Mnemosyne: Secure, Federated Memory Engine for AI Agents

• Federated architecture - Data stays in your region/cloud/on-premise, agents query across boundaries
• Automatic tiering - Hot data in-memory, warm in SSD, cold in S3, archived to Glacier
• GDPR right-to-deletion - Permanently erase user data across all memory tiers on request
• Multi-tenant isolation - Cryptographic separation ensures Customer A can't access Customer B's memory
• 100B+ vector scale - Store billions of embeddings with sub-50ms query latency

• Customer service memory - Agents remember past conversations and customer preferences
• Enterprise knowledge base - Agents access company documents, policies, and institutional knowledge
• Multi-tenant SaaS - Isolate customer data with cryptographic guarantees
• Regulatory compliance - Enforce retention policies, data residency, and deletion requirements

When an agent needs to store or retrieve memory, it sends a request to Mnemosyne's Query Engine. The engine evaluates access policies, determines which storage tiers to query, retrieves results, and returns them to the agent—transparently handling federation, tiering, and encryption.

Memory Lifecycle:

1. Ingestion: Agent stores memory (conversation, fact, workflow) via Mnemosyne API
2. Policy Check: THEMIS validates agent has permission to store this data
3. Encryption: Data encrypted with tenant-specific key (AES-256)
4. Indexing: Generate vector embeddings for semantic search, metadata for filters
5. Initial Storage: Store in hot tier (Redis) for fast access
6. Tiering: Background process moves data to warm/cold tiers based on access patterns
7. Retention: Automatic deletion after retention period expires (e.g., 90 days, 7 years)
8. Deletion Requests: GDPR right-to-deletion triggers immediate erasure across all tiers

1. Agent Query: Agent asks for relevant memories (semantic search or metadata filter)
2. Policy Evaluation: THEMIS checks if agent can access requested data
3. Federation Routing: Query planner determines which regions/tiers to search
4. Parallel Queries: Issue sub-queries to hot/warm/cold tiers simultaneously
5. Result Aggregation: Merge results, rank by relevance or timestamp
6. Decryption: Decrypt results with tenant key
7. PII Redaction: Apply data minimization (remove unnecessary fields)
8. Return to Agent: Deliver top-K results (typically 5-20 documents)

• AIOS Agents: Agents access memory via AIOS SDK (transparent to agent code)
• THEMIS Policies: Policy engine controls who can read/write which memories
• Vector Databases: Pinecone, Weaviate, Qdrant, Milvus for embeddings
• Graph Databases: Neo4j, Amazon Neptune for entity relationships
• Time-Series: InfluxDB, TimescaleDB for episodic memory
• Object Storage: S3, Azure Blob, GCS for cold/archive tiers
• Embedding Models: OpenAI, Cohere, HuggingFace, custom models

• Scale: 100B+ vectors, 10PB+ total storage capacity
• Query Latency: < 10ms (hot), < 50ms (warm), < 500ms (cold)
• Throughput: 100,000+ queries/second per cluster
• Consistency: Eventual consistency across regions, strong within region
• Embedding Dimensions: 384-4096 dimensions (OpenAI, Cohere, custom)
• Memory Types: Episodic, semantic, procedural, declarative
• Encryption: AES-256 at rest, TLS 1.3 in transit
• Deployment: Multi-cloud, multi-region, hybrid, on-premise

Storing Memory (Agent Code):

from aios_sdk import MnemosyneClient

memory = MnemosyneClient()

# Store conversation in episodic memory
memory.store(
  type="episodic",
  content="Customer reported billing issue with account #12345",
  metadata={
    "customer_id": "cust_abc123",
    "agent_id": "support-agent-1",
    "timestamp": "2025-01-15T10:30:00Z",
    "category": "billing",
    "sentiment": "frustrated"
  },
  ttl_days=90  // Retain for 90 days per policy
)

# Store fact in semantic memory
memory.store(
  type="semantic",
  content="Customer prefers email communication over phone",
  metadata={
    "customer_id": "cust_abc123",
    "entity_type": "preference"
  },
  ttl_days=365
)

Retrieving Memory (Agent Code):

# Semantic search for relevant past conversations
results = memory.search(
  query="billing issues",
  filters={
    "customer_id": "cust_abc123",
    "category": "billing"
  },
  limit=5
)

for result in results:
  print(f"[{result.timestamp}] {result.content}")
  # Output: [2025-01-15T10:30:00Z] Customer reported billing issue...

GDPR Deletion (Privacy Request):

# Customer exercises right to be forgotten
memory.delete_user(
  customer_id="cust_abc123",
  cascade=True  // Delete across all tiers, including backups
)

# Mnemosyne returns deletion certificate
{
  "deletion_id": "del_xyz789",
  "customer_id": "cust_abc123",
  "deleted_at": "2025-01-20T14:00:00Z",
  "records_deleted": 247,
  "tiers_affected": ["hot", "warm", "cold", "archive"],
  "certificate_hash": "0xabc123...",
  "compliance": ["GDPR Article 17", "CCPA Section 1798.105"]
}