# ADR-003: Memory System Architecture

## Status
**Proposed**

## Context

Current AI frameworks lack sophisticated memory systems, limiting their ability to maintain context across interactions and learn from experience:

- **DSPy**: No built-in memory system, relies on external storage
- **LangChain**: Basic conversation memory with limited scalability
- **Current Kailash**: SemanticMemoryStoreNode exists but is not integrated into a comprehensive system

Enterprise AI applications require:
- **Persistent Context**: Long-term conversation and workflow memory
- **Security**: Encrypted, access-controlled memory with audit trails
- **Scalability**: Support for millions of concurrent memory contexts
- **Multi-tenancy**: Isolated memory spaces for different users/organizations
- **Performance**: Sub-100ms memory operations at scale

## Decision

We will build a **distributed, multi-tier memory system** that provides enterprise-grade persistent memory for AI workflows while maintaining high performance and security.

### Architecture Overview

```
┌─────────────────────────────────────────────────────────────┐
│                  KAIZEN MEMORY SYSTEM                      │
├─────────────────────────────────────────────────────────────┤
│  Memory Access Layer                                       │
│  ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐│
│  │ MemoryContext   │ │ MemoryQuery     │ │ MemoryStream    ││
│  │ (Session Mgmt)  │ │ (Vector Search) │ │ (Real-time)     ││
│  └─────────────────┘ └─────────────────┘ └─────────────────┘│
├─────────────────────────────────────────────────────────────┤
│  Memory Storage Layer                                      │
│  ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐│
│  │ Hot Memory      │ │ Warm Memory     │ │ Cold Memory     ││
│  │ (Redis/In-Mem)  │ │ (Vector DB)     │ │ (Object Store)  ││
│  └─────────────────┘ └─────────────────┘ └─────────────────┘│
├─────────────────────────────────────────────────────────────┤
│  Memory Management Layer                                   │
│  ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐│
│  │ Lifecycle Mgmt  │ │ Security Layer  │ │ Replication     ││
│  │ (TTL/Cleanup)   │ │ (Encryption)    │ │ (Multi-Region)  ││
│  └─────────────────┘ └─────────────────┘ └─────────────────┘│
└─────────────────────────────────────────────────────────────┘
```

### Core Components

1. **MemoryContextManager**: Session and context lifecycle management
2. **MemoryStorageEngine**: Multi-tier storage with automatic data movement
3. **MemorySecurityLayer**: Encryption, access control, and audit logging
4. **MemoryQueryEngine**: Vector search and semantic retrieval
5. **MemoryReplicationSystem**: Multi-region consistency and backup

### Memory Types and Usage

```python
from kailash.kaizen.memory import MemoryContext, MemoryType

# Conversation Memory
@signature.stateful
class ChatAgent:
    conversation: MemoryContext = memory(
        type=MemoryType.CONVERSATION,
        ttl="7d",
        max_size="10MB",
        compression=True
    )

    async def execute(self, user_input: str):
        # Access conversation history
        history = await self.conversation.get_recent(limit=10)

        # Store new interaction
        await self.conversation.store({
            "user": user_input,
            "timestamp": datetime.now(),
            "context": self.get_context()
        })

# Episodic Memory
@signature.stateful
class LearningAgent:
    episodes: MemoryContext = memory(
        type=MemoryType.EPISODIC,
        ttl="30d",
        indexing=["semantic", "temporal"]
    )

    async def learn_from_feedback(self, action, outcome, feedback):
        episode = {
            "action": action,
            "outcome": outcome,
            "feedback": feedback,
            "timestamp": datetime.now(),
            "success": outcome.success
        }

        await self.episodes.store(episode)

        # Query similar past episodes
        similar = await self.episodes.query(
            semantic_similarity=action,
            filters={"success": True},
            limit=5
        )

# Procedural Memory
@signature.stateful
class TaskAgent:
    procedures: MemoryContext = memory(
        type=MemoryType.PROCEDURAL,
        ttl="permanent",
        versioning=True
    )

    async def execute_task(self, task_type: str):
        # Retrieve learned procedures
        procedure = await self.procedures.get_latest(
            key=f"procedure:{task_type}"
        )

        if procedure:
            return await self.execute_procedure(procedure)
        else:
            return await self.learn_new_procedure(task_type)
```

### Multi-Tier Storage Strategy

#### Hot Memory (Redis/In-Memory)
- **Purpose**: Immediate access data (current session, recent context)
- **Capacity**: 1-10MB per context
- **Latency**: <1ms access time
- **TTL**: Minutes to hours

#### Warm Memory (Vector Database)
- **Purpose**: Semantic search and medium-term storage
- **Capacity**: 100MB-1GB per context
- **Latency**: <50ms query time
- **TTL**: Days to weeks

#### Cold Memory (Object Storage)
- **Purpose**: Long-term archival and compliance
- **Capacity**: Unlimited
- **Latency**: <1s retrieval time
- **TTL**: Months to years

