Memory Optimization - Hybrid Storage & Semantic Compaction

⚙️ Memory Optimization (Agent-Brain Focus)

Goal: Expand agent-brain with hybrid storage and efficient memory management.

Hybrid Storage Architecture

Storage Tiers

1. Qdrant - Hot vectors

   • Real-time retrieval
   • Active agent context
   • Recent interactions

2. PgVector - Persistent context

   • Long-term storage
   • Historical data
   • Relational context

3. LanceDB - Local caches

   • Zero-dependency
   • Offline capabilities
   • Fast local access

Semantic Compaction

Build compaction jobs in workflow-engine:

• Weekly embedding pruning
• Remove redundant vectors
• Consolidate similar embeddings
• Maintain semantic coverage

Trace Indexing

Index traces from agent-tracer:

• Store as "contextual experience"
• Searchable reasoning patterns
• Successful strategy recall
• Error pattern detection

Memory Management Features

• Automatic tiering (hot → warm → cold)
• Intelligent eviction policies
• Cross-tier search
• Memory budget enforcement
• Usage analytics

Expected Benefits

• Dynamic memory efficiency
• Reduced storage costs
• Faster retrieval times
• Better context relevance

Implementation Tasks

• Design storage tier architecture
• Implement data migration between tiers
• Build compaction jobs in workflow-engine
• Create trace indexing pipeline
• Add memory analytics dashboard
• Optimize query performance

Priority

High - Core memory efficiency

Phase

Phase 3 - Memory optimization