Feature: Memory summarization and compression for long-term retention

[cdzzy]

Problem

Agents accumulate thousands of memories over time, but retrieval degrades and storage costs grow. Important memories get lost in noise.

Proposed Solution: Intelligent Memory Compression

const memory = new Engram({
  compression: {
    enabled: true,
    strategy: "importance-based",
    schedule: "daily",
    maxMemoryAge: "30d"  // Compress memories older than 30 days
  }
})

// Before compression
// [100 detailed messages about project discussion]

// After compression
// "Discussed Project X timeline, key decisions: deadline extended to March, need UX review"

Compression Strategies

Strategy	Description	Use Case
importance-based	Keep high-importance, summarize rest	General use
temporal	Merge consecutive similar memories	Event streams
topic-based	Cluster and summarize by topic	Research agents
llm-summarize	Use LLM for semantic compression	High-fidelity

API

// Manual compression trigger
await memory.compress({ strategy: "importance-based" })

// Get compression statistics
const stats = await memory.getCompressionStats()
console.log(stats)
// {
//   originalCount: 5000,
//   compressedCount: 800,
//   spaceSaved: "75%",
//   summaryQuality: 0.92
// }

Searchable Compressed Memories

// Compressed summaries are still searchable
const results = await memory.recall("project timeline decisions")
// Returns: compressed summary + link to original if needed

This solves the memory bloat problem for persistent agents.