- 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:
- STOP — Do not start composing your response / 停止，不要开始回复
- WRITE — Update SESSION-STATE.md with the detail / 写入 SESSION-STATE.md
- 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:
- At 60% context: Clear old buffer, start fresh
- Every message after 60%: Append to
memory/working-buffer.md - After compaction: Read buffer FIRST, extract important context
- 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
- Write before respond — WAL is non-negotiable / 先写再回，WAL不可妥协
- Buffer the danger zone — >60% = automatic logging / 缓冲危险区，>60%自动记录
- Strict budgeting — 10/40/20/20, no exceptions / 严格预算，10/40/20/20无例外
- Auto-tiering — HOT→WARM→COLD without manual effort / 自动分层，无需手动
- Promote aggressively — Recurring patterns become permanent / 积极提升，重复模式永久化
- 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*