🧠 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
INLINECODE0	Search memories by natural language (semantic)
INLINECODE1

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

1. 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

CODEBLOCK0

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

CODEBLOCK1

Option	Default	Description
INLINECODE8	INLINECODE9	Inject relevant memories before each turn
INLINECODE10

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:
CODEBLOCK2

Default: INLINECODE40

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. 1. Let it run for a few days — memory improves over time
2. Manually store important facts with INLINECODE41
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