Patterns
📚

Memory Reading/Writing Operations(MRWO)

Systematic operations for reading, writing, and managing memory access patterns based on recency, relevance, and importance for multi-agent agentic AI systems

Complexity: mediumMemory Management

🎯 30-Second Overview

Pattern: Systematic operations for reading, writing, and managing memory access based on intelligent scoring

Why: Optimized retrieval performance, multi-agent coordination, intelligent memory prioritization

Key Insight: Composite Scoring (R×0.3 + R×0.5 + I×0.2) + Caching + Concurrency Control → Intelligent memory access

⚡ Quick Implementation

1Score Memories:Calculate recency, relevance, importance weights
2Optimize Retrieval:Use composite scoring for memory selection
3Coordinate Access:Multi-agent read/write synchronization
4Cache Frequently:Cache high-scoring memories for performance
5Update Dynamically:Real-time scoring updates based on usage
Example: memory_scoring → retrieval_optimization → access_coordination → caching_strategy → dynamic_updates

📋 Do's & Don'ts

Use composite scoring: (Recency × 0.3) + (Relevance × 0.5) + (Importance × 0.2)
Implement read-write locks for multi-agent memory consistency
Cache frequently accessed memories with TTL expiration
Use approximate nearest neighbor search for large memory stores
Implement memory access patterns analytics for optimization
Use fixed scoring weights - adapt to agent/domain needs
Ignore memory access conflicts in multi-agent environments
Cache without considering memory staleness and updates
Use linear search for large-scale memory retrieval
Forget to implement memory garbage collection strategies

🚦 When to Use

Use When

  • Large-scale memory systems (1M+ entries)
  • Multi-agent concurrent memory access
  • Performance-critical retrieval scenarios
  • Complex memory ranking requirements
  • Real-time memory optimization needed

Avoid When

  • Small memory stores (< 1000 entries)
  • Single-agent simple retrieval
  • Static memory without updates
  • Memory privacy isolation required
  • Ultra-low latency requirements (< 1ms)

📊 Key Metrics

Retrieval Precision
% relevant memories returned
Access Latency
P50/P95/P99 memory read times
Cache Hit Rate
% requests served from cache
Concurrency Efficiency
Multi-agent access throughput
Memory Freshness
Average age of retrieved memories
Storage Efficiency
Useful memories / total storage

💡 Top Use Cases

Customer Service Systems: 10M+ interaction history with 50ms retrieval (recency + customer relevance scoring)
Recommendation Engines: User behavior patterns with real-time preference weighting (collaborative filtering optimization)
Conversational AI: Context-aware dialogue history with semantic similarity ranking (coherent long conversations)
Knowledge Management: Corporate knowledge base with expertise-weighted retrieval (subject matter expert routing)
Multi-Agent Gaming: Shared world state with spatial-temporal memory access (coordinated decision making)

References & Further Reading

Deepen your understanding with these curated resources

Contribute to this collection

Know a great resource? Submit a pull request to add it.

Contribute

Patterns

closed

Loading...

Built by Kortexya