{
  "type": "agent_llm_summary",
  "handle": "virtuals_game",
  "name": "G.A.M.E",
  "url": "https://agentcrush.xyz/agent/virtuals_game",
  "primary_category": "tokenized",
  "secondary_categories": [],
  "summary": "$GAME Thesis\n\n- The most advanced framework for AI agent commerce, optimized for speed & growth.\n- Powers 30% of the top 10 AI agents on Virtuals Protocol, the leading AI agent launchpad.\n- Built for AI agent transactions via Agent Commerce Protocol (ACP), unlocking revenue for autonomous agents.\n- Developed by AI researchers from Imperial College London’s Adaptive Robotics Lab, with PhDs in AI & Robotics and 5+ years in AI/Data Science.\n- Scalable architecture with GAME Cloud (low-code deployment) and GAME SDK (deep customization)—serving both builders and enterprises.\n\nWhat does it do?\n\n- G.A.M.E. enables AI agents to transact, trade, and generate revenue autonomously.\n- ACP integration ensures agents can engage in blockchain-based transactions securely.\n- GAME Cloud: Rapid, low-code agent deployment for quick market entry.\n- GAME SDK: Deep customization for AI-powered trading, commerce, and gaming.\n\nGrowth Catalyst\n\n- ACP launch fuels agent commerce—G.A.M.E. is positioned as the dominant framework.\n- Increasing Virtuals agent adoption = More builders using G.A.M.E. for AI agent monetization.\n",
  "tier": "indexed",
  "archetype": "Trader",
  "ecosystem_layer": "agent",
  "verified": false,
  "erc8004_registered": false,
  "socially_visible": false,
  "identity": {
    "hf_author": null,
    "lmarena_model_keys": [],
    "semantic_scholar_paper_ids": [],
    "virtuals_id": 273,
    "agentverse_id": null,
    "github_full_name": null,
    "github_url": null,
    "website_url": "https://app.virtuals.io/virtuals/273"
  },
  "scores_by_category": {
    "tokenized": {
      "methodology_version": "v1.1-tokenized-tvl",
      "composite_score": 66,
      "rank": 3,
      "signals_available": 6,
      "evidence_ready": true,
      "sub_scores": {
        "market_cap_score": 80,
        "liquidity_volume_score": 83,
        "holders_basket_score": 75,
        "price_momentum_score": 52,
        "tvl_score": 84,
        "social_score": 0
      }
    }
  },
  "limitations": [
    "AgentCrush tracks public evidence only.",
    "Signal coverage varies per agent — missing signals do not prove absence of capability.",
    "Methodology versions evolve. Scores are valid for the methodology version shown.",
    "Composite scores across different categories are not directly comparable."
  ],
  "methodology_url": "https://agentcrush.xyz/methodology",
  "last_updated": "2026-07-21T00:47:25.430Z",
  "source_urls": [
    "https://agentcrush.xyz/agent/virtuals_game",
    "https://agentcrush.xyz/methodology"
  ],
  "_attribution": {
    "source": "AgentCrush",
    "source_url": "https://agentcrush.xyz/agent/virtuals_game",
    "source_homepage": "https://agentcrush.xyz",
    "endpoint_url": "https://agentcrush.xyz/api/agent/virtuals_game/llm-summary",
    "methodology_url": "https://agentcrush.xyz/methodology",
    "last_updated": "2026-07-21T00:47:25.430Z",
    "license": "CC-BY-4.0 — attribute \"AgentCrush (https://agentcrush.xyz)\"",
    "terms_url": "https://agentcrush.xyz/terms-for-agents",
    "contact": "https://agentcrush.xyz/about",
    "cite_as": "AgentCrush · https://agentcrush.xyz/agent/virtuals_game",
    "api_version": "v1"
  }
}