implementation details to a model optimized for code generation. Different specialties, one unified tool.

Direct Code Editing
Modify files without ever leaving the terminal. Kode provides intelligent suggestions and refactoring options designed to help you write high-quality code in less time.

Codebase Parsing
Kode analyzes your project structure to map the relationships between files and modules. This allows you to spend less time navigating architecture and more time building features.

Real-Time Command Execution
Execute shell commands and view the output instantly. Kode eliminates the need to switch windows or copy-paste data between separate tools.

Workflow Automation
Provide Kode with a high-level instruction, and it will handle multi-step tasks such as project initialization, running test suites, or managing repetitive maintenance chores.

Interactive Interface
The terminal UI is clean and functional. Integrated syntax highlighting ensures that both code and commands remain highly readable, making it suitable for long development sessions.

Extensible Tool System
The modular architecture uses specialized tools for specific jobs. This framework allows you to add or integrate new capabilities as your project requirements evolve.

Smart Context Management
Kode maintains a persistent conversation thread, so you never have to repeat yourself. Discussions remain coherent and relevant, even when switching between different models or running multiple commands.

Installing Kode

Install the package globally via npm:

npm install -g @shareai-lab/kode

Once installed, you can launch Kode using any of the following commands:

• kode – The standard command.
• kwa – Kode With Agent (alias).
• kd – A shortened alias for quick access.

Using Kode

Interactive Mode

To start a continuous session where complex tasks are handled step-by-step, use:

kode
# or
kwa
# or
kd

Non-Interactive Mode

For quick answers or single-task executions, use the -p flag:

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

Running Kode with Docker

For developers who prefer containerized environments, follow these steps:

1. Clone the repository

git clone https://github.com/shareAI-lab/Kode.git
cd Kode

2. Build the image locally

docker build --no-cache -t Kode .

3. Run within your project directory

cd your-project-directory
docker run -it --rm \
  -v $(pwd):/workspace \
  -v ~/.kode:/root/.kode \
  -v ~/.kode.json:/root/.kode.json \
  -w /workspace \
  Kode

Docker Configuration Details

Volume Mounts

• $(pwd):/workspace – Maps your current project directory into the container.
• ~/.kode:/root/.kode – Ensures Kode’s configuration directory persists across sessions.
• ~/.kode.json:/root/.kode.json – Maintains your global configuration settings.

Environment

• Working Directory: Set to /workspace inside the container.
• Interactive Mode: The -it flag provides a fully interactive terminal.
• Cleanup: The --rm flag automatically removes the container once you exit.

The initial run will build the image, while subsequent runs will start instantly using the cached version. Upon launch, follow the prompts to select your models. You can change models later using the /model command or manually add OpenAI-compatible endpoints via /config.

Common Commands

• /help – View all available commands and descriptions.
• /model – Access model settings to switch providers or adjust parameters.
• /config – Open the configuration panel to customize behavior.
• /cost – Monitor token usage and associated costs.
• /clear – Reset the conversation history for a fresh start.
• /init – Initialize the project context to help Kode understand your codebase.

Multi-Model Collaboration Under the Hood

While the official Claude CLI restricts users to a single model, Kode orchestrates multiple models simultaneously, allowing you to delegate tasks based on specific model strengths.

Core Architecture

1. ModelManager A unified system for managing model profiles:

• Profile Storage: Each profile stores API endpoints, authentication keys, context window limits, and pricing parameters.
• Model Pointers: Assign specific models to different roles via /model:
  • main: The default model for the primary agent.
  • task: The default model for sub-agents.
  • reasoning: Reserved for deep-thinking tasks (e.g., ThinkTool).
  • quick: A fast, cost-effective model for lightweight tasks like safety checks or title generation.
• Dynamic Switching: Transition between models during a session without losing your progress or context.

2. TaskTool: Intelligent Task Delegation The TaskTool serves as the architectural core, enabling:

• Sub-agent spawning: Launch several sub-agents to handle multiple tasks in parallel.
• Parameter passing: Specify a model for a sub-agent directly within a request.
• Inheritance: Sub-agents automatically use the model designated by the task pointer unless otherwise specified.

3. AskExpertModel: Specialist Consultations This feature allows you to:

• Invoke specialists: Call upon a specific model for a one-off difficult problem.
• Maintain flow: Expert responses are kept separate to avoid cluttering the main conversation.
• Integration: Easily apply the expert's insights to your current project.

How to Switch Models

Instant Toggle: Press the Tab key while in the input field to cycle through your configured models.

Command Access: Use /model to manage profiles and reassign roles.

Manual Overrides: You can specify a particular model by name at any prompt to handle a specific request.

Strategic Workload Distribution

Architecture Phase
Utilize models like o3 or GPT-5 for high-level system design. These models are optimized for abstract reasoning and complex planning.

Refinement Phase
Switch to Gemini to flesh out production-level designs, balancing engineering requirements with practical trade-offs.

Implementation Phase
Deploy Qwen Coder, Kimi K2, GLM-4.5, or Claude Sonnet 4 for the actual coding. These models excel at file generation and precise editing. Sub-agents can also be used here to refactor multiple modules at once.

Problem-Solving Phase
When encountering logic errors or obscure bugs, consult o3, Claude Opus 4.1, or Grok 4 for deep technical analysis and creative fixes.

Real-World Scenarios

1. Architecture: "Use o3 to design a high-concurrency message queue system."
2. Multi-model flow: "Have GPT-5 analyze this performance bottleneck, then ask Claude Sonnet 4 to implement the fix."
3. Parallelizing: "Use Qwen Coder as a sub-agent to refactor these three files at the same time."
4. Expert Opinion: "Ask Claude Opus 4.1 for a solution to this memory leak without adding it to the main chat history."
5. Code Review: "Have Kimi K2 perform a quality audit on this pull request."

Key Implementation Details

1. Configuration System Example of a multi-model setup:

{
  "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",
    "task": "qwen",
    "reasoning": "o3",
    "quick": "glm-4.5"
  }
}

2. Cost Tracking

• Detailed Statistics: /cost breaks down expenses and token usage per model.
• Economic Optimization: Compare costs in real time to choose the most efficient model for a task.

3. Context Manager

• Seamless Inheritance: Switch models without losing the thread of your conversation.
• Auto-Adaptation: Automatically adjusts data flow to match the context limits of the active model.

The Multi-Model Advantage

• Efficiency: Use the right tool for the right job, rather than a "one size fits all" approach.
• Budget Control: Reserve expensive models for complex reasoning while using cheaper models for routine tasks.
• Concurrency: Execute multiple sub-tasks simultaneously using different sub-agents.
• Versatility: Pivot between different providers and models instantly as your project needs change.

Kode vs. Official Claude CLI

Development Setup

Kode is built with modern tooling and requires Bun.

Install Bun

macOS / Linux:

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

Windows:

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

Set Up the Environment

1. Clone and Enter Repository

git clone https://github.com/shareAI-lab/kode.git
cd kode

2. Install Dependencies

bun install

3. Run Development Mode

bun run dev

Build and Test

# Build the project
bun run build

# Run the test suite
bun test

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