- 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- Outdated debug solutions reference deleted files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