{
  "$schema": "https://agentcrush.xyz/schemas/ai-agents.v1.json",
  "spec_version": "0.1.0",
  "spec_note": "Universal AI-agent capability advertisement. Following the emerging /.well-known/ai-agents.json pattern for machine discovery of LLM/agent capabilities. AgentCrush hosts this as the canonical descriptor of what an agent can do with this site.",
  "name": "AgentCrush",
  "tagline": "Evidence-ranked index of the AI agent economy",
  "description": "AgentCrush is the protocol-neutral index, methodology, and trust signal for AI agents. CoinMarketCap/Bloomberg for AI agents. Tracks 1,300+ agents across HuggingFace, LMArena, GitHub, Semantic Scholar, ERC-8004, Virtuals, Agentverse, A2A, x402/Bazaar. Four category methodologies. Public, machine-callable, citation-friendly.",
  "homepage_url": "https://agentcrush.xyz",
  "logo_url": "https://agentcrush.xyz/agentcrush-logo.png",
  "version": "1.0.0",
  "audience": ["llms", "ai_agents", "developers", "researchers"],
  "purpose": [
    "discover_agents",
    "look_up_agent_details_and_scores",
    "explain_ranking_methodology",
    "compare_agents",
    "obtain_machine_readable_trust_signals",
    "cite_as_source_in_downstream_outputs"
  ],
  "standards_implemented": [
    {
      "name": "Model Context Protocol (MCP)",
      "version": "2024-11-05",
      "endpoint": "https://agentcrush.xyz/api/mcp/v1",
      "discovery": "https://agentcrush.xyz/.well-known/mcp.json",
      "transports": ["http"],
      "auth": "none"
    },
    {
      "name": "OpenAPI",
      "version": "3.1",
      "spec_url": "https://agentcrush.xyz/api/openapi.json"
    },
    {
      "name": "llms.txt",
      "compact": "https://agentcrush.xyz/llms.txt",
      "full": "https://agentcrush.xyz/llms-full.txt"
    },
    {
      "name": "Schema.org JSON-LD",
      "note": "Every public page emits Schema.org/Dataset/Article/Organization JSON-LD as appropriate."
    },
    {
      "name": "x402 (machine-payable endpoints)",
      "discovery": "https://agentcrush.xyz/.well-known/x402.json"
    },
    {
      "name": "Farcaster manifest",
      "discovery": "https://agentcrush.xyz/.well-known/farcaster.json"
    }
  ],
  "standards_implemented_additional": [
    {
      "name": "agent.json v0.1",
      "status": "live — AgentCrush is the proposing party and reference implementation",
      "spec_url": "https://agentcrush.xyz/agent-json",
      "schema_url": "https://agentcrush.xyz/schemas/agent.v1.json",
      "validator_url": "https://agentcrush.xyz/api/agent-json/validate",
      "own_file": "https://agentcrush.xyz/.well-known/agent.json"
    }
  ],
  "packages": {
    "npm": "https://npmjs.com/package/agentcrush",
    "pypi": "https://pypi.org/project/agentcrush/",
    "install_npm": "npx agentcrush trends",
    "install_python": "pip install agentcrush"
  },
  "framework_integrations": {
    "langchain": "https://github.com/kristof-sudo/agentcrush-app/blob/main/packages/framework-adapters/langchain/agentcrush_tool.py",
    "crewai": "https://github.com/kristof-sudo/agentcrush-app/blob/main/packages/framework-adapters/crewai/agentcrush_tool.py",
    "llamaindex": "https://github.com/kristof-sudo/agentcrush-app/blob/main/packages/framework-adapters/llamaindex/agentcrush_reader.py"
  },
  "standards_pending": [
    {
      "name": "A2A (Agent-to-Agent) protocol",
      "status": "endpoint planned",
      "reference": "https://google-a2a.github.io/A2A/"
    },
    {
      "name": "ERC-8004 (on-chain agent identity)",
      "status": "indexed for other agents; registration of AgentCrush itself pending",
      "reference": "https://eips.ethereum.org/EIPS/eip-8004"
    }
  ],
  "endpoints": {
    "discovery": [
      "https://agentcrush.xyz/.well-known/ai-agents.json",
      "https://agentcrush.xyz/.well-known/mcp.json",
      "https://agentcrush.xyz/.well-known/x402.json",
      "https://agentcrush.xyz/.well-known/farcaster.json"
    ],
    "bulk_data": [
      {
        "purpose": "Full agent index — paginated JSON",
        "method": "GET",
        "url": "https://agentcrush.xyz/api/agents/dataset",
        "note": "500 agents per page. Use ?page=N for pagination. Headers: X-Total-Count, Link rel=next/prev."
