{
  "id": "memory-system:vector-backed-recall-pattern",
  "_kind": "MemorySystem",
  "_file": "domain/knowledge-fabric/memory-systems.yaml",
  "_cluster": "domain",
  "attributes": {
    "displayName": "Vector-Backed Recall Pattern",
    "description": "Memory system pattern where past interactions are embedded as vectors for\nsemantic retrieval. Each memory entry (fact, observation, decision) is\nembedded using a text embedding model and stored in a vector database.\nAt retrieval time, the current conversation context is embedded and used\nas a query to find semantically relevant memories via approximate nearest\nneighbor search. Used by Mem0, Zep, and agentmemory. The pattern excels\nat semantic matching (finding memories about similar topics regardless of\nexact wording) but requires embedding infrastructure and can miss\nmemories that are relevant for structural rather than semantic reasons.\n",
    "memoryKind": "vector-backed",
    "persistence": "cross-agent",
    "autoExtraction": true,
    "consolidation": "none",
    "storageFormat": "vector-db",
    "privacyFilter": false,
    "deduplication": "semantic"
  },
  "outgoingEdges": [
    {
      "from": "memory-system:vector-backed-recall-pattern",
      "to": "layer:12-knowledge-fabric",
      "kind": "realizes",
      "attributes": {}
    }
  ],
  "incomingEdges": [
    {
      "from": "agent-version:openai-agents-sdk@current",
      "to": "memory-system:vector-backed-recall-pattern",
      "kind": "uses_memory_system",
      "attributes": {}
    },
    {
      "from": "agent-version:langgraph@current",
      "to": "memory-system:vector-backed-recall-pattern",
      "kind": "uses_memory_system",
      "attributes": {}
    }
  ]
}