Describe your use case

Over time, memory databases can accumulate:

• Duplicate or highly similar memories
• Redundant information that can be consolidated
• Outdated memories that should be compressed or merged
• Low-importance memories taking up storage

Users need automatic optimization to:

• Reduce storage costs
• Improve search performance
• Maintain memory quality
• Consolidate related memories

Describe the solution you'd like

Implement an intelligent compression and deduplication system:

1. Deduplication:

   • Detect duplicate memories using semantic similarity (embedding-based)
   • Detect near-duplicates with configurable similarity threshold
   • Merge duplicates intelligently (keep most important, combine metadata)
   • Report deduplication results

2. Compression:

   • Identify similar memories that can be merged
   • Use LLM to summarize and consolidate related memories
   • Preserve key information while reducing redundancy
   • Maintain memory relationships and metadata

3. Optimization strategies:

   • Automatic: Run periodically based on configuration
   • Manual: memory.optimize(strategy="deduplicate") or memory.compress()
   • Selective: Optimize specific user/agent memories or date ranges

4. Configuration:

   • Similarity threshold for deduplication
   • Compression aggressiveness (conservative vs. aggressive)
   • Scheduling (daily, weekly, on-demand)
   • Dry-run mode to preview changes

5. API:

   # Deduplication
   results = memory.deduplicate(user_id="user123", threshold=0.95)
   # Returns: {"duplicates_found": 10, "merged": 10, "saved_space": "..."}
   
   # Compression
   results = memory.compress(user_id="user123", strategy="conservative")
   # Returns: {"compressed": 5, "original_count": 20, "compressed_count": 15}

The solution should:

• Be safe (backup before optimization, rollback on errors)
• Preserve important information
• Maintain memory relationships and graph connections
• Provide detailed reports of optimizations
• Support incremental optimization (process in batches)

Describe alternatives you've considered

No response

Additional context

No response