memory-design-patterns

# Memory Design Patterns

Production-ready memory architecture patterns for AI applications using Mem0. This skill provides comprehensive guidance on designing scalable, performant memory systems with proper isolation, retention strategies, and optimization techniques.

## Instructions

### Phase 1: Understand Memory Types

Mem0 provides three distinct memory scopes, each serving different purposes:

#### 1. User Memory (Persistent Preferences & Profile)
**Purpose**: Long-term personal preferences, profile data, and user characteristics that persist across all interactions.

**Use Cases**:
- User preferences (dietary restrictions, communication style, language preferences)
- Personal information (location, occupation, family details)
- Long-term goals and interests
- Historical context that should persist indefinitely

**Implementation**:
```python
# Add user-level memory
memory.add(
    "User prefers concise responses without technical jargon"
    user_id="customer_bob"
)

# Search user memories
user_context = memory.search(
    "communication style"
    user_id="customer_bob"
)
```

**Key Characteristics**:
- Persists indefinitely (or until explicitly deleted)
- Shared across all agents interacting with this user
- Should contain stable, long-term information
- Typically 10-50 memories per user

#### 2. Agent Memory (Agent-Specific Context)
**Purpose**: Agent-specific knowledge, behaviors, and learned patterns that apply across all users interacting with this agent.

**Use Cases**:
- Agent capabilities and limitations
- Domain-specific knowledge
- Learned behaviors and patterns
- Agent-specific instructions and protocols

**Implementation**:
```python
# Add agent-level memory
memory.add(
    "When handling refund requests, always check order date first"
    agent_id="support_agent_v2"
)

# Search agent memories
agent_context = memory.search(
    "refund process"
    agent_id="support_agent_v2"
)
```

**Key Characteristics**:
- Shared across all users interacting w