🧠 Mementos System Architecture

Table of Contents

1. Overview
2. Architecture Overview
3. Data Flow & Request Lifecycle
4. Components Deep Dive
5. Memory Management & Grooming
6. Context Window Protection
7. Database Schema & Performance
8. API Endpoints
9. Production Issues & Fixes
10. Monitoring & Alerting
11. Troubleshooting Guide
12. Best Practices

---

Overview

The Mementos System provides intelligent, persistent memory for AI conversations that automatically manages itself to prevent context window overflow and optimize retrieval performance. It implements a three-tier memory hierarchy (HOT/WARM/COLD) with automatic grooming, emergency memory enforcement, and context window protection.

Key Features

• 🔥 Three-Tier Memory System: HOT (immediate access), WARM (recent context), COLD (long-term storage)
• 🛡️ Automatic Memory Management: Intelligent grooming prevents memory bloat
• 🚨 Context Window Protection: Prevents 188K+ token overflow crashes
• ⚡ Performance Optimized: Database queries optimized for real-time access
• 📊 Production Monitoring: Comprehensive logging and alerting
• 🔄 Self-Healing: Automatic recovery from memory limit violations

System Stats

• Memory Limit: 32,000 characters (configurable)
• Context Token Limit: 50,000 tokens emergency cap
• Grooming Triggers: 75% warning, 90% danger, 95% emergency
• Database: MongoDB with optimized indexes
• Performance: Sub-100ms context retrieval for 1000+ mementos

---

Architecture Overview

graph TB
    subgraph "Client Layer"
        UI[ProfileModal/MementosTabContent.tsx]
        API_CALLS[mementosAPICalls.ts]
        HOOKS[useApi Hook]
    end
    
    subgraph "API Layer"
        CREATE[/api/mementos/create]
        BATCH[/api/mementos/create-batch]
        LIST[/api/mementos/list]
        UPDATE[/api/mementos/update]
        DELETE[/api/mementos/delete]
    end
    
    subgraph "Core Services"
        GROOMING[MementoGroomingService]
        EVALUATION[MementoEvaluationService]
        CHAT_FEATURES[ChatCompletionFeatures]
    end
    
    subgraph "Memory Management"
        HOT[(HOT Tier<br/>Immediate Access)]
        WARM[(WARM Tier<br/>Recent Context)]
        COLD[(COLD Tier<br/>Long-term Storage)]
        GROOMER[Grooming Engine]
    end
    
    subgraph "Database"
        MONGO[(MongoDB<br/>Mementos Collection)]
        INDEXES[Optimized Indexes]
    end
    
    subgraph "Context Integration"
        CHAT[Chat Completion]
        CONTEXT[Context Window<br/>Protection]
        TOKENS[Token Limiting]
    end

    UI --> API_CALLS
    API_CALLS --> HOOKS
    HOOKS --> CREATE
    HOOKS --> BATCH
    HOOKS --> LIST
    HOOKS --> UPDATE
    HOOKS --> DELETE
    
    CREATE --> GROOMING
    BATCH --> GROOMING
    CREATE --> EVALUATION
    
    GROOMING --> HOT
    GROOMING --> WARM
    GROOMING --> COLD
    GROOMING --> GROOMER
    
    CHAT_FEATURES --> CONTEXT
    CONTEXT --> TOKENS
    CONTEXT --> HOT
    
    HOT --> MONGO
    WARM --> MONGO
    COLD --> MONGO
    MONGO --> INDEXES

---

Data Flow & Request Lifecycle

1. Memento Creation Flow

// Example: User creates a memento
User Input → MementosTabContent.tsx 
→ createMemento() in mementosAPICalls.ts
→ useApi hook (adds auth)
→ POST /api/mementos/create
→ Memory limit check (95% threshold)
→ Emergency grooming (if needed)
→ MementoEvaluationService.evaluate()
→ Database insertion
→ Background grooming trigger
→ Response to client
→ UI update with toast

2. Chat Completion Integration Flow

// Example: AI needs context for response
Chat Request → ChatCompletionFeatures.ts
→ MementoFeature.getContextMessages()
→ findHotMementosByUserId() (HOT tier only)
→ Token limit enforcement (50K cap)
→ Context message assembly
→ LLM API call with safe context
→ Response generation
→ Automatic memento creation
→ Memory limit check before creation
→ Grooming if needed

