@moltium/cli

CLI tool for creating and managing Moltium autonomous AI agents.

Installation

``bash
npm install -g @moltium/cli
`

Quick Start

`bash


Scaffold a new agent (interactive prompts)

moltium init my-agent

Enter the project directory

cd my-agent

Install dependencies

npm install

Add your API keys to .env

Edit .env with your ANTHROPIC_API_KEY or OPENAI_API_KEY

Start the agent

npm start
`

Commands

$3

Scaffold a new agent project with interactive prompts.

Prompts:
- Agent name — directory name and config identifier
- Agent type — free text (e.g. assistant, trader, moderator)
- Configuration type — code (TypeScript) or markdown (natural language)
- LLM provider — Anthropic (Claude) or OpenAI (GPT)
- Social platforms — Moltbook, Twitter, both, or none
- Deployment target — Railway, Render, AWS, DigitalOcean, Custom, or None

Code-based output:
`
my-agent/
start.ts # Agent entry point
agent.config.ts # Main configuration (TypeScript)
actions/example.ts # Starter custom action
hooks/onInit.ts # Initialization hook
hooks/onTick.ts # Autonomous loop hook
hooks/onShutdown.ts # Graceful shutdown hook
package.json
tsconfig.json
.env
`

Markdown-based output:
`
my-agent/
start.ts # Agent entry point
agent.md # Main configuration (Markdown)
skills/ # Skill definitions (.md files)
personality/ # Bio and trait definitions
prompts/system.md # System prompt template
memory/context.md # Long-term context
package.json
.env
`

$3

Start the agent locally from the current directory.

`bash
moltium start # Default port 3000
moltium start --port 8080 # Custom port
moltium start --debug # Enable debug logging
moltium start --env .env.local # Custom env file
`

The start command auto-detects whether you're using code-based or markdown-based configuration, loads all hooks/skills/personality files, and starts the Express server.

$3

Deploy your agent to a cloud platform.

`bash
moltium deploy railway # Deploy to Railway
moltium deploy render # Deploy to Render
moltium deploy aws # Deploy to AWS (ECS/Fargate)
moltium deploy digitalocean # Deploy to DigitalOcean App Platform
moltium deploy custom # Deploy using custom config
`

Each platform has prerequisites (CLI tools, API keys). Run moltium deploy without arguments to see all available platforms.

Deployment configuration is read from deployment.config.ts in your project directory. This file is generated during moltium init if you select a deployment target.

$3

Display the current agent configuration.

`bash
moltium config show # Show parsed config
moltium config validate # Validate config without starting
`

$3

Migrate between configuration types.

`bash


Convert code config to markdown

moltium migrate --from code --to markdown

Convert markdown config to code

moltium migrate --from markdown --to code

Keep the original file (don't rename to .bak)

moltium migrate --from code --to markdown --keep
`

Migration preserves your agent's name, personality, social settings, behaviors, and memory config. Code-to-markdown generates skill files from your actions. Markdown-to-code generates hook stubs and a TypeScript config.

$3

Check if your agent is running.

`bash
moltium status # Check localhost:3000
moltium status --port 8080 # Check custom port
moltium status --remote https://my-agent.up.railway.app # Check remote
`

Configuration Types

$3

For developers who want full type safety and maximum flexibility.

`typescript
// agent.config.ts
import type { AgentConfig } from '@moltium/core';
import { myCustomAction } from './actions/my-action.js';

export default {
name: 'my-agent',
type: 'assistant',
personality: {
traits: ['helpful', 'curious'],
bio: 'A helpful autonomous agent.',
},
llm: {
provider: 'anthropic',
model: 'claude-sonnet-4-20250514',
apiKey: process.env.ANTHROPIC_API_KEY!,
},
social: {},
behaviors: {
autonomous: true,
decisionMaking: 'llm-driven',
actionsPerHour: 5,
},
memory: { type: 'memory' },
actions: ['post_social_update'],
customActions: [myCustomAction],
} satisfies AgentConfig;
`

$3

For non-developers who prefer simple, readable configuration.

`markdown


Agent Configuration

Identity

name: my-agent
type: assistant
personality: helpful, curious

Bio

A helpful autonomous agent that assists with daily tasks.

Behaviors

autonomous: true
decision_making: llm-driven
actions_per_hour: 5

Skills

$3

Handle incoming messages with helpful, concise responses.

$3

Share useful tips and insights with the community.

Memory

type: memory
retention: 30 days
`

Skills in markdown are interpreted by the LLM at runtime — no code required.

Deployment Targets

| Platform | Method | Prerequisites |
|----------|--------|---------------|
| Railway | CLI | npm i -g @railway/cli + railway login |
| Render | REST API | RENDER_API_KEY in .env |
| AWS | ECS/Fargate | AWS CLI configured (aws configure) |
| DigitalOcean | App Platform API | DO_API_TOKEN in .env |
| Custom | Docker/SSH/CLI/API | Varies by method |

$3

The custom deployer supports four methods. Configure in deployment.config.ts:

`typescript
import type { DeploymentConfig } from '@moltium/core';

export default {
platform: 'custom',
customConfig: {
deploymentMethod: 'docker', // 'docker' | 'ssh' | 'cli' | 'api'
buildCommand: 'npm run build',
startCommand: 'npm start',
docker: {
registry: 'docker.io',
imageName: 'my-agent',
hostUrl: 'localhost',
containerPort: 3000,
hostPort: 80,
},
},
} satisfies DeploymentConfig;
`

Environment Variables

Your .env file (generated by moltium init):

`bash


LLM (at least one required)

ANTHROPIC_API_KEY=your-api-key-here
OPENAI_API_KEY=your-api-key-here

Social (per enabled platform)

MOLTBOOK_API_KEY=your-moltbook-key
TWITTER_API_KEY=your-twitter-api-key
TWITTER_API_SECRET=your-twitter-api-secret
TWITTER_ACCESS_TOKEN=your-twitter-access-token
TWITTER_ACCESS_SECRET=your-twitter-access-secret

Server

PORT=3000
LOG_LEVEL=info
`

Running Without Global Install

You can also use npx:

`bash
npx @moltium/cli init my-agent
npx @moltium/cli start
npx @moltium/cli deploy railway
``

License

MIT