Openclaw Local Memory

Brain-like local memory plugin for OpenClaw — stores, searches, and injects memories with importance scoring, entity extraction, and automatic consolidation.

FlexRox@flexrox

Install

openclaw skills install @flexrox/openclaw-local-memory

🧠 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

Tool	Description
`local_memory_search`	Search memories by natural language (semantic)
`local_memory_store`	Manually save a specific memory
`local_memory_list`	List all memories, optionally filtered by category
`local_memory_profile`	View user profile (entities, preferences, facts)
`local_memory_stats`	View memory statistics
`local_memory_recent`	Get recently accessed memories
`local_memory_forget`	Delete memory matching a query
`local_memory_wipe`	Delete ALL memories (irreversible)

How It Works

Memory Lifecycle

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

Recall Scoring Formula

text

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

Significance Detection Patterns

Pattern	Category	Weight
entschieden, geplant, wird, werden	decision	0.30
ich bin, mein, unser Unternehmen	identity	0.25
bevorzug, immer, nie, prefer	preference	0.25
api_key, password, token	credential	0.20
skill, können, fähig	skill	0.20
projekt, build, deploy	project	0.15

Configuration

json

{
  "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
}

Option	Default	Description
`autoRecall`	`true`	Inject relevant memories before each turn
`autoCapture`	`true`	Auto-capture conversation exchanges
`captureInterval`	`8`	Capture every N turns (higher = less storage)
`captureSignificantOnly`	`true`	Only capture significant content
`minSignificanceScore`	`0.5`	Min score to capture (higher = stricter)
`profileFrequency`	`15`	Inject profile every N turns (higher = less context)
`maxRecallResults`	`5`	Max memories injected per turn
`similarityThreshold`	`0.35`	Min relevance to inject
`maxMemoryInjections`	`3`	Max memories to show per recall
`contextBudget`	`2000`	Max chars of memory context injected
`maxMemories`	`500`	Maximum memories to keep
`pruneOlderThanDays`	`30`	Auto-delete memories older than N days
`decayRate`	`0.05`	Importance decay speed
`importanceWeight`	`0.25`	Weight of importance in scoring
`recencyWeight`	`0.25`	Weight of recency in scoring
`relevanceWeight`	`0.5`	Weight of TF-IDF relevance in scoring

Data Storage

All memories stored locally in: