Overview
- Skill Key
- bowen31337/tiered-memory
- Author
- bowen31337
- Source Repo
- openclaw/skills
- Version
- 2.2.0
- Source Path
- skills/bowen31337/tiered-memory
- Latest Commit SHA
- a6b0cbc5d4a431499d0941ea0c6509625355bec6
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.
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直接复制以下提示词,发送给你的 AI 助手即可完成安装。
请先检查是否已安装 SkillHub 商店,若未安装,请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店,然后安装 tiered-memory 技能。 若已安装,则直接安装 tiered-memory 技能。
# 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 │ │ │ │ │ └────┬─────┘ └────────────────────────────────┘ │ │ │ │ └───────┼────────────────────────...
# 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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