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Getting Started

Prerequisites​

  • Node.js 22+ (required for built-in node:sqlite)
  • npm, yarn, or pnpm
  • API Key (Gemini, OpenAI, or Anthropic) for LLM agents

Installation​

# Install as a library
npm install @game_ryo/lsji

# Or use CLI directly with npx
npx @game_ryo/lsji --help

Quick Start: AI Agent (New in v0.3+)​

1. Set Your API Key​

# Option 1: Gemini (recommended - free tier available)
export GEMINI_API_KEY="your-gemini-key"

# Option 2: OpenAI
export OPENAI_API_KEY="your-openai-key"

# Option 3: Anthropic
export ANTHROPIC_API_KEY="your-anthropic-key"

# Option 4: Local (Ollama)
# No API key needed, just run: ollama serve

2. Start the Runtime Server with Web UI​

npx @game_ryo/lsji serve

Open http://localhost:3456 — you'll see the web control panel where you can:

  • Create new agent runs with a task description
  • Watch real-time thought logs (💭 thinking, ⚡ actions, 👁 observations)
  • Approve/reject sensitive operations (file writes, API calls, code execution)
  • Monitor budget/cost in real-time

3. Run an Agent Task via CLI​

# Simple task with Gemini
npx @game_ryo/lsji agent run --task "Research TypeScript best practices and create a summary" --provider gemini --model gemini-1.5-flash

# Durable run with checkpointing (resumable after crash)
npx @game_ryo/lsji agent run-durable --task "Analyze codebase and create report" --workflow-id my-analysis

# With budget limits
npx @game_ryo/lsji agent run --task "Write a report" --max-cost 5 --max-tokens 50000

4. Manage Approvals​

# List pending approvals
npx @game_ryo/lsji hitl list

# Approve via CLI
npx @game_ryo/lsji hitl approve --id <approval-id> --reason "Approved by human"

Quick Start: RL Agent (Legacy)​

1. Train an Agent​

# Train with default settings (200 episodes, random pattern)
npx @game_ryo/lsji train --episodes 500

# Train against specific opponent
npx @game_ryo/lsji train --episodes 1000 --opponent counter

2. Play Against the Agent​

# Play Rock (0)
npx @game_ryo/lsji play --hand 0

# Play Paper (2)
npx @game_ryo/lsji play --hand 2

3. Check Status​

npx @game_ryo/lsji status --json

Using as a Library​

AI Agent (v0.3+)​

import { createLLMAgent, startServer } from '@game_ryo/lsji';

async function main() {
  // Create agent with full production features
  const agent = await createLLMAgent({
    llm: { 
      provider: 'gemini', 
      model: 'gemini-1.5-flash', 
      apiKey: process.env.GEMINI_API_KEY 
    },
    budget: { maxCostPerRun: 5, maxCostPerDay: 50 },
    hitl: { enabled: true, defaultTimeout: 300000 },
    memory: { conversation: true, episodic: true, semantic: true },
  });

  const result = await agent.run("Research TypeScript best practices and create a summary");
  console.log(result.answer);

  await agent.shutdown();
}

// Or start the full runtime server
const server = await startServer({ port: 3456 });
// Visit http://localhost:3456 for the web control panel

RL Agent (Legacy)​

import { Agent, QLearning, createStorage, RockPaperScissorsEnv } from '@game_ryo/lsji';

async function main() {
  const storage = await createStorage('sqlite', { path: './my-agent.db' });
  const qlearning = new QLearning({
    alpha: 0.1, gamma: 0.9, epsilon: 0.1, storage
  });
  const env = new RockPaperScissorsEnv({ opponent: 'random' });
  const agent = new Agent({ qlearning, storage, env });

  await agent.train({ episodes: 1000 });
  const result = await agent.play(0);
  console.log(result);

  await storage.close();
}

main().catch(console.error);

Next Steps​