memory-optimization

# Memory Optimization

Performance optimization patterns and tools for Mem0 memory systems. This skill provides comprehensive optimization techniques for query performance, cost reduction, caching strategies, and infrastructure tuning for both Platform and OSS deployments.

## Instructions

### Phase 1: Performance Assessment

Start by analyzing your current memory system performance:

```bash
bash scripts/analyze-performance.sh [project_name]
```

This generates a comprehensive performance report including:
- Query latency metrics (average, P95, P99)
- Operation throughput (searches, adds, updates, deletes)
- Cache performance statistics
- Resource utilization (memory, storage, CPU)
- Slow query identification
- Cost analysis

**Review the output to identify optimization priorities:**
- Query latency > 200ms → Focus on query optimization
- High costs → Focus on cost optimization
- Low cache hit rate < 60% → Focus on caching
- High resource usage → Focus on infrastructure tuning

### Phase 2: Query Optimization

Optimize memory search operations for speed and efficiency.

#### 2.1 Limit Search Results

**Problem**: Retrieving too many results increases latency and costs.

**Solution**: Use appropriate limit values based on use case.

```python
# ❌ BAD: Using default or excessive limits
memories = memory.search(query, user_id=user_id)  # Default: 10

# ✅ GOOD: Optimized limits
memories = memory.search(query, user_id=user_id, limit=5)  # Chat apps
memories = memory.search(query, user_id=user_id, limit=3)  # Quick context
memories = memory.search(query, user_id=user_id, limit=10) # RAG systems
```

**Impact**: 30-40% reduction in query time

**Guidelines**:
- Chat applications: 3-5 results
- RAG context retrieval: 8-12 results
- Recommendation systems: 10-20 results
- Semantic search: 20-50 results

#### 2.2 Use Filters to Reduce Search Space

**Problem**: Searching entire index is slow and expensive.

**Solution**: Apply filters to narrow search scope.

```python
# ❌