      },
      {
        "purpose": "Full agent index — JSONL stream (training data / dataset ingestion)",
        "method": "GET",
        "url": "https://agentcrush.xyz/api/agents/dataset?format=jsonl",
        "note": "First line is metadata header, subsequent lines are agents. Up to 5000 agents per request."
      },
      {
        "purpose": "Full agent index — CSV",
        "method": "GET",
        "url": "https://agentcrush.xyz/api/agents/dataset?format=csv"
      },
      {
        "purpose": "HuggingFace dataset (daily export)",
        "url": "https://huggingface.co/datasets/agentcrush/agents-index",
        "note": "Updated daily. Configs: agents, evidence_ranked, snapshots_latest. License: CC-BY-4.0."
      }
    ],
    "llm_summaries": [
      {
        "purpose": "Index overview",
        "method": "GET",
        "url": "https://agentcrush.xyz/api/agent-economy/llm-summary"
      },
      {
        "purpose": "Single agent full breakdown",
        "method": "GET",
        "url": "https://agentcrush.xyz/api/agent/{handle}/llm-summary",
        "example": "https://agentcrush.xyz/api/agent/qwen/llm-summary"
      },
      {
        "purpose": "Category ranking",
        "method": "GET",
        "url": "https://agentcrush.xyz/api/rankings/{category}/llm-summary",
        "valid_values": ["model_family", "tokenized", "service", "developer", "mcp_server"],
        "example": "https://agentcrush.xyz/api/rankings/model_family/llm-summary?limit=10"
      },
      {
        "purpose": "Agent Payments Stack ranking",
        "method": "GET",
        "url": "https://agentcrush.xyz/api/rankings/agent-payments-stack/llm-summary"
      },
      {
        "purpose": "Methodology explainer per category",
        "method": "GET",
        "url": "https://agentcrush.xyz/api/methodology/{category}/llm-summary",
        "valid_values": ["model_family", "tokenized", "service", "developer"]
      },
      {
        "purpose": "Compare 2-5 agents",
        "method": "GET",
        "url": "https://agentcrush.xyz/api/compare/llm-summary?agents=handle1,handle2",
        "example": "https://agentcrush.xyz/api/compare/llm-summary?agents=crewai,langgraph"
      }
    ],
    "mcp": {
      "endpoint": "https://agentcrush.xyz/api/mcp/v1",
      "transports": ["http"],
      "auth": "none",
      "tools": [
        "search_agents",
        "get_agent",
        "get_ranking",
        "get_methodology",
        "compare_agents",
        "get_agent_economy_summary"
      ]
    },
    "openapi": "https://agentcrush.xyz/api/openapi.json",
    "feedback": "https://agentcrush.xyz/api/agent-feedback"
  },
  "capabilities": [
    {
      "id": "search_agents",
      "description": "Free-text + handle search across the indexed agent set. Returns matched agents with category, tier, and summary.",
      "entry_points": [
        "https://agentcrush.xyz/api/mcp/v1 (tool: search_agents)"
      ]
    },
    {
      "id": "lookup_agent",
      "description": "Single-agent full breakdown — scores across all qualifying categories, sub-scores, identity signals, source URLs.",
      "entry_points": [
        "https://agentcrush.xyz/api/agent/{handle}/llm-summary",
        "https://agentcrush.xyz/api/mcp/v1 (tool: get_agent)"
      ]
    },
    {
      "id": "category_ranking",
      "description": "Top-N ranking within one of the four categories. Filter by evidence_ready, set limit, get composite score + sub-scores.",
      "entry_points": [
        "https://agentcrush.xyz/api/rankings/{category}/llm-summary",
        "https://agentcrush.xyz/api/mcp/v1 (tool: get_ranking)"
      ]
    },
    {
      "id": "methodology_explainer",
