Descripción general de AgentForge

# Install
pnpm add @elata-biosciences/agentforge

# Scaffold a simulation folder
npx forge-sim init

# Run the built-in toy scenario
npx forge-sim run --toy

# Launch the Studio dashboard
npx forge-sim studio

import { BaseAgent, type Action, type TickContext } from '@elata-biosciences/agentforge';

export class MyTrader extends BaseAgent {
  async step(ctx: TickContext): Promise<Action | Action[] | null> {
    if (ctx.rng.chance(0.3)) {
      return {
        id: this.generateActionId('buy', ctx.tick),
        name: 'buy',
        params: { amount: ctx.rng.nextInt(1, 100), asset: 'TOKEN' },
      };
    }
    return null;
  }
}

import { defineScenario } from '@elata-biosciences/agentforge';
import { ToyPack, RandomTraderAgent, MomentumAgent } from '@elata-biosciences/agentforge/toy';

export default defineScenario({
  name: 'market-stress',
  seed: 42,
  ticks: 100,
  tickSeconds: 3600,
  pack: new ToyPack({
    assets: [{ name: 'TOKEN', initialPrice: 100, volatility: 0.05 }],
    initialCash: 10000,
  }),
  agents: [
    { type: RandomTraderAgent, count: 10 },
    { type: MomentumAgent, count: 5, params: { threshold: 0.02 } },
  ],
  assertions: [
    { type: 'gt', metric: 'totalVolume', value: 0 },
    { type: 'gte', metric: 'successRate', value: 0.9 },
  ],
});

npx forge-sim run sim/scenarios/market-stress.ts --seed 42

results/market-stress-ci/
├── summary.json          # Run metadata, final metrics, assertion results
├── metrics.csv           # Time-series data for analysis
├── actions.ndjson        # Complete action log
├── gossip.ndjson         # Gossip posts/deliveries
├── config_resolved.json  # Resolved configuration for reproducibility
├── replay_bundle.json    # Exploration trace for replay mode
└── report.md             # Generated report