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tiered-memory

EvoClaw Tiered Memory Architecture v2.2.0 - LLM-powered three-tier memory system with automatic daily note ingestion, structured metadata extraction, URL preservation, validation, and cloud-first sync.

bowen313370 installations selon la source☆ 0 Étoiles du dépôtv2.2.0

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install bowen31337/tiered-memory

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

Tiered Memory System v2.2.0

A mind that remembers everything is as useless as one that remembers nothing. The art is knowing what to keep. 🧠

EvoClaw-compatible three-tier memory system inspired by human cognition and PageIndex tree retrieval.

What's New in v2.2.0

🔄 Automatic Daily Note Ingestion

  • Consolidation (daily/monthly/full modes) now auto-runs ingest-daily
  • Bridges memory/YYYY-MM-DD.md files → tiered memory system
  • No more manual ingestion required — facts flow automatically
  • Fixes the "two disconnected data paths" problem

What's New in v2.1.0

🎯 Structured Metadata Extraction

  • Automatic extraction of URLs, shell commands, and file paths from facts
  • Preserved during distillation and consolidation
  • Searchable by URL fragment

✅ Memory Completeness Validation

  • Check daily notes for missing URLs, commands, and next steps
  • Proactive warnings for incomplete information
  • Actionable suggestions for improvement

🔍 Enhanced Search

  • Search facts by URL fragment
  • Get all stored URLs from warm memory
  • Metadata-aware fact storage

🛡️ URL Preservation

  • URLs explicitly preserved during LLM distillation
  • Fallback metadata extraction if LLM misses them
  • Command-line support for adding metadata manually

Architecture

┌─────────────────────────────────────────────────────┐
│              AGENT CONTEXT (~8-15KB)                │
│                                                     │
│  ┌──────────┐  ┌────────────────────────────────┐  │
│  │  Tree    │  │  Retrieved Memory Nodes         │  │
│  │  Index   │  │  (on-demand, 1-3KB)            │  │
│  │  (~2KB)  │  │                                │  │
│  │          │  │  Fetched per conversation      │  │
│  │  Always  │  │  based on tree reasoning       │  │
│  │  loaded  │  │                                │  │
│  └────┬─────┘  └────────────────────────────────┘  │
│       │                                             │
└───────┼────────────────────────...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/bowen31337/tiered-memory
Version
2.2.0
Commit
a6b0cbc5d4a431499d0941ea0c6509625355bec6
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

README

Tiered Memory v2.0

A mind that remembers everything is as useless as one that remembers nothing.
The art is knowing what to keep.
🧠

Three-tier memory system for OpenClaw agents implementing the EvoClaw Tiered Memory Architecture. Inspired by human cognition and PageIndex tree-based retrieval.

Version: 2.0.0
License: MIT
Python: 3.8+ (zero external dependencies)


What's New in v2.0

🆕 LLM-Powered Tree Search — Reasoning-based retrieval instead of keyword matching
🆕 Distillation Engine — 3-stage compression (500B → 80B → 20B)
🆕 Hot Memory Structure — Identity, owner profile, active context, lessons (auto-pruning)
🆕 Score-Based Tiers — >=0.7 Hot, >=0.3 Warm, >=0.05 Cold, <0.05 Frozen
🆕 Multi-Agent Support — Agent ID scoping for all operations
🆕 Consolidation Modes — Quick/daily/monthly/full with tree pruning
🆕 Critical Sync — Cloud-first hot+tree sync after every conversation
🆕 Metrics & Observability — Comprehensive memory system metrics


Architecture

┌─────────────────────────────────────────────────────┐
│                 AGENT CONTEXT                        │
│                                                     │
│  ┌──────────────┐  ┌──────────────────────────┐    │
│  │ Memory Tree  │  │  Retrieved Memory Nodes  │    │
│  │ Index (~2KB) │  │  (on-demand, ~1-3KB)     │    │
│  │              │  │                          │    │
│  │ Always in    │  │  Fetched per conversation│    │
│  │ context      │  │  based on tree reasoning │    │
│  └──────┬───────┘  └──────────────────────────┘    │
│         │                                           │
└─────────┼───────────────────────────────────────────┘
          │
          │ Tree Search (LLM reasoning)
          │
┌─────────┼───────────────────────────────────────────┐
│         ▼              MEMORY TIERS                  │
│                                                     │
│  ┌─────────────┐  ┌─────────...