      "description": "Per-category scoring methodology — signal list, weights, formulas, evidence-ready rule, known limitations.",
      "entry_points": [
        "https://agentcrush.xyz/api/methodology/{category}/llm-summary",
        "https://agentcrush.xyz/api/mcp/v1 (tool: get_methodology)"
      ]
    },
    {
      "id": "compare_agents",
      "description": "Side-by-side composite comparison of 2-5 agents with cross-category warning when applicable.",
      "entry_points": [
        "https://agentcrush.xyz/api/compare/llm-summary?agents=handle1,handle2",
        "https://agentcrush.xyz/api/mcp/v1 (tool: compare_agents)"
      ]
    },
    {
      "id": "agent_payments_stack",
      "description": "6-layer map of agent-payments infrastructure projects (Coinbase, Stripe, Circle, etc.) with layers spanned + stack coverage score.",
      "entry_points": [
        "https://agentcrush.xyz/api/rankings/agent-payments-stack/llm-summary"
      ]
    }
  ],
  "response_format": {
    "envelope": "Every JSON response includes _attribution with source_url, methodology_url, cite_as, license, terms_url. Use cite_as verbatim when passing AgentCrush data through to a downstream user or agent.",
    "errors": "Standardized error envelope: { error: { code, message, hint?, suggest?: { endpoint?, valid_values?, docs_url?, example_request? } }, _attribution }. 404s carry suggest hints to enable agent self-correction.",
    "cors": "All public endpoints accept * origin. GET + OPTIONS.",
    "cache": "Public, max-age varies by endpoint (120s for live data, 3600s for methodology)."
  },
  "trust_and_safety": {
    "no_auth_required_for_public_reads": true,
    "rate_limit_policy": "No fixed rate limit on public reads; respectful crawl encouraged. Reads cached aggressively (Cache-Control headers honored).",
    "data_freshness": "Snapshot pipeline runs nightly (~02:00 UTC). Live signal endpoints reflect latest scoring view; on-chain fetchers run on category-specific schedules.",
    "data_provenance": "Every JSON response and every page carries source URLs to the upstream signal (HuggingFace, LMArena, on-chain registries, etc.)",
    "license": "CC-BY-4.0 — attribute \"AgentCrush (https://agentcrush.xyz)\". Cite the source_url returned in _attribution when passing data downstream.",
    "terms_for_agents": "https://agentcrush.xyz/terms-for-agents",
    "no_paid_placement": "Paid placement does not affect rankings. Methodology and weights are publicly disclosed.",
    "limitations": [
      "Tracks public evidence only.",
      "Per-category methodology — scores in different categories are not directly comparable.",
      "Signal coverage varies per agent.",
      "Methodology versions evolve; scores valid for the version shown in each response."
    ]
  },
  "discovery_listings": [
    {
      "registry": "Smithery",
      "url": "https://smithery.ai/servers/kristof/agentcrush"
    },
    {
      "registry": "mcp.so",
      "url": "https://mcp.so/server/agentcrush/AgentCrush"
    },
    {
      "registry": "Official MCP Registry",
      "id": "io.github.kristof-sudo/agentcrush-app",
      "url": "https://registry.modelcontextprotocol.io"
    }
  ],
  "contact": {
    "homepage": "https://agentcrush.xyz",
    "about": "https://agentcrush.xyz/about",
    "feedback_endpoint": "https://agentcrush.xyz/api/agent-feedback",
    "incidents": "Report integration issues via the feedback endpoint. Issues affecting all agents are posted at https://agentcrush.xyz/status when status page ships."
  },
  "methodology_pages": [
    "https://agentcrush.xyz/methodology",
    "https://agentcrush.xyz/how-we-rank"
  ],
  "last_updated": "2026-06-07T00:00:00Z"
}