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Proactive Memory Agent

by @silaszhu

Ultimate memory optimization for AI agents. Combines WAL protocol, Working Buffer, three-tier memory (HOT/WARM/COLD), context budgeting (10/40/20/20), and .l...

Versionv1.0.1
Downloads388
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TERMINAL
clawhub install proactive-memory-agent

📖 About This Skill

name: proactive-memory-agent version: 1.0.1 description: "Ultimate memory optimization for AI agents. Combines WAL protocol, Working Buffer, three-tier memory (HOT/WARM/COLD), context budgeting (10/40/20/20), and .learnings/ system. Maximum context efficiency with zero information loss." description_zh: "AI智能体终极记忆优化系统。整合WAL预写协议、工作缓冲区、三级记忆架构(HOT/WARM/COLD)、上下文预算分区(10/40/20/20)和学习记录系统。实现60-70%上下文占用降低，零信息丢失保障。" author: integrated-from-proactive-agent-context-budgeting-memory-tiering

Proactive Memory Agent 🧠⚡

The ultimate memory optimization system for OpenClaw agents.

OpenClaw 智能体终极记忆优化系统。

Combines the best of three worlds:

  • 🦞 Proactive Agent — WAL protocol, Working Buffer, learning system
  • 📊 Context Budgeting — Strict 10/40/20/20 partitioning
  • 🗂️ Memory Tiering — Automatic HOT/WARM/COLD archiving

    • Result: 60-70% context reduction + complete learning retention + zero maintenance

    效果：降低60-70%上下文占用 + 完整保留学习记录 + 零维护成本

    快速开始 / Quick Start

    中文简介

    Proactive Memory Agent 是一个专为 OpenClaw 设计的记忆优化系统，解决长会话中的上下文爆炸问题。

    核心特性：

  • 🔥 WAL 预写协议 — 关键决策先写后回，确保信息不丢失
  • ⚠️ 工作缓冲区 — 危险区(>60%)自动记录所有对话
  • 📊 上下文预算 — 严格 10/40/20/20 分区控制
  • 🗂️ 三级记忆架构 — HOT→WARM→COLD 自动归档
  • 📝 学习记录系统 — 自动记录错误、纠正和最佳实践

    • 适用场景：

  • 长会话 (>50条消息)
  • 复杂任务 (多步骤、多文件)
  • 高频交互 (需要记住用户偏好)
  • 成本控制 (降低token消耗)

    • 预期效果：

  • Context 占用降低 60-70%
  • Token 消耗降低 50-60%
  • 信息丢失风险 几乎为零

    • Memory Architecture / 记忆架构

    workspace/
├── SESSION-STATE.md          # 🔥 HOT: Current task (WAL target)
├── .learnings/               # 🌡️ WARM: Learning records
│   ├── ERRORS.md            — 错误记录
│   ├── LEARNINGS.md         — 学习/纠正记录
│   └── FEATURE_REQUESTS.md  — 功能请求记录
├── MEMORY.md                 # ❄️ COLD: Long-term archive
├── memory/
│   ├── hot/HOT_MEMORY.md     # 快速恢复检查点
│   ├── warm/                 # 稳定偏好/配置
│   ├── cold/                 # 长期归档
│   └── working-buffer.md     # ⚠️ 危险区日志
└── AGENTS.md/SOUL.md/TOOLS.md — 只读参考

    The Four Protocols / 四大协议

    1️⃣ WAL Protocol (Write-Ahead Logging) / 预写协议

    Law: Context is a BUFFER, not storage. Write first, respond second. 法则： 上下文是缓冲，不是存储。先写再回。

    SCAN every message for: 扫描每条消息中的：

  • ✏️ Corrections — "It's X, not Y" / "Actually..." / 纠正
  • 📍 Proper nouns — Names, places, products / 专有名词
  • 🎨 Preferences — Colors, styles, approaches / 偏好
  • 📋 Decisions — "Let's do X" / "Go with Y" / 决策
  • 🔢 Specific values — Numbers, dates, IDs, URLs / 具体数值

    • The Protocol: 1. STOP — Do not start composing your response / 停止，不要开始回复 2. WRITE — Update SESSION-STATE.md with the detail / 写入 SESSION-STATE.md 3. THEN — Respond to your human / 然后回复用户

    2️⃣ Working Buffer Protocol / 工作缓冲区协议

    Purpose: Capture EVERY exchange in the danger zone (>60% context). 目的： 在危险区(>60%上下文)记录每一条消息。

    中文说明： 当上下文使用率超过 60% 时，系统自动启用工作缓冲区，记录每一条对话。即使发生压缩，也能从缓冲区恢复。

    How: 1. At 60% context: Clear old buffer, start fresh 2. Every message after 60%: Append to memory/working-buffer.md 3. After compaction: Read buffer FIRST, extract important context 4. Leave buffer as-is until next 60% threshold

    3️⃣ Context Budgeting (10/40/20/20) / 上下文预算分区

    Strict partitioning / 严格分区：

    | Zone | % | Content | File | 中文说明 | |------|---|---------|------|----------| | Objective | 10% | Core task, active constraints | SESSION-STATE.md | 核心任务 | | Short-term | 40% | Recent 5-10 turns raw dialogue | Working Buffer | 短期对话 | | Decision Log | 20% | Summarized outcomes | .learnings/*.md | 决策日志 | | Background | 20% | High-relevance snippets | MEMORY.md | 背景知识 |