3. Memory Grooming Flow

// Example: Memory limit exceeded
Memory Check → 90% threshold exceeded
→ MementoGroomingService.checkAndScheduleGrooming()
→ Synchronous grooming (if forceImmediate=true)
→ groomWarmToCold() (15% of WARM → COLD)
→ groomHotToWarm() (reduce HOT to 80% target)
→ Database batch updates
→ Memory recalculation
→ Logging and monitoring

---

Components Deep Dive

Frontend Components

MementosTabContent.tsx

Location: packages/client/app/components/ProfileModal/MementosTabContent.tsx

Responsibilities:

• Interactive memento management interface
• Real-time memory usage visualization
• Inline editing with optimistic UI updates
• CSV export/import functionality
• Search and filtering capabilities

Key Features:

• Joy UI components (not Material UI)
• React Query for server state
• Optimistic updates with rollback
• Memory usage progress bars
• Tier-based color coding

Performance Optimizations:

• Virtualized lists for 1000+ mementos
• Debounced search input
• Memoized sorting and filtering
• Lazy loading of large content

mementosAPICalls.ts

Location: packages/client/app/utils/mementosAPICalls.ts

Purpose: Type-safe API abstraction layer

export interface MementoAPIResponse {
  createMemento: (data: CreateMementoRequest) => Promise<IMementoDocument>;
  createMementosBatch: (data: CreateMementoRequest[]) => Promise<IMementoDocument[]>;
  updateMemento: (id: string, data: UpdateMementoRequest) => Promise<IMementoDocument>;
  deleteMemento: (id: string) => Promise<void>;
  listMementos: (params: ListMementosParams) => Promise<PaginatedMementos>;
}

Backend Services

MementoGroomingService

Location: packages/client/services/MementoGroomingService.ts

Critical Features:

• Synchronous Emergency Grooming: Prevents memory overflow in real-time
• Asynchronous Background Grooming: Regular maintenance
• Memory Threshold Management: 75%, 90%, 95% thresholds
• Tier Migration Logic: HOT→WARM→COLD transitions

export class MementoGroomingService {
  // Emergency synchronous grooming for API endpoints
  async forceImmediateGrooming(userId: string): Promise<void>
  
  // Background asynchronous grooming
  async checkAndScheduleGrooming(userId: string): Promise<void>
  
  // Core grooming logic
  private async groomHotToWarm(userId: string): Promise<void>
  private async groomWarmToCold(userId: string): Promise<void>
}

MementoEvaluationService

Location: b4m-core/packages/core/services/llm/MementoEvaluationService.ts

Purpose: AI-powered memento analysis and scoring

Features:

• Content summarization
• Importance scoring (0-1000)
• Tag extraction
• Semantic analysis
• Quality assessment

ChatCompletionFeatures

Location: b4m-core/packages/core/services/llm/ChatCompletionFeatures.ts

Critical Production Fixes:

• HOT-Only Context Loading: Prevents loading ALL mementos
• Token Limit Enforcement: 50K emergency cap
• Memory Limit Checks: Before automatic memento creation
• Graceful Degradation: Skips creation if memory full

---

Memory Management & Grooming

Three-Tier Memory System

Tier	Purpose	Size Limit	Access Pattern	Migration Rules
HOT	Immediate context	80% of total	Every chat completion	Lowest weight → WARM
WARM	Recent memory	No strict limit	Similarity search	Lowest weight → COLD
COLD	Long-term storage	Unlimited	Explicit retrieval	Archive or delete

Memory Limits & Thresholds

export const MEMORY_LIMITS = {
  DEFAULT_MAX_TOTAL_CHARS: 32000,
  WARNING_THRESHOLD: 0.75,    // 75% - Background grooming
  DANGER_THRESHOLD: 0.9,      // 90% - Aggressive grooming  
  EMERGENCY_THRESHOLD: 0.95,  // 95% - Synchronous grooming
};

export const TARGET_THRESHOLDS = {
  HOT_TARGET: 0.8,     // Reduce HOT to 80% after grooming
  HOT_TRIGGER: 0.9,    // Trigger when HOT reaches 90%
  WARM_TARGET: 0.8,    // Reduce WARM to 80% after grooming
  WARM_TRIGGER: 0.9,   // Trigger when WARM reaches 90%
};

Grooming Algorithm

Phase 1: WARM → COLD Migration

private async groomWarmToCold(userId: string): Promise<void> {
  const warmMementos = await Memento.find({
    userId,
    tier: MementoTier.WARM,
  }).sort({ weight: 1, lastAccessedAt: 1 }); // Lowest priority first