### Security and Compliance

```python
# Memory with enterprise security
@signature.stateful
class SecureAgent:
    sensitive_data: MemoryContext = memory(
        type=MemoryType.CONVERSATION,
        encryption="AES-256",
        access_control="RBAC",
        audit_logging=True,
        compliance=["SOX", "GDPR", "HIPAA"]
    )

    async def process_sensitive_info(self, data: str):
        # Automatic PII detection and masking
        processed = await self.detect_and_mask_pii(data)

        # Store with full audit trail
        await self.sensitive_data.store(
            processed,
            metadata={
                "user_id": self.current_user,
                "classification": "confidential",
                "retention_policy": "7_years"
            }
        )
```

## Consequences

### Positive
- **Enterprise Ready**: Built-in encryption, access control, and compliance
- **High Performance**: Multi-tier architecture optimizes for access patterns
- **Scalability**: Distributed design supports millions of contexts
- **Developer Experience**: Simple, intuitive API for complex memory operations
- **Cost Optimization**: Automatic data tiering reduces storage costs
- **Multi-tenancy**: Secure isolation between users and organizations

### Negative
- **Complexity**: Sophisticated system requires careful operation and monitoring
- **Resource Usage**: Memory system consumes additional compute and storage
- **Consistency Challenges**: Distributed system introduces potential consistency issues
- **Migration Complexity**: Moving existing workflows to memory-aware signatures

## Alternatives Considered

### Option 1: External Memory Service
**Description**: Use external service like Pinecone or Weaviate
- **Pros**: Proven technology, managed service benefits
- **Cons**: Vendor lock-in, limited customization, security concerns
- **Why Rejected**: Insufficient control for enterprise requirements

### Option 2: Simple Database Integration
**Description**: Use existing DataFlow database capabilities
- **Pros**: Leverages existing infrastructure, simpler implementation
- **Cons**: Not optimized for AI memory patterns, limited scalability
- **Why Rejected**: Doesn't provide AI-specific memory capabilities

### Option 3: File-Based Memory
**Description**: Store memory as files in object storage
- **Pros**: Simple implementation, unlimited capacity
- **Cons**: Poor performance, no semantic search, security challenges
- **Why Rejected**: Inadequate performance for interactive AI applications

## Implementation Plan

### Phase 1: Core Memory Infrastructure (Weeks 1-4)
- MemoryContext API and basic storage
- Redis integration for hot memory
- Basic encryption and access control
- Memory lifecycle management

### Phase 2: Advanced Storage (Weeks 5-8)
- Vector database integration for warm memory
- Object storage for cold memory
- Automatic data tiering
- Semantic search capabilities

### Phase 3: Enterprise Features (Weeks 9-12)
- Advanced security and compliance
- Multi-tenancy and isolation
- Audit logging and monitoring
- Backup and disaster recovery

### Phase 4: Performance Optimization (Weeks 13-16)
- Caching and performance tuning
- Distributed replication
- Load testing and optimization
- Production deployment tools

## Integration Points

### With Core SDK
- Leverage existing DataFlow database infrastructure
- Integrate with WorkflowBuilder for memory-aware workflows
- Use Node patterns for memory operations

### With DataFlow
- Use DataFlow models for memory metadata
- Leverage database connection pooling
- Integrate with DataFlow's multi-instance isolation

### With Nexus
- Expose memory operations via API endpoints
- Provide CLI tools for memory management
- MCP integration for memory-aware tools

### With Existing AI Nodes
- Enhance LLMAgentNode with memory capabilities
- Upgrade A2ACoordinatorNode for shared memory
- Extend SemanticMemoryStoreNode with new architecture

## Performance Targets

- **Memory Access**: <1ms for hot memory, <50ms for warm memory
- **Throughput**: 100,000+ operations/second per node
- **Capacity**: Support for 1M+ concurrent memory contexts
- **Availability**: 99.9% uptime with automatic failover
- **Consistency**: Strong consistency for critical operations

## Security Requirements

- **Encryption**: AES-256 encryption at rest and in transit
- **Access Control**: Fine-grained RBAC with attribute-based policies
- **Audit Trail**: Complete audit logging for all memory operations
- **Compliance**: SOX, GDPR, HIPAA compliance certification
- **Data Residency**: Support for regional data requirements

## Success Criteria

- **Performance**: Meet all latency and throughput targets
- **Adoption**: 80%+ of Kaizen workflows use memory features
- **Reliability**: Zero data loss incidents in production
- **Security**: Pass external security audit and penetration testing
- **Compliance**: Achieve required compliance certifications

## Related ADRs
- ADR-001: Kaizen Framework Architecture
- ADR-002: Signature Programming Model Implementation
- ADR-004: Model Orchestration Strategy
- ADR-005: Security and Compliance Framework