Contenu extrait

SKILL.md
# Tiered Memory System v2.2.0

> *A mind that remembers everything is as useless as one that remembers nothing. The art is knowing what to keep.* 🧠

EvoClaw-compatible three-tier memory system inspired by human cognition and PageIndex tree retrieval.

## What's New in v2.2.0

🔄 **Automatic Daily Note Ingestion**
- Consolidation (`daily`/`monthly`/`full` modes) now auto-runs `ingest-daily`
- Bridges `memory/YYYY-MM-DD.md` files → tiered memory system
- No more manual ingestion required — facts flow automatically
- Fixes the "two disconnected data paths" problem

## What's New in v2.1.0

🎯 **Structured Metadata Extraction**
- Automatic extraction of URLs, shell commands, and file paths from facts
- Preserved during distillation and consolidation
- Searchable by URL fragment

✅ **Memory Completeness Validation**
- Check daily notes for missing URLs, commands, and next steps
- Proactive warnings for incomplete information
- Actionable suggestions for improvement

🔍 **Enhanced Search**
- Search facts by URL fragment
- Get all stored URLs from warm memory
- Metadata-aware fact storage

🛡️ **URL Preservation**
- URLs explicitly preserved during LLM distillation
- Fallback metadata extraction if LLM misses them
- Command-line support for adding metadata manually

## Architecture

```
┌─────────────────────────────────────────────────────┐
│              AGENT CONTEXT (~8-15KB)                │
│                                                     │
│  ┌──────────┐  ┌────────────────────────────────┐  │
│  │  Tree    │  │  Retrieved Memory Nodes         │  │
│  │  Index   │  │  (on-demand, 1-3KB)            │  │
│  │  (~2KB)  │  │                                │  │
│  │          │  │  Fetched per conversation      │  │
│  │  Always  │  │  based on tree reasoning       │  │
│  │  loaded  │  │                                │  │
│  └────┬─────┘  └────────────────────────────────┘  │
│       │                                             │
└───────┼────────────────────────...
README
# Tiered Memory v2.0

> *A mind that remembers everything is as useless as one that remembers nothing.  
> The art is knowing what to keep.* 🧠

Three-tier memory system for OpenClaw agents implementing the EvoClaw Tiered Memory Architecture. Inspired by human cognition and PageIndex tree-based retrieval.

**Version:** 2.0.0  
**License:** MIT  
**Python:** 3.8+ (zero external dependencies)

---

## What's New in v2.0

🆕 **LLM-Powered Tree Search** — Reasoning-based retrieval instead of keyword matching  
🆕 **Distillation Engine** — 3-stage compression (500B → 80B → 20B)  
🆕 **Hot Memory Structure** — Identity, owner profile, active context, lessons (auto-pruning)  
🆕 **Score-Based Tiers** — >=0.7 Hot, >=0.3 Warm, >=0.05 Cold, <0.05 Frozen  
🆕 **Multi-Agent Support** — Agent ID scoping for all operations  
🆕 **Consolidation Modes** — Quick/daily/monthly/full with tree pruning  
🆕 **Critical Sync** — Cloud-first hot+tree sync after every conversation  
🆕 **Metrics & Observability** — Comprehensive memory system metrics  

---

## Architecture

```
┌─────────────────────────────────────────────────────┐
│                 AGENT CONTEXT                        │
│                                                     │
│  ┌──────────────┐  ┌──────────────────────────┐    │
│  │ Memory Tree  │  │  Retrieved Memory Nodes  │    │
│  │ Index (~2KB) │  │  (on-demand, ~1-3KB)     │    │
│  │              │  │                          │    │
│  │ Always in    │  │  Fetched per conversation│    │
│  │ context      │  │  based on tree reasoning │    │
│  └──────┬───────┘  └──────────────────────────┘    │
│         │                                           │
└─────────┼───────────────────────────────────────────┘
          │
          │ Tree Search (LLM reasoning)
          │
┌─────────┼───────────────────────────────────────────┐
│         ▼              MEMORY TIERS                  │
│                                                     │
│  ┌─────────────┐  ┌─────────...

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