  // Target 15% of WARM mementos for demotion
  const targetCount = Math.ceil(warmMementos.length * 0.15);
  const memosToDowngrade = warmMementos.slice(0, targetCount);
  
  // Batch update for performance
  await Memento.updateMany(
    { _id: { $in: memosToDowngrade.map(m => m._id) } },
    { $set: { tier: MementoTier.COLD } }
  );
}

Phase 2: HOT → WARM Migration

private async groomHotToWarm(userId: string, maxTotalChars: number): Promise<void> {
  const hotMementos = await Memento.find({
    userId,
    tier: MementoTier.HOT,
  }).sort({ weight: 1, lastAccessedAt: 1 });

  const currentHotSize = calculateHotMementoSize(hotMementos);
  const targetSize = maxTotalChars * TARGET_THRESHOLDS.HOT_TARGET;
  
  // Calculate exact amount to remove
  const sizeToRecover = currentHotSize - targetSize;
  let recoveredSize = 0;
  const memosToDowngrade = [];

  for (const memo of hotMementos) {
    if (recoveredSize >= sizeToRecover) break;
    
    recoveredSize += calculateMementoSize(memo);
    memosToDowngrade.push(memo._id);
  }

  // Batch migration
  await Memento.updateMany(
    { _id: { $in: memosToDowngrade } },
    { $set: { tier: MementoTier.WARM } }
  );
}

Emergency Memory Enforcement

Critical Feature: Prevents the 161% memory overflow issue

// In API endpoints (create.ts, create-batch.ts)
const projectedUsagePercent = projectedHotSize / maxTotalChars;

if (projectedUsagePercent > 0.95) {
  // Force immediate synchronous grooming
  await groomingService.checkAndScheduleGrooming(req.user.id, maxTotalChars, true);
  
  // Re-check after grooming
  const postGroomUsagePercent = calculateNewUsage();
  
  if (postGroomUsagePercent > 1.0) {
    return res.status(413).json({
      error: `Memory limit exceeded: ${(postGroomUsagePercent * 100).toFixed(1)}%`,
      currentUsage: Math.round(postGroomUsagePercent * 100),
      maxLimit: 100
    });
  }
}

---

Context Window Protection

The Production Crisis

Problem: Users with large memento collections experienced catastrophic failures:

Error: input length and max_tokens exceed context limit: 188544 + 17000 > 200000

Multi-Layer Protection System

Layer 1: HOT-Only Context Loading

// Before (DANGEROUS - loads ALL mementos)
const allMementos = await this.db.mementos.findByUserId(this.user.id);

// After (SAFE - loads only HOT tier)
const hotMementos = await this.db.mementos.findHotMementosByUserId(this.user.id);

Impact: Reduced context size by 60-90% for typical users

Layer 2: Emergency Token Limiting

async getContextMessages(): Promise<IMessage[]> {
  const MAX_MEMENTO_TOKENS = 50000; // Emergency cap
  let totalTokens = 0;
  const safeMessages: IMessage[] = [];

  for (const memento of hotMementos) {
    const estimatedTokens = Math.ceil(content.length / 3.5);
    
    if (totalTokens + estimatedTokens > MAX_MEMENTO_TOKENS) {
      this.logger.warn(`Token limit reached, skipping remaining mementos`);
      break;
    }
    
    safeMessages.push(message);
    totalTokens += estimatedTokens;
  }
  
  return safeMessages;
}

Layer 3: Context Window Validation

// In ChatCompletion.ts
const contextLimit = modelInfo.contextWindow ?? 200000;
const safetyBuffer = 1000;
const maxSafeInputTokens = contextLimit - maxTokens - safetyBuffer;

if (inputTokens > maxSafeInputTokens) {
  logger.error(`🚨 CRITICAL: Context overflow detected!`, {
    inputTokens,
    maxTokens,
    contextLimit,
    userId: this.user.id
  });
  
  throw new Error(`Context window exceeded: ${inputTokens} + ${maxTokens} > ${contextLimit}`);
}

Layer 4: Model-Specific max_tokens Protection

const modelMaxOutputTokens = modelInfo.max_tokens ?? 16384;
let safeMaxTokens = maxTokens;

if (maxTokens > modelMaxOutputTokens) {
  safeMaxTokens = modelMaxOutputTokens;
  logger.warn(`Adjusting max_tokens from ${maxTokens} to ${safeMaxTokens} for model ${model}`);
}

---

Database Schema & Performance

MongoDB Schema

interface IMementoDocument {
  _id: ObjectId;
  userId: string;           // Index: { userId: 1, tier: 1 }
  sessionId: string;        // Index: { sessionId: 1 }
  questId?: string;         // Index: { questId: 1 }
  
  // Content
  type: MementoType;        // 'prompt' | 'reply' | 'insight' | 'context'
  tier: MementoTier;        // 'hot' | 'warm' | 'cold'
  summary: string;          // AI-generated summary
  fullContent: string;      // Complete content
  
  // Scoring & Management
  weight: number;           // 0-1 importance score
  tags: string[];           // Index: { tags: 1 }
  lastAccessedAt: Date;     // Index: { lastAccessedAt: 1 }
  
  // Metadata
  isArchived: boolean;
  embedding?: number[];     // Vector for similarity search
  metadata?: Record<string, any>;
  
  // Timestamps
  createdAt: Date;
  updatedAt: Date;
}

Critical Database Indexes

// Primary user queries (most important)
db.mementos.createIndex({ userId: 1, tier: 1 });

// HOT tier optimization (for context loading)
db.mementos.createIndex({ userId: 1, tier: 1, weight: -1, lastAccessedAt: -1 });

// Session-based queries
db.mementos.createIndex({ sessionId: 1 });

// Quest tracking
db.mementos.createIndex({ questId: 1 });

// Tag searching
db.mementos.createIndex({ tags: 1 });

// Grooming operations
db.mementos.createIndex({ lastAccessedAt: 1 });
db.mementos.createIndex({ weight: 1 });

// Vector similarity (future)
db.mementos.createIndex({ embedding: "2dsphere" });

Optimized Database Queries

HOT Mementos Query (Production Critical)

// Optimized static method
MementoSchema.statics.findHotMementosByUserId = function(userId: string) {
  return this.find({ userId, tier: MementoTier.HOT })
    .sort({ weight: -1, lastAccessedAt: -1 }) // Highest priority first
    .lean(); // Skip Mongoose overhead for read-only operations
};

Grooming Queries

// WARM tier grooming (batch efficient)
const warmMementos = await Memento.find({
  userId,
  tier: MementoTier.WARM,
})
.sort({ weight: 1, lastAccessedAt: 1 })
.limit(targetCount)
.select('_id'); // Only fetch IDs for update

// Batch update (single query)
await Memento.updateMany(
  { _id: { $in: warmMementos.map(m => m._id) } },
  { $set: { tier: MementoTier.COLD } }
);

---

API Endpoints

Core CRUD Operations

POST /api/mementos/create

Purpose: Create single memento with memory enforcement

Critical Features:

• Pre-creation memory validation
• Emergency grooming triggers
• Synchronous memory enforcement
• Comprehensive logging

Request:

interface CreateMementoRequest {
  type: MementoType;
  tier: MementoTier;
  weight: number;        // 0-1000 (converted to 0-1)
  sessionId: string;     // Required
  summary: string;
  fullContent?: string;
  tags?: string[];
  metadata?: Record<string, any>;
  questId?: string;
  lastAccessedAt?: Date;
  isArchived?: boolean;
}

Response:

// Success (201)
IMementoDocument

// Memory limit exceeded (413)
{
  error: string;
  currentUsage: number;    // Percentage
  maxLimit: number;        // 100
  currentSize: number;     // Characters
  newMementoSize: number;  // Characters
  maxTotalChars: number;   // Limit
}

POST /api/mementos/create-batch

Purpose: Batch create mementos with collective memory validation

Key Differences from Single Create:

• Validates total batch size before creation
• All-or-nothing transaction semantics
• Collective memory impact calculation
• Batch-optimized grooming

GET /api/mementos/list

Purpose: Paginated memento listing with filtering

Query Parameters:

interface ListMementosParams {
  page?: number;           // Default: 1
  limit?: number;          // Default: 50, Max: 100
  tier?: MementoTier;      // Filter by tier
  type?: MementoType;      // Filter by type
  sessionId?: string;      // Filter by session
  search?: string;         // Full-text search
  tags?: string[];         // Tag filtering
  sortBy?: 'weight' | 'createdAt' | 'lastAccessedAt';
  sortOrder?: 'asc' | 'desc';
}

Response:

interface PaginatedMementos {
  mementos: IMementoDocument[];
  totalCount: number;
  page: number;
  limit: number;
  totalPages: number;
  hasNextPage: boolean;
  hasPrevPage: boolean;
  
  // Memory usage stats
  memoryUsage: {
    hotSize: number;
    hotCount: number;
    warmSize: number;
    warmCount: number;
    coldSize: number;
    coldCount: number;
    totalSize: number;
    usagePercent: number;
    maxTotalChars: number;
  };
}

PUT /api/mementos/:id

Purpose: Update memento with tier change validation

Special Handling:

• Tier changes trigger weight recalculation
• Memory impact validation for HOT promotions
• Access time updates
• Optimistic locking prevention

DELETE /api/mementos/:id

Purpose: Safe deletion with cleanup

Features:

• Soft delete option (isArchived: true)
• Hard delete with cascade cleanup
• Memory usage recalculation
• Audit trail logging

Internal Endpoints

POST /api/mementos/groom

Purpose: Manual grooming trigger (admin only)

Use Cases:

• Emergency manual intervention
• Maintenance operations
• Testing grooming logic

GET /api/mementos/stats

Purpose: Detailed memory usage statistics

Response:

interface MementoStats {
  userId: string;
  memoryUsage: {
    hotTier: { count: number; size: number; avgWeight: number; };
    warmTier: { count: number; size: number; avgWeight: number; };
    coldTier: { count: number; size: number; avgWeight: number; };
    total: { count: number; size: number; usagePercent: number; };
  };
  groomingHistory: {
    lastGroomAt?: Date;
    groomCount: number;
    avgGroomInterval: number; // minutes
  };
  performance: {
    avgContextLoadTime: number; // ms
    avgCreationTime: number;    // ms
  };
}

---

Production Issues & Fixes

Issue 1: Context Window Overflow (CRITICAL)

Problem: 188K+ token context causing model API failures

Error: input length and max_tokens exceed context limit: 188544 + 17000 > 200000

Root Cause: Loading ALL user mementos for context instead of HOT tier only

Fix: Multi-layer protection system

• ✅ HOT-only context loading (60-90% reduction)
• ✅ Emergency token limits (50K cap)
• ✅ Model-specific validation
• ✅ Context window safety checks

Impact: Zero context overflow errors since deployment

Issue 2: Memory Usage at 161% (CRITICAL)

Problem: Memory grooming not preventing unlimited growth

Root Cause: Asynchronous grooming allowed memory to exceed limits during high-frequency creation

Fix: Synchronous memory enforcement

• ✅ Pre-creation memory validation (95% threshold)
• ✅ Immediate synchronous grooming
• ✅ All-or-nothing memory semantics
• ✅ Comprehensive usage logging

Impact: Memory usage maintained below 95% threshold

Issue 3: Database Performance Degradation

Problem: Full table scans for memento queries

Root Cause: Missing compound indexes for common query patterns

Fix: Optimized database schema

• ✅ Added compound indexes
• ✅ Optimized HOT tier queries
• ✅ Lean queries for read operations
• ✅ Batch operations for grooming

Impact: 10x improvement in query performance

Issue 4: Cascading Failures

Problem: Memory overflow in one user affecting system stability

Root Cause: Lack of user isolation and circuit breakers

Fix: Isolation and resilience

• ✅ Per-user memory limits
• ✅ Graceful degradation
• ✅ Error isolation
• ✅ Automatic recovery

Impact: Single user issues no longer affect system

---

Monitoring & Alerting

Key Metrics

Memory Usage Metrics

// CloudWatch Custom Metrics
await cloudWatch.putMetricData({
  Namespace: 'Bike4Mind/Mementos',
  MetricData: [
    {
      MetricName: 'MemoryUsagePercent',
      Dimensions: [{ Name: 'UserId', Value: userId }],
      Value: usagePercent * 100,
      Unit: 'Percent'
    },
    {
      MetricName: 'HotTierSize',
      Dimensions: [{ Name: 'UserId', Value: userId }],
      Value: hotSize,
      Unit: 'Count'
    }
  ]
});

Performance Metrics

• Context load time (target: <100ms)
• Grooming execution time (target: <5s)
• Memory enforcement overhead (target: <50ms)
• API response times (target: <200ms)

Error Metrics

• Context window overflow count (target: 0)
• Memory limit violations (target: 0)
• Grooming failures (target: <1%)
• API error rates (target: <0.1%)

Alerting Rules

Critical Alerts (Page Immediately)

- name: context_window_overflow
  condition: count > 0
  message: "🚨 CRITICAL: Context window overflow detected"
  action: immediate_page

- name: memory_enforcement_failure
  condition: memory_usage > 100%
  message: "🚨 CRITICAL: Memory limit enforcement failed"
  action: immediate_page

- name: grooming_system_down
  condition: grooming_failures > 5
  message: "🚨 CRITICAL: Grooming system failure"
  action: immediate_page

Warning Alerts (Slack Notification)

- name: high_memory_usage
  condition: memory_usage > 85%
  message: "⚠️ WARNING: High memory usage detected"
  action: slack_alert

- name: slow_context_loading
  condition: context_load_time > 500ms
  message: "⚠️ WARNING: Slow context loading"
  action: slack_alert

Monitoring Dashboard

Essential Widgets:

1. Memory Usage Trends: Per-user memory consumption over time
2. Grooming Efficiency: Success rates and execution times
3. Context Window Safety: Token usage distribution
4. API Performance: Response times and error rates
5. Database Performance: Query execution times
6. Error Rates: Failure categorization and trends

---

Troubleshooting Guide

Common Issues

1. Memory Limit Exceeded (413 Error)

Symptoms:

HTTP 413: Memory limit exceeded. Current usage: 98.2% after grooming.

Diagnosis:

# Check current memory usage
curl -H "Authorization: Bearer $TOKEN" \
     "https://api.bike4mind.com/api/mementos/stats"

# Check grooming history
grep "grooming" /var/log/application.log | tail -20

Solutions:

1. Manual grooming: POST /api/mementos/groom
2. Increase memory limit: Update MEMORY_LIMITS.DEFAULT_MAX_TOTAL_CHARS
3. Bulk archive: Archive old COLD tier mementos
4. Check grooming logic: Verify thresholds are appropriate

2. Context Window Overflow

Symptoms:

Error: input length and max_tokens exceed context limit: 188544 + 17000 > 200000

Diagnosis:

// Check if HOT-only loading is working
const hotMementos = await db.mementos.findHotMementosByUserId(userId);
const totalTokens = calculateTotalTokens(hotMementos);
console.log(`HOT tier tokens: ${totalTokens}`);

Solutions:

1. Verify HOT-only loading: Ensure not loading ALL mementos
2. Check token limits: Verify 50K emergency cap is active
3. Reduce HOT tier: Lower HOT_TARGET threshold
4. Model compatibility: Check model context window size

3. Slow Performance

Symptoms:

• Context loading >500ms
• API responses >1s
• Database timeouts

Diagnosis:

// Check index usage
db.mementos.explain().find({ userId: "user123", tier: "hot" });

// Check query performance
console.time('context-load');
const context = await getContextMessages();
console.timeEnd('context-load');

Solutions:

1. Index optimization: Ensure compound indexes exist
2. Query tuning: Use .lean() for read-only operations
3. Caching: Implement Redis caching for frequent queries
4. Database scaling: Consider read replicas

4. Grooming Failures

Symptoms:

Error during grooming operation: MongoError: Connection timeout

Diagnosis:

# Check grooming logs
grep "grooming" /var/log/application.log | grep "ERROR"

# Check database connection
mongosh --eval "db.adminCommand('ping')"

Solutions:

1. Database connectivity: Check MongoDB cluster health
2. Timeout configuration: Increase query timeouts
3. Batch size optimization: Reduce grooming batch sizes
4. Retry logic: Implement exponential backoff

Debugging Tools

Memory Usage Calculator

export function debugMemoryUsage(userId: string) {
  const mementos = await Memento.findByUserId(userId);
  
  const stats = {
    hot: { count: 0, size: 0 },
    warm: { count: 0, size: 0 },
    cold: { count: 0, size: 0 }
  };
  
  for (const memento of mementos) {
    const size = calculateMementoSize(memento);
    stats[memento.tier].count++;
    stats[memento.tier].size += size;
  }
  
  console.table(stats);
  return stats;
}

Context Window Simulator

export function simulateContextWindow(userId: string, model: string) {
  const hotMementos = await findHotMementosByUserId(userId);
  const messages = assembleContextMessages(hotMementos);
  const totalTokens = calculateTotalTokens(messages);
  const modelInfo = getModelInfo(model);
  
  console.log({
    hotMementoCount: hotMementos.length,
    totalTokens,
    modelContextWindow: modelInfo.contextWindow,
    utilization: (totalTokens / modelInfo.contextWindow * 100).toFixed(1) + '%',
    safe: totalTokens < modelInfo.contextWindow * 0.8
  });
}

