openclaw-local-memory（OpenClaw 本地记忆）

🧠 Local Memory Plugin v0.4

A brain-like memory system for OpenClaw. Remembers what matters, forgets what doesn't, and builds a persistent understanding of you over time.

Zero-config, no external service, no API key, works out of the box.

Features

🧠 Brain-Like Memory Architecture

• Hierarchical Memory: Exchanges → Summaries → Profile
• Importance Scoring: Each memory scored 0-1 based on significance
• Time Decay: Importance decreases over time (adjustable rate)
• Entity Tracking: Extracts and tracks people, places, things
• Semantic Chunking: Long content auto-split into manageable pieces

🔍 Smart Recall

• Multi-Factor Scoring: Combines relevance, importance, AND recency
• Profile Injection: Builds and injects user profile periodically
• Context Window: Tracks conversation turns and manages memory refresh

💾 Intelligent Capture

• Significance Detection: Only captures meaningful content
• Auto-Deduplication: Won't store the same thing twice
• Periodic Consolidation: Summarizes accumulated content when context grows long
• Category Detection: Auto-categorizes as preference, fact, decision, entity, skill

🗑️ Self-Maintaining

• Auto-Pruning: Removes old/unimportant memories when limit reached
• Importance Protection: High-value memories kept longer
• Memory Stats: Track memory health and composition

Tools

How It Works

Memory Lifecycle

1. Capture → User + Assistant exchange
2. Significance Assessment → Score based on patterns (decisions score high, greetings low)
3. Storage → If significant enough, store with extracted entities and tags
4. Importance Calculation → Based on category, length, entities, source
5. Decay Over Time → Importance decreases exponentially
6. Recall → On query, combine TF-IDF relevance + importance + recency
7. Pruning → When max reached, lowest combined-score memories removed

Recall Scoring Formula

score = (relevanceWeight × tfidf_similarity) 
      + (importanceWeight × decayed_importance)
      + (recencyWeight × recency_factor)

Significance Detection Patterns

Configuration

{
  "autoRecall": true,
  "autoCapture": true,
  "captureInterval": 8,
  "captureSignificantOnly": true,
  "minSignificanceScore": 0.5,
  "profileFrequency": 15,
  "includeProfileOnFirstTurn": true,
  "maxRecallResults": 5,
  "similarityThreshold": 0.35,
  "maxMemoryInjections": 3,
  "contextBudget": 2000,
  "maxMemories": 500,
  "pruneOlderThanDays": 30,
  "decayRate": 0.05,
  "chunkSize": 800,
  "importanceWeight": 0.25,
  "recencyWeight": 0.25,
  "relevanceWeight": 0.5
}

Data Storage

All memories stored locally in:

~/.openclaw/memory/<containerTag>.json

Default: ~/.openclaw/memory/openclaw_local_memory.json

Privacy

• 100% Local: No data leaves your machine
• You Control: Auto-capture can be disabled
• Significance Filter: Won't store every random message
• No External APIs: No internet required

Requirements

• OpenClaw 2026.1.29 or later
• Node.js (built-in TF-IDF, no external dependencies)

Tips

For Best Results

1. Let it run for a few days — memory improves over time
2. Manually store important facts with local_memory_store
3. Check profile with local_memory_profile periodically
4. Adjust importanceWeight, recencyWeight, relevanceWeight to your preference

If Context Gets Long

• Reduce summariseThreshold to trigger earlier consolidation
• Increase decayRate to forget older stuff faster
• Lower maxMemories to prune more aggressively

Forgot Something?

• Use local_memory_forget query="what to forget" to delete
• Use local_memory_search to find what you're looking for