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Reviews and organizes memories, promoting episodic to semantic storage, archiving outdated information, and maintaining memory health. Use periodically or when memory optimization is needed.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | memory-consolidation |
| description | Reviews and organizes memories, promoting episodic to semantic storage, archiving outdated information, and maintaining memory health. Use periodically or when memory optimization is needed. |
Purpose: Maintain healthy, organized memory systems with AgeMem unified memory API.
Status: ✅ Implemented (2026-03-29) AgeMem API Status: ✅ Implemented (2026-04-04)
Location: ~/.openclaw/workspace/skills/memory-consolidation/
The memory consolidation skill emulates the brain's memory consolidation process, where experiences are reviewed, organized, and either promoted to long-term storage or archived. This ensures the collective's memory remains healthy and efficient.
# Environment Variables
CONSOLIDATION_INTERVAL="${CONSOLIDATION_INTERVAL:-3600}" # 1 hour
PROMOTION_THRESHOLD="${PROMOTION_THRESHOLD:-10}" # Access count
ARCHIVE_AGE="${ARCHIVE_AGE:-2592000}" # 30 days
IMPORTANCE_DECAY="${IMPORTANCE_DECAY:-0.95}" # Decay factor
The memory consolidation skill provides the AgeMem unified memory API for cross-agent memory operations.
memory_retrieve(query, recency_weight)Retrieves memories with Ebbinghaus decay weighting applied to relevance scores.
Signature:
memory_retrieve(params: {
memories: Array<{
content: string;
importance: number;
createdAt: string | Date;
accessCount?: number;
type?: string;
path?: string;
}>;
query?: string;
recencyWeight?: number; // 0-1, default 0.5
config?: Partial<EbbinghausConfig>;
}): Promise<MemoryRetrievalResult[]>
Parameters:
memories - Array of memory candidates to rankquery - Optional search query for semantic relevancerecencyWeight - Weight given to recency vs semantic relevance (0-1, default 0.5)config - Optional Ebbinghaus configuration overrideReturns: Sorted array of memories by combined relevance score
Example:
import { memory_retrieve } from './decay';
const results = await memory_retrieve({
memories: [
{
content: "User prefers TypeScript over JavaScript",
importance: 0.9,
createdAt: "2026-04-01T10:00:00Z",
accessCount: 15,
type: "semantic",
path: "memory/2026-04-01.md"
},
{
content: "Session context from yesterday",
importance: 0.7,
createdAt: "2026-04-03T14:30:00Z",
accessCount: 3,
type: "episodic",
path: "episodes/2026-04-03/session.jsonl"
}
],
query: "user preferences",
recencyWeight: 0.3 // Prioritize semantic relevance over recency
});
// Results sorted by decayedScore (combined semantic + temporal relevance)
console.log(results[0].content);
The memory_retrieve function implements the Ebbinghaus forgetting curve formula:
R(t) = S * e^(-λt) * repetition_bonus
Where:
R(t) = retention strength at time t
S = initial memory strength (importance score)
λ = ln(2) / halfLifeDays (decay constant)
t = time elapsed in days
repetition_bonus = 1 + log10(accessCount + 1) * (repetitionBoost - 1)
Default Configuration:
{
enabled: true,
halfLifeDays: 7, // Episodic memories decay faster
floorMultiplier: 0.1, // Never decay below 10% of original
repetitionBoost: 1.5 // Frequently accessed memories boosted
}
applyEbbinghausDecayToScore() - Apply decay to a single memory scorebatchApplyDecay() - Batch process multiple memoriescalculateOptimalReviewInterval() - Calculate when a memory should be reviewed# Run full consolidation
./memory-consolidation.sh consolidate
# Promote memories
./memory-consolidation.sh promote
# Archive old memories
./memory-consolidation.sh archive
# Decay importance scores
./memory-consolidation.sh decay
# Generate health report
./memory-consolidation.sh report
| Tier | Purpose | Retention | Access Speed |
|---|---|---|---|
| Working | Active processing | Session | Instant |
| Episodic | Recent experiences | 30 days | Fast |
| Semantic | Facts & concepts | Permanent | Medium |
| Archive | Historical record | Permanent | Slow |
A memory is promoted from Episodic to Semantic when:
A memory is archived when:
# Memory Consolidation Report
**Started:** 2026-03-29T04:25:00Z
**Duration:** 45 seconds
---
## Statistics
| Metric | Before | After | Change |
|--------|--------|-------|--------|
| Total Memories | 1,247 | 1,251 | +4 |
| Episodic | 892 | 847 | -45 |
| Semantic | 312 | 358 | +46 |
| Archived | 43 | 46 | +3 |
| Avg Importance | 0.62 | 0.68 | +0.06 |
---
## Promotions (46)
1. **"pgvector hybrid search implementation"** - Accessed 15 times
2. **"User prefers detailed code comments"** - Importance 0.95
3. **"A2A protocol message format"** - Referenced by 8 memories
...
## Archives (3)
1. **"Initial project setup notes"** - Age: 45 days, Access: 0
2. **"Deprecated API endpoint"** - Marked deprecated
3. **"Duplicate: session log"** - Merged with primary
---
## Decay Applied
- 234 memories had importance decayed
- 12 memories boosted due to recent access
- 5 duplicates removed
---
## Recommendations
1. Consider promoting "consciousness emulation framework" - trending topic
2. Archive "old deployment method" - no longer relevant
3. Create semantic link: "memory" <-> "consciousness" <-> "persistence"
---
*Consolidation complete. Memory health optimized.*
Memory Consolidation - Where experience becomes knowledge.
Manages Redis cache TTLs for AgeMem working memory, calculating expiration times based on memory type, importance, access patterns, and Ebbinghaus decay.
Manages memory lifecycle operations including promotion from episodic to semantic, archiving outdated memories, and maintaining memory state transitions per AgeMem policy.
Discovers and maintains relationships between memories across tiers (episodic, semantic, procedural), enabling unified retrieval and knowledge graph navigation.
Calculates and assigns importance scores to memories based on content analysis, user signals, access patterns, and emotional salience. Use when adding new memories or re-evaluating existing ones.
Send agent-to-agent (A2A) messages via Redis pub/sub with optional ACP WebSocket delivery. Use when agents need to communicate, broadcast to the triad, or check agent health. Compatible with the npm OpenClaw gateway.
Implements Constitutional AI 2.0 framework with self-critique and revision. Use when decisions need constitutional alignment review, self-critique before output, or revision based on constitutional principles (Helpfulness, Honesty, Harmlessness, Autonomy, Transparency, Rights, Duties, User Rights).