CLI Overview
The Tarko Agent CLI is a flexible framework built on top of the Agent Kernel (@tarko/agent). It provides a comprehensive command-line interface for deploying and running agents with ease, featuring built-in Web UI and powerful extensibility.
Installation
Install globally via npm:
npm install -g @tarko/agent-cli
Or use with npx for one-time execution:
npx @tarko/agent-cli run my-agent
Quick Start
# Start interactive Web UI (default)
tarko
# Run with built-in agents
tarko run agent-tars # Agent TARS
tarko run omni-tars # Omni-TARS
tarko run mcp-agent # MCP Agent
# Run with custom agent
tarko run ./my-agent.js
# Start headless API server
tarko serve
# Headless mode with direct input
tarko run --headless --input "Analyze current directory structure"
# Pipeline input
echo "Summarize this code" | tarko run --headless
Core Concepts
Deployment Modes
- Interactive Mode (
tarko run) - Web UI for development and testing
- Server Mode (
tarko serve) - Headless API server for production
- Scripting Mode (
tarko run --headless) - Silent execution for automation
Built-in Agents
Tarko CLI includes several ready-to-use agents:
agent-tars - Advanced task automation and reasoning system
omni-tars - Multi-modal agent with comprehensive capabilities
mcp-agent - Model Context Protocol agent for tool integration
Configuration Flexibility
Supports multiple configuration formats with auto-discovery:
tarko.config.ts - TypeScript configuration
tarko.config.yaml - YAML configuration
tarko.config.json - JSON configuration
- Environment variables and CLI arguments
Key Features
Tool & MCP Server Filtering
Filter available tools and MCP servers via configuration:
tarko --tool.include "file_*,web_*" --tool.exclude "dangerous_*"
tarko --mcpServer.include "filesystem" --mcpServer.exclude "experimental_*"
调试支持
通过 --debug 参数启用详细日志:
# 启用调试模式
tarko run --debug
# 使用环境变量
DEBUG=tarko:* tarko run
Use Cases
Development
- Interactive agent development with Web UI
- Real-time debugging and testing
- Hot reload and development mode
Production
- Headless API server deployment
- Docker and Kubernetes integration
- Load balancing and scaling
Automation
- CI/CD pipeline integration
- Batch processing and scripting
- Silent execution with structured output
Testing
- Direct LLM requests for debugging
- Agent behavior validation
- Performance testing and benchmarking
Next Steps
API Reference
For detailed TypeScript definitions: