Memory Management
in Production
Agent Systems

The architecture mistake costing you $800/month
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The Common Mistake

Most teams:

"Store everything in a vector database"

Every message → Embed → Store
28/30
production systems reviewed
had this architecture
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Three Problems

❌ Problem 1: Expensive
Embedding costs on EVERY message
Even "hi", "thanks", order numbers
$2,400/mo in embeddings
❌ Problem 2: Slow
100-200ms latency per retrieval
For simple conversation history
100-200ms per query
❌ Problem 3: Imprecise
Semantic search for exact data
Overkill for structured lookups
Wrong tool for the job
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Two Types of Memory

🔄 Short-Term
• Current conversation
• Last 5-10 messages
• Session state
• Exact retrieval
• Accessed frequently
• Temporary (1 hour)
💾 Long-Term
• Cross-session patterns
• Conversation summaries
• User preferences
• Semantic search
• Accessed occasionally
• Persistent (90 days)
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Use the Right Tool

✅ Short-term → Redis / Key-Value
Conversation history, session state, caching
Exact lookup by key
Cost: $50/mo
Latency: 1-2ms
Exact retrieval
✅ Long-term → Vector DB
Summaries (not full transcripts)
Cross-session patterns, semantic search
Cost: $270/mo
Latency: 50-100ms
Semantic search
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Complete Architecture

Message Arrives
Store in Redis (1-2ms, 1hr TTL)
↓
Retrieve Short-term Context
Exact lookup from Redis
↓
Agent Processes
With short-term + long-term context
↓
Conversation Ends
Generate summary (200 tokens)
↓
Relevance Check
Should we keep this?
↓
Embed Summary
Store in Vector DB (not full transcript)
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4 Pruning Strategies

Strategy 1
Time-Based Expiration
Short-term: 1 hour TTL (auto-delete)
Long-term: 90 days (archived)
Strategy 2
Relevance Filtering
Only store meaningful conversations
Skip greetings, errors, low-value chats
Result: -60% storage
Strategy 3
Deduplication
Check similarity before storing
If >0.95 similar → Update existing
Result: -30% duplicates
Strategy 4
Access-Based Pruning
Delete memories not accessed in 60 days
Track usage in metadata
Result: -40% unused data
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Cost Comparison

10,000 users, 10 requests/day each
❌ All Vector DB
Embeddings: $2,400/mo
Storage: $200/mo
Retrieval: $3,000/mo
$5,600/mo
✅ Hybrid Approach
Redis: $50/mo
Embeddings: $240/mo
Vector storage: $30/mo
$320/mo
94%
Cost Reduction
Same Functionality
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Real Production Example

Customer support chatbot
Monthly Cost
$520
↓ 88%
Retrieval Latency
45ms
↓ 75%
Memory Size
3GB
↓ 94%
Quality After Optimization
Same
A/B tested • User satisfaction unchanged
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Key Takeaways

1. Different Memory, Different Storage
Short-term → Key-value store
Long-term → Vector DB (summaries only)
2. Embed What Needs Semantic Search
Not every message
Not conversation history
Only patterns and summaries
3. Prune Aggressively
Time-based • Relevance • Deduplication • Access
Automated nightly cleanup
4. Measure Everything
Cost per user • Retrieval latency • Storage growth
A/B test quality impact
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