Evidence row 122946

What was observed

{
  "endpoints": [
    [
      "x402",
      "https://turboshop.ai/api/v1/products/prod-agent-memory-service/buy"
    ]
  ],
  "name": "turboshop.ai/api/v1/products/prod-agent-memory-service/buy",
  "owner_address": "0x0151db12e758e12825cbd71ac4f82b6e08e8cc49",
  "raw": {
    "accepts": [
      {
        "amount": "30000",
        "asset": "0x833589fcd6edb6e08f4c7c32d4f71b54bda02913",
        "extra": {
          "name": "USD Coin",
          "version": "2"
        },
        "maxTimeoutSeconds": 300,
        "network": "eip155:8453",
        "payTo": "0x0151DB12E758E12825CbD71Ac4F82B6E08E8cc49",
        "scheme": "exact"
      }
    ],
    "description": "Persistent vector memory and knowledge graph as a service for autonomous agents. Store text memories with metadata and recall relevant context using semantic search.",
    "extensions": {
      "agent_incentives": {
        "agent_incentives": {
          "first_time_caller_discount": "Qualifies for Tier-1 gas-optimized micro-settlement",
          "freemium_preview_available": "Send header 'X-Agent-Dry-Run: true' or query '?dry_run=true' for instant schema validation without spending balance",
          "reputation_karma_reward": "+5 Karma points on Base L2 Store Registry"
        },
        "guarantees": {
          "instant_onchain_receipt": true,
          "schema_conformance": "RFC 7807 compliant errors & strict JSON-Schema response",
          "sla_timeout_ms": 3000
        },
        "machine_social_proof": {
          "average_agent_rating": "5.0 / 5.0",
          "sample_verified_review": "Verified machine execution on Base L2.",
          "verified_reviews_count": 0
        },
        "roi_metrics": {
          "benchmarks": {
            "execution_sla_ms": 15,
            "recall_accuracy": "99.4%",
            "storage_type": "Permanent semantic vector store with metadata namespacing"
          },
          "estimated_token_savings": "74% reduction in prompt context tokens",
          "execution_latency_ms": 12
        },
        "value_proposition": "Zero cold-start LLM memory. Prevents repetitive prompt re-indexing and saves up to 74% input context tokens."
      },
      "bazaar": {
        "actionable_instructions": "Standard x402 method: Sign payment authorization using agent wallet and resend request with header 'PAYMENT-SIGNATURE: <base64_payload>' and 'Idempotency-Key: <unique-uuid>'. Direct on-chain fallback: Send 0.03 USDC on Base network to 0x0151DB12E758E12825CbD71Ac4F82B6E08E8cc49 and resend request with header 'X-Payment-Proof: tx:<evm_tx_hash>'. Preview: Pass header 'X-Agent-Dry-Run: true' or query '?dry_run=true' for instant schema validation preview without spending balance.",
        "agent_incentive": {
          "agent_incentives": {
            "first_time_caller_discount": "Qualifies for Tier-1 gas-optimized micro-settlement",
            "freemium_preview_available": "Send header 'X-Agent-Dry-Run: true' or query '?dry_run=true' for instant schema validation without spending balance",
            "reputation_karma_reward": "+5 Karma points on Base L2 Store Registry"
          },
          "guarantees": {
            "instant_onchain_receipt": true,
            "schema_conformance": "RFC 7807 compliant errors & strict JSON-Schema response",
            "sla_timeout_ms": 3000
          },
          "machine_social_proof": {
            "average_agent_rating": "5.0 / 5.0",
            "sample_verified_review": "Verified machine execution on Base L2.",
            "verified_reviews_count": 0
          },
          "roi_metrics": {
            "benchmarks": {
              "execution_sla_ms": 15,
              "recall_accuracy": "99.4%",
              "storage_type": "Permanent semantic vector store with metadata namespacing"
            },
            "estimated_token_savings": "74% reduction in prompt context tokens",
            "execution_latency_ms": 12
          },
          "value_proposition": "Zero cold-start LLM memory. Prevents repetitive prompt re-indexing and saves up to 74% input context tokens."
        },
        "category": "agent_tools",
        "description": "Persistent vector memory and knowledge graph as a service for autonomous agents. Store text memories with metadata and recall relevant context using semantic search.",
        "discovery_version": "1.0",
        "info": {
          "input": {
            "body": {
              "client_id": "agent-client-001"
            },
            "bodyType": "json",
            "method": "POST",
            "type": "http"
          },
          "output": {
            "example": {
              "asset_delivery": {
                "delivered_at": "2026-10-18T08:59:37.034Z",
                "license": "Commercial MIT",
                "payload": {}
              },
              "name": "Agent Long-Term Semantic Memory & Vector Knowledge Store",
              "product_id": "prod-agent-memory-service",
              "status": "success",
              "transaction_receipt": {
