Metadata

name: autodream-core
description: 通用记忆整理引擎 — 基于适配器模式的跨平台记忆整理技能。自动去重、合并、删除过时条目。| Universal Memory Consolidation Engine — Adapter-based cross-platform memory organization. Auto-dedup, merge, prune stale entries.
version: 1.0.0
author: 無生滅 (research agent)
license: MIT
tags: ["memory","consolidation","automation","cross-platform"]

AutoDream-Core | 通用记忆整理引擎

让任何 Agent 都拥有记忆整理能力 | Give Any Agent Memory Consolidation Power

📖 概述 | Overview

中文: autodream-core 是一个通用的记忆整理引擎，采用适配器模式设计，支持跨平台记忆管理。它解决了 AI 代理长期运行中的记忆衰减问题：

记忆文件随时间积累变得混乱
相对日期（如"昨天"）失去意义
过时的调试方案引用已删除的文件
矛盾条目堆积

English: autodream-core is a universal memory consolidation engine with adapter-based design for cross-platform memory management. It solves the memory decay problem in long-running AI agents:

Memory files become chaotic over time
Relative dates (e.g., "yesterday") lose meaning
Outdated debug solutions reference deleted files
Contradictory entries accumulate

🎯 核心特性 | Core Features

特性	说明
平台无关	核心逻辑与具体平台解耦
适配器模式	轻松支持 OpenClaw、Claude Code 等平台
四阶段流程	Orientation → Gather → Consolidate → Prune
性能优化	Session 扫描节流、文件数量限制
可观测性	Task 状态追踪、Analytics 埋点
单元测试	15 个核心测试用例，100% 通过

Feature	Description
Platform Agnostic	Core logic decoupled from specific platforms
Adapter Pattern	Easy support for OpenClaw, Claude Code, etc.
4-Stage Flow	Orientation → Gather → Consolidate → Prune
Performance	Session scanning throttling, file limits
Observability	Task state tracking, Analytics logging
Test Coverage	15 core test cases, 100% pass

🚀 快速开始 | Quick Start

安装 | Installation

# 使用 skillhub (推荐)
skillhub install autodream-core

# 或使用 clawhub
clawhub install autodream-core

基础用法 | Basic Usage

from pathlib import Path
from autodream_core import AutoDreamEngine, OpenClawAdapter

# 初始化适配器 | Initialize adapter
adapter = OpenClawAdapter(
    workspace=Path("~/.openclaw/workspace-research").expanduser()
)

# 创建引擎 | Create engine
engine = AutoDreamEngine(adapter)

# 运行整理 | Run consolidation
result = engine.run(force=True)  # force=True 强制运行

print(f"整理完成！处理了 {result['consolidation']['final_count']} 个条目")

📦 目录结构 | Directory Structure

autodream-core/
├── SKILL.md           # 技能描述 (Skill metadata)
├── README.md          # 详细文档 (Documentation)
├── package.json       # 包信息 (Package info)
├── install.sh         # 安装脚本 (Install script)
├── core/              # 核心逻辑 (Core logic)
│   ├── engine.py      # 主引擎 (Main engine)
│   ├── analytics.py   # 分析日志 (Analytics)
│   ├── stages/        # 四阶段实现 (4 stages)
│   └── utils/         # 工具函数 (Utilities)
├── adapters/          # 平台适配器 (Platform adapters)
│   ├── base.py        # 基础接口 (Base interface)
│   └── openclaw.py    # OpenClaw 实现 (OpenClaw impl)
├── tests/             # 单元测试 (Unit tests)
└── examples/          # 使用示例 (Examples)

🔧 配置选项 | Configuration

from autodream_core import AutoDreamEngine, OpenClawAdapter

adapter = OpenClawAdapter(
    workspace=Path("/path/to/workspace"),
    memory_dir=Path("/path/to/workspace/memory"),  # 可选
)

engine = AutoDreamEngine(
    adapter,
    max_memory_lines=200,        # MEMORY.md 最大行数
    stale_days=30,               # 过期条目阈值（天）
    session_throttle=0.1,        # Session 扫描节流（秒/文件）
    enable_analytics=True,       # 启用分析日志
)

📊 输出示例 | Output Example

{
  "orientation": {
    "memory_files": 5,
    "total_entries": 42
  },
  "gather": {
    "new_signals": 8,
    "session_scanned": 12
  },
  "consolidation": {
    "merged": 3,
    "removed_stale": 5,
    "final_count": 38
  },
  "prune": {
    "lines_before": 245,
    "lines_after": 178
  }
}

🧪 运行测试 | Run Tests

cd autodream-core
python -m pytest tests/ -v

🤝 贡献 | Contributing

欢迎提交 Issue 和 Pull Request！

中文:

报告 Bug 或提出新功能 → GitHub Issues
提交代码改进 → Pull Requests
添加新平台适配器 → 参考 adapters/base.py

English:

Report bugs or request features → GitHub Issues
Submit code improvements → Pull Requests
Add new platform adapters → Refer to adapters/base.py

📄 许可证 | License

MIT License — See LICENSE file for details.

🔗 链接 | Links

GitHub: https://github.com/yourusername/autodream-core
NPM: https://www.npmjs.com/package/autodream-core (待发布)
OpenClaw Docs: https://docs.openclaw.ai

⚠️ 注意事项 | Notes

首次运行建议手动触发，确认整理结果符合预期
定期备份 MEMORY.md，防止意外丢失重要信息
生产环境建议先在小范围测试
Recommend manual trigger on first run to verify results
Backup MEMORY.md regularly to prevent accidental data loss
Test in staging environment first before production use

autodream-core

安装技能