---

Best Practices

For Developers

1. Memory-Aware Development

// ✅ GOOD: Always check memory before creating mementos
const currentUsage = await calculateMemoryUsage(userId);
if (currentUsage > 0.9) {
  await forceImmediateGrooming(userId);
}

// ❌ BAD: Create without memory checks
await Memento.create(data); // Could cause overflow

2. Context-Safe Queries

// ✅ GOOD: HOT tier only for context
const contextMementos = await findHotMementosByUserId(userId);

// ❌ BAD: All mementos for context  
const allMementos = await findByUserId(userId); // Context overflow risk

3. Performance-Conscious Operations

// ✅ GOOD: Batch operations
await Memento.updateMany(
  { _id: { $in: ids } },
  { $set: { tier: MementoTier.COLD } }
);

// ❌ BAD: Individual updates
for (const id of ids) {
  await Memento.findByIdAndUpdate(id, { tier: MementoTier.COLD });
}

For Operations

1. Monitoring Setup

• Set up CloudWatch dashboards for all key metrics
• Configure alerts for critical thresholds
• Monitor database performance regularly
• Track user adoption and usage patterns

2. Capacity Planning

• Monitor memory growth trends
• Plan for user base expansion
• Consider database sharding at scale
• Implement auto-scaling for Lambda functions

3. Backup & Recovery

• Regular database backups
• Test grooming logic in staging
• Maintain rollback procedures
• Document emergency procedures

For Product

1. User Education

• Explain tier system to power users
• Provide memory usage visibility
• Guide optimal memento creation
• Educate on search and filtering

2. Feature Development

• Consider memory impact of new features
• Design with grooming in mind
• Implement usage analytics
• Plan for user feedback integration

3. Scaling Considerations

• Design for multi-tenant efficiency
• Consider per-user limit customization
• Plan for enterprise-scale deployments
• Implement usage-based pricing models

---

Future Enhancements

Short Term (Next Quarter)

1. Vector Similarity Search

• Implement semantic memento retrieval
• Use embeddings for context relevance
• Improve search beyond text matching
• Enable AI-powered memento clustering

2. Advanced Grooming

• Machine learning-based importance scoring
• User behavior pattern recognition
• Predictive grooming triggers
• Personalized memory management

3. Performance Optimizations

• Redis caching layer
• Database connection pooling
• Async processing pipeline
• CDN for static memento content

Medium Term (6 Months)

1. Multi-Modal Mementos

• Image and file attachments
• Audio transcription support
• Video content summarization
• Rich media memory search

2. Collaborative Memory

• Shared memento spaces
• Team memory pools
• Permission-based access
• Collaborative grooming

3. Advanced Analytics

• Memory usage prediction
• Content quality scoring
• User behavior insights
• Performance optimization recommendations

Long Term (1 Year+)

1. AI-Powered Memory Management

• Fully autonomous grooming
• Predictive context assembly
• Intelligent tier management
• Self-optimizing memory systems

2. Federation & Synchronization

• Cross-device memory sync
• Distributed memory architecture
• Edge computing integration
• Real-time collaboration

3. Enterprise Features

• Advanced compliance tools
• Audit and governance
• Bulk management operations
• Integration with enterprise systems

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Conclusion

The Mementos System represents a sophisticated approach to AI memory management that balances immediate performance needs with long-term scalability. Through the implementation of critical production fixes, comprehensive monitoring, and intelligent automation, the system provides reliable, high-performance memory services that scale with user needs.

The multi-layered protection against context window overflow, combined with proactive memory management and emergency enforcement mechanisms, ensures system stability even under extreme load conditions. The three-tier memory hierarchy provides an optimal balance between accessibility and efficiency, while the grooming system maintains performance without user intervention.

This architecture serves as a foundation for advanced AI memory capabilities while maintaining the reliability and performance standards required for production deployment. The comprehensive monitoring and troubleshooting capabilities ensure that issues can be quickly identified and resolved, while the modular design allows for future enhancements without compromising existing functionality.

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This documentation reflects the system state as of the critical production fixes implementation. For the latest updates and changes, refer to the system changelogs and monitoring dashboards.