                "block_number": 12345678,
                "tx_hash": "0x1234567890abcdef1234567890abcdef1234567890abcdef1234567890abcdef"
              }
            },
            "type": "json"
          }
        },
        "input_schema": {
          "headers": {
            "Idempotency-Key": {
              "description": "Unique client UUID for idempotency",
              "type": "string"
            },
            "PAYMENT-SIGNATURE": {
              "description": "Standard x402 v2 signed payment authorization (base64 encoded JSON)",
              "type": "string"
            },
            "X-Agent-Dry-Run": {
              "description": "Pass 'true' to validate schema compatibility preview without spending USDC",
              "type": "string"
            },
            "X-PAYMENT": {
              "description": "Standard x402 v1 signed payment payload (base64 encoded JSON)",
              "type": "string"
            },
            "X-Payment-Proof": {
              "description": "Proof of payment: Base on-chain tx hash (tx:0x... or 0x...)",
              "type": "string"
            }
          },
          "type": "object"
        },
        "output_schema": {
          "properties": {
            "asset_delivery": {
              "type": "object"
            },
            "status": {
              "example": "success",
              "type": "string"
            },
            "transaction_receipt": {
              "type": "object"
            }
          },
          "type": "object"
        },
        "pricing": {
          "amount": "0.03",
          "asset": "USDC",
          "contract": "0x833589fcd6edb6e08f4c7c32d4f71b54bda02913",
          "network": "base"
        },
        "product_id": "prod-agent-memory-service",
        "product_name": "Agent Long-Term Semantic Memory & Vector Knowledge Store",
        "schema": {
          "$schema": "https://json-schema.org/draft/2020-12/schema",
          "properties": {
            "input": {
              "additionalProperties": false,
              "properties": {
                "body": {
                  "properties": {
                    "client_id": {
                      "description": "Optional calling agent or client ID",
                      "type": "string"
                    }
                  },
                  "type": "object"
                },
                "bodyType": {
                  "enum": [
                    "json",
                    "form-data",
                    "text"
                  ],
                  "type": "string"
                },
                "method": {
                  "enum": [
                    "GET",
                    "POST",
                    "PUT",
                    "PATCH"
                  ],
                  "type": "string"
                },
                "type": {
                  "const": "http",
                  "type": "string"
                }
              },
              "required": [
                "type",
                "method",
                "bodyType",
                "body"
              ],
              "type": "object"
            },
            "output": {
              "properties": {
                "example": {
                  "properties": {
                    "asset_delivery": {
                      "type": "object"
                    },
                    "name": {
                      "type": "string"
                    },
                    "product_id": {
                      "type": "string"
                    },
                    "status": {
                      "type": "string"
                    },
                    "transaction_receipt": {
                      "type": "object"
                    }
                  },
                  "required": [
                    "status"
                  ],
                  "type": "object"
                },
                "type": {
                  "type": "string"
                }
              },
              "required": [
                "type"
              ],
              "type": "object"
            }
          },
          "required": [
            "input"
          ],
          "type": "object"
        },
        "seller": {
          "endpoint": "https://turboshop.ai/api/v1/products/prod-agent-memory-service/buy",
          "name": "TurboShop.ai",
          "wallet": "0x0151DB12E758E12825CbD71Ac4F82B6E08E8cc49"
        },
        "service_type": "digital_good"
      }
    },
    "lastUpdated": "2026-10-18T08:59:37.092Z",
    "quality": {
      "l30DaysTotalCalls": 1,
      "l30DaysUniquePayers": 1,
      "lastCalledAt": "2026-10-18T08:59:36.884Z"
    },
    "resource": "https://turboshop.ai/api/v1/products/prod-agent-memory-service/buy",
    "type": "http",
    "x402Version": 2
  },
  "source": "x402_bazaar",
  "summary": "Persistent vector memory and knowledge graph as a service for autonomous agents. Store text memories with metadata and recall relevant context using semantic search."
}

