@smythos/cli

SmythOS SRE CLI

Command line interface for SmythOS SRE (Smyth Runtime Environment) - an advanced agentic AI platform that provides a comprehensive runtime environment for building and managing AI agents.

Installation

pnpm install -g @smythos/cli

Commands Overview

The SRE CLI provides three main commands:

• sre agent - Run SmythOS agent files with various execution modes
• sre create - Create new SmythOS projects
• sre update - Update the CLI and check for updates

Agent Command

Run SmythOS agent files (.smyth) with different execution modes.

Basic Usage

sre agent <path-to-agent.smyth> [options]

Available Modes

1. Chat Mode (--chat)

Start an interactive chat interface with the agent:

sre agent ./myagent.smyth --chat
sre agent ./myagent.smyth --chat claude-3.7-sonnet
sre agent ./myagent.smyth --chat gpt-4o

Options:

• --chat - Start chat with default model (gpt-4o)
• --chat <model> - Start chat with specified model

2. Prompt Mode (--prompt)

Query the agent with a single prompt:

sre agent ./myagent.smyth --prompt "What is the weather in Tokyo?"
sre agent ./myagent.smyth --prompt "Analyze this data" claude-3.7-sonnet

Options:

• --prompt <text> - Send a prompt to the agent
• --prompt <text> <model> - Send a prompt using specific model

3. Skill Execution (--skill)

Execute a specific skill from the agent:

sre agent ./myagent.smyth --skill getUserInfo
sre agent ./myagent.smyth --skill processData input="sample data" format="json"
sre agent ./myagent.smyth --skill ask question="who are you"

Options:

• --skill <skillname> - Execute a skill without parameters
• --skill <skillname> key1="value1" key2="value2" - Execute skill with parameters

4. MCP Server Mode (--mcp)

Start the agent as an MCP (Model Context Protocol) server:

sre agent ./myagent.smyth --mcp
sre agent ./myagent.smyth --mcp stdio
sre agent ./myagent.smyth --mcp sse 3388

Options:

• --mcp - Start MCP server with default settings (stdio)
• --mcp stdio - Start MCP server using stdio transport
• --mcp sse - Start MCP server using SSE transport (default port 3388)
• --mcp sse <port> - Start MCP server using SSE transport on specified port

Global Options

These options work with all execution modes:

Vault Configuration (--vault)

Provide a vault file for secure credential storage:

sre agent ./myagent.smyth --chat --vault ./secrets.vault
sre agent ./myagent.smyth --skill getUserInfo --vault ./myvault.json

Models Configuration (--models)

Specify custom models configuration:

sre agent ./myagent.smyth --chat --models ./custom-models.json
sre agent ./myagent.smyth --prompt "Hello" --models ./prod-models.json

Complete Examples

# Interactive chat with custom vault
sre agent ./agent.smyth --chat --vault ./secrets.vault

# Execute skill with parameters and vault
sre agent ./agent.smyth --skill processData input="test" format="json" --vault ./vault.json

# One-time prompt with specific model and custom models config
sre agent ./agent.smyth --prompt "Summarize this data" claude-3.7-sonnet --models ./models.json

# Start MCP server with vault authentication
sre agent ./agent.smyth --mcp sse 8080 --vault ./secrets.vault

# Chat with multiple configurations
sre agent ./agent.smyth --chat gpt-4o --vault ./vault.json --models ./models.json

Create Command

Create a new SmythOS project with interactive setup:

sre create
sre create "My AI Project"

Features:

• Interactive project setup wizard
• Multiple project templates:
  • Empty Project
  • Minimal: Just the basics to get started
  • Interactive: Chat with one agent
  • Interactive chat with agent selection
• Automatic vault setup with API key detection
• Smart resource folder configuration

Examples:

# Interactive project creation
sre create

# Create project with specific name
sre create "Customer Support Bot"

Update Command

Check for and install CLI updates:

sre update
sre update --check
sre update --force
sre update --package pnpm

Options:

• --check, -c - Only check for updates without installing
• --force, -f - Force update check and installation
• --package, -p <manager> - Specify package manager (npm, pnpm, yarn)

Examples:

# Check and install updates
sre update

# Only check for updates
sre update --check

# Force update with specific package manager
sre update --force --package npm

# Check updates using yarn
sre update --check --package yarn

Global Options

• --help, -h - Show help for any command
• --version - Show CLI version

File Formats

• Agent Files: .smyth files containing agent configuration and workflows
• Vault Files: .json or .vault files for secure credential storage
• Models Files: .json files defining available LLM models

Models Configuration

The --models flag allows you to specify custom model configurations for your agents. You can provide either:

• Single JSON file: A single .json file containing model definitions
• Directory: A directory containing multiple .json files (all will be merged)

Usage Examples

# Single models file
sre agent ./myagent.smyth --chat --models ./models.json

# Directory with multiple model files
sre agent ./myagent.smyth --chat --models ./models-config/

# Multiple model files in a directory
sre agent ./myagent.smyth --skill processData --models ./custom-models/

Models File Format

Each model configuration file should be a JSON object where keys are model names and values are model configurations:

{
    "gemma-3-4b": {
        "provider": "OpenAI",
        "label": "gemma-3-4b-it",
        "modelId": "gemma-3-4b-it",
        "features": ["text", "tools"],
        "tokens": 8000,
        "completionTokens": 512,
        "enabled": true,
        "baseURL": "http://localhost:1234/v1",
        "credentials": ["vault"]
    },
    "gemma-3-1b": {
        "provider": "OpenAI",
        "label": "gemma-3-1b-it",
        "modelId": "gemma-3-1b-it",
        "features": ["text", "tools"],
        "tokens": 4096,
        "completionTokens": 512,
        "enabled": true,
        "baseURL": "http://localhost:1234/v1",
        "credentials": ["vault"]
    }
}

Model Configuration Properties

• provider: The LLM provider (e.g., "OpenAI", "Anthropic", "Google")
• label: Display name for the model
• modelId: The actual model identifier used by the provider
• features: Array of supported features (["text", "tools"])
• tokens: Maximum input tokens supported
• completionTokens: Maximum completion tokens
• enabled: Whether the model is available for use
• baseURL: Custom API endpoint (optional)
• credentials: Array specifying how to retrieve credentials (["vault"])

Directory Structure Example

When using a directory, you can organize models by provider or type:

models-config/
├── openai-models.json
├── anthropic-models.json
├── local-models.json
└── custom-models.json

All JSON files in the directory will be automatically merged, allowing you to organize your model configurations however you prefer.

Configuration

The CLI supports various configuration options through:

• Command-line flags
• Environment variables
• Configuration files
• Interactive prompts during project creation

For detailed configuration options and advanced usage, see the SmythOS documentation.