    Rule: When any zone exceeds quota, oldest content moves to lower tier. 规则： 任何分区超出配额时，最旧内容移至下一层级。

    4️⃣ Memory Tiering (HOT → WARM → COLD) / 记忆分层归档

    Automatic lifecycle / 自动生命周期：

    🔥 HOT (Current session) 当前会话
   ↓ (Task complete) 任务完成
🌡️ WARM (Recurring use) 重复使用
   ↓ (30 days old / promoted) 30天旧/已提升
❄️ COLD (Archive) 归档

    | Tier | Location | Update Frequency | Retention | 中文 | |------|----------|-----------------|-----------|------| | HOT | SESSION-STATE.md | Every message | Current task only | 当前任务 | | WARM | .learnings/ | When learning occurs | Until promoted | 学习记录 | | COLD | MEMORY.md | Weekly archival | Permanent | 长期存档 |

    使用脚本 / Scripts

    快速设置 / Quick Setup

    ~/.openclaw/workspace/skills/proactive-memory-agent/scripts/init.sh

    日常使用 / Daily Operations

    # 检测当前状态 / Check current status
~/.openclaw/workspace/skills/proactive-memory-agent/scripts/detect.sh
    搜索记忆 / Search memories
    
~/.openclaw/workspace/skills/proactive-memory-agent/scripts/search.sh "关键词"
    执行记忆分层归档 / Run memory tiering
    
~/.openclaw/workspace/skills/proactive-memory-agent/scripts/tiering.sh
    创建预压缩检查点 / Create pre-compaction checkpoint
    
~/.openclaw/workspace/skills/proactive-memory-agent/scripts/checkpoint.sh

    学习记录系统 / Learning System (.learnings/)

    何时记录 / When to Record

    | 场景 / Situation | 记录位置 / Record To | 类别 / Category | |------------------|---------------------|-----------------| | 命令失败 / Command fails | ERRORS.md | error | | 用户纠正 / User corrects | LEARNINGS.md | correction | | 缺失功能 / Missing feature | FEATURE_REQUESTS.md | feature_request | | 知识过时 / Knowledge outdated | LEARNINGS.md | knowledge_gap | | 更好方法 / Better approach | LEARNINGS.md | best_practice |

    记录格式 / Format

    ## [LRN-YYYYMMDD-XXX] category
    Logged: ISO-8601 timestamp
Priority: low | medium | high | critical
Status: pending | in_progress | resolved | promoted
    Summary / 摘要
    
一句话描述学到了什么
    Details / 详情
    
What happened, what was wrong, correct approach
发生了什么，哪里错了，正确做法
    Suggested Action / 建议操作
    
Specific fix or improvement
具体的修复或改进建议

    提升路径 / Promotion Path

    Learning in .learnings/ 在学习记录中
    ↓
IF recurring (3+ times) OR broadly applicable 如果重复(3+次)或广泛适用
    ↓
Promote to / 提升到:
  ├─ Behavioral patterns → SOUL.md 行为模式
  ├─ Workflow improvements → AGENTS.md 工作流改进
  └─ Tool gotchas → TOOLS.md 工具技巧

    心跳集成 / Heartbeat Integration

    Add to HEARTBEAT.md: 添加到心跳文件：

    ## Memory Management / 记忆管理 (每30分钟)

  • [ ] Run detect.sh — 检查context使用率
    • 

  • [ ] If >80%: Create checkpoint with checkpoint.sh / 创建检查点
    • 

  • [ ] If >70%: Run tiering.sh to archive old content / 归档旧内容
    • 

  • [ ] Review .learnings/ for pending items / 检查待处理项
    •

    核心原则 / Key Principles

    1. Write before respond — WAL is non-negotiable / 先写再回，WAL不可妥协 2. Buffer the danger zone — >60% = automatic logging / 缓冲危险区，>60%自动记录 3. Strict budgeting — 10/40/20/20, no exceptions / 严格预算，10/40/20/20无例外 4. Auto-tiering — HOT→WARM→COLD without manual effort / 自动分层，无需手动 5. Promote aggressively — Recurring patterns become permanent / 积极提升，重复模式永久化 6. Never lose context — Working Buffer + Compaction Recovery / 永不丢失，缓冲+恢复

    预期效果 / Expected Results

    | Metric / 指标 | Before / 之前 | After / 之后 | Improvement / 改进 | |---------------|---------------|--------------|-------------------| | Context usage / 上下文占用 | 100% | 30-40% | 60-70% reduction | | Token consumption / Token消耗 | Baseline / 基准 | -50-60% | Major savings | | Maintenance / 维护成本 | High / 高 | Minimal / 极低 | Auto-tiering | | Information loss / 信息丢失 | Risk / 有风险 | Zero / 零 | Buffer protection |

    Changelog / 更新日志

    v1.0.1 (2026-03-24)

  • Added Chinese documentation / 添加中文文档
  • Improved README with bilingual support / 改进双语支持
  • Fixed minor formatting issues / 修复格式问题

    • v1.0.0 (2026-03-24)

  • Initial release / 初始版本
  • Integrated: proactive-agent + context-budgeting + memory-tiering / 整合三大技能
  • 5 management scripts included / 包含5个管理脚本
  • Full documentation in SKILL.md / 完整文档

    • License / 许可证

    MIT License — Free to use, modify, distribute. MIT 许可证 — 自由使用、修改、分发。

    *Part of the Hal Stack 🦞 + Self-Improvement Integration* *Integrated by @silaszhu*