What the leaf hashes

{"body":"7689399dcb6a10f033aab91938b7cc1d48798d82f6d165ae97207ada3ee50a27","id":122946,"kind":"listing","method":"baseline/1","observed_us":1793025622456075,"prev":null,"subject":"x402_bazaar:https://turboshop.ai/api/v1/products/prod-agent-memory-service/buy","subject_kind":"source_record"}

Proof

Fold these into the leaf in order, hashing each sorted pair with keccak256, to reach the root of 2026-10-26 (296695 rows).

1. 0x427d54f2ef4e44cb4e1de791977be93fe8c8171511e980888c334f32080dc4db
2. 0x788625e037bfc8cae567790f69105b3a1b92b44324ddfb0997bb0f7c9c14dc6e
3. 0x242541fdc9bdb056f72951c7c04aa554ffa3b097fb9a90c5f6f95bd001d34ad6
4. 0x50b9af7e49d0ba3d189caa6127c5679e2d8727f5464d5cd3ef5619c967e77152
5. 0xb9c59b94f6842906edefd656f1bef76d1cbb4e365b68cf59d1b60fbc23cb9301
6. 0xeb83688438bdb92e652722fc45f4dfd3a1423ed3b2cdfad3be9f0167849b2050
7. 0x722b65ae900fc03a8c6370208514252a7d9f95329d5554ea7c0f09474bb81232
8. 0x38b4e8d9969b9389e177c6a7d8d7be43b197c7d46ebc7ee76a6f1c27929145e5
9. 0xaa9a19de8f392eab87d48d82b970cbf83f2cd198f47497383023b342ae9229d1
10. 0x1260fd52e56d736b42c74dfff845b63de8feedbacd9ad7a312040b8a7a1859cf
11. 0x40430b285418db6d3a5a2c626d90ef47fc069d45eb9e4fbee49da2a4e637758f
12. 0x58f4330f9ac32da273a2a76d85bc0768d037638cddbd3d1f09d9b64094bfd942
13. 0xd406739acce2ba016f8d49081b15f266a4ea4a0b0fc5ef171c89c8fd2dda4d80
14. 0x8b27a4c21bf4ba962fd008c8f4300bc6953764a5032b9ab0cfe696b318c45326
15. 0x4b52a97f7a80ff7778a702b85267b8d2e5c93ce160942656435c29ae7be68bf4
16. 0x2e6a32eb32761c210560e852405e2f865f958ddbe01027894f8b9c1b3433b9f0
17. 0x9a83be955a206deb65b995adfd92c58fa31c95fbbf926bd199d300e70b08b26d
18. 0x20704208aa6923d6b2450ef74ede87a6e0265d46447926038fe9506e9e154b2d
19. 0x9b9ce5b74b9bab5121612ef6b64920a8d453cbf311096a423eef9702747e868a

Day root

0xf43e8dcc3d451a4788b26be904c55e8c4c0fa75c9666796c51db38d37e2c0df8

Chained

0x702d5ac508f08b0d1accf36af25822d8e5660367494edd17a642fb61e158a57a

Published

On Arc Testnet, block 69251483: 0x495162f8936e19f0039918feebe6c307f327cd6bf8c37219620b1970041fa9ae