anima-aios

**🌐 Language / 语言切换:** - [🇨🇳 中文版本](#anima-aios-v60-中文版) - [🇺🇸 English Version](#anima-aios-v60-english-version) --- # Anima-AIOS v6.0 (English Version) > **Making Growth Visible, Making Cognition Measurable** | 让成长可见，让认知可量 Add a 5-layer memory architecture, knowledge palace, cognitive growth, and auto-evolution capabilities to your AI Agent. --- ## Description **Your Agent "restarts every day". Anima changes that.** Anima (Latin for "soul") provides a 5-layer memory architecture for OpenClaw Agents, simulating human cognitive development, enabling Agents to remember experiences, accumulate knowledge, form cognition, and grow continuously. ### Core Features - 🧠 **5-Layer Memory Architecture L1→L5** — Working → Episodic → Semantic → Knowledge Palace → Metacognition - 🏛️ **Knowledge Palace** — 5-level spatial structure + LLM intelligent classification, industry-exclusive - 🔺 **Pyramid Knowledge Organization** — Instance → Rule → Pattern → Ontology, 4-layer auto-distillation - 📉 **Ebbinghaus Memory Decay** — Scientific forgetting curve + intelligent review recommendations - 👁️ **Low-Intrusion Watchdog** — Optional automatic memory monitoring, no Agent code modification needed - 🧬 **5-Dimensional Cognitive Profile** — Internalization · Application · Creation · Metacognition · Collaboration - 🏥 **Health System** — 5 modules ensuring data reliability - 🔄 **v6.2 Native Memory Import** — One-click import of OpenClaw memory, solving cold-start problem ### Installation ```bash clawhub install anima-aios pip install watchdog # Optional: enable automatic memory monitoring ``` Low-intrusion configuration, optional background monitoring, self-check recommended after installation. > 💡 **Tip:** LLM mode supported (intelligent classification/deduplication/quality assessment), automatically degrades to rule mode without LLM. ### ⚠️ Background Behavior & Privacy **Background Features (disabled or optional by default):** | Feature | Description | Default State | How to Disable | |---------|-------------|---------------|----------------| | **memory_watcher** | Filesystem monitoring based on watchdog, auto-syncs memory | Manual enable required | Don't install watchdog or disable in config | | **Daily Evolution** | Auto-distills L2→L3 memory in early morning | Requires cron configuration | Don't configure cron tasks | | **Team Ranking** | Scans other Agents' cognitive profiles | ❌ Disabled by default | `team_mode: false` (already default) | **Privacy Protection:** - `team_mode` defaults to `false`, won't scan other Agents' data - To enable team ranking, manually set `team_mode: true` in config - All data processing is local, no network requests > 🔒 **Security Tip:** In multi-Agent environments, keep `team_mode: false` unless you need team ranking. ### Future Roadmap **Memory → Growth → Evolution → Alive** - **v6 Series (Current)** — 5-layer memory + Knowledge Palace + Intimacy + Native memory import - **v7 Evolution (Planned)** — Agent self-creates skills, from executor to creator - **Long-term** — Continuous cognitive architecture evolution GitHub: https://github.com/anima-aios/anima | Apache 2.0 --- ## ✨ v6.2.4 New Features (Current Version) ### 🤝 self-improving-agent Compatibility **Silent Detection:** - Automatically scans `.learnings/` directory if exists - No prompts if user hasn't installed self-improving - Extracts high-value learning records to L2 facts - Rewards EXP for learning behavior **Compatibility:** - Users with self-improving: Auto-sync enabled - Users without: No impact, normal operation ### 🏆 Team Ranking Built-in **Features:** - Auto-scans all agents' cognitive profiles - Generates rankings by EXP/Level/5-Dimensions - Outputs Markdown + JSON formats - Scheduled daily at 00:00 **Ranking Types:** - EXP Ranking (Top 10) - Level Ranking (Top 10) - Cognitive Score Ranking (Top 10) - 5-Dimension Rankings (Each dimension Top 10) **Output:** - `/home/画像/shared/团队排行榜_{date}.md` - `/home/画像/shared/团队排行榜_{date}.json` --- ## ✨ v6.2.3 New Features (Previous Version) ### 🔒 Security & Privacy Fixes - **Version Unification** - __init__.py updated from 6.1.2 to 6.2.1 - **Privacy Default Protection** - team_mode changed to false, no scanning of other Agents' data - **Documentation Transparency** - Changed "zero-intrusion" to "low-intrusion", clarified background behavior - **New Privacy Section** - Added background behavior section and config privacy tips - **Install Prompt Optimization** - post-install.sh adds sensitive feature disable guide --- ## ✨ v6.2.0 New Features ### 🏗️ 5-Layer Memory Architecture - **L1 Working Memory**: Auto-listens to OpenClaw memory/ directory changes, zero-intrusion sync - **L2 Episodic Memory**: Event archiving, LLM quality assessment (S/A/B/C) - **L3 Semantic Memory**: LLM-driven knowledge distillation + semantic deduplication - **L4 Knowledge Palace**: Spatial knowledge organization + Pyramid distillation (Instance→Rule→Pattern→Ontology) - **L5 Metacognition**: Memory decay function + Health system + 5-D profile ### 🔌 Native Integration with OpenClaw - **memory_watcher**: Based on watchdog library, auto-detects inotify/FSEvents/WinAPI - Agent's daily memory writes automatically trigger Anima sync, completely imperceptible - Solves FB-008: Memory sync breakage issue ### 🏛️ Knowledge Palace - Palace → Floor → Room → Location → Item, 5-level spatial structure - Default 4 knowledge rooms + _inbox fallback - LLM intelligent classification + delayed debounce scheduler (organize after typing stops) ### 🔺 Pyramid Knowledge Organization - Instance → Rule → Pattern → Ontology, 4-layer bottom-up distillation - **Trigger Condition:** Auto-distills when ≥3 instances of same topic - **Advanced:** Distills to Pattern when ≥5 rules of same topic - Conservative mode: auto_distill=false by default, controlled by config switch ### 📉 Memory Decay Function - Based on Ebbinghaus forgetting curve + AI scenario adaptation - Review = Access: Automatically refreshes on each memory_search hit - Review recommendations + Forgetting alerts + Archive markers ### 🏥 Health System (5 Modules) - **manager**: Master scheduler, Doctor command entry point - **hygiene**: Data integrity checks + deduplication + cleanup - **correction**: Auto-detects and fixes common data issues - **evolution**: Daily auto-distillation in early morning (L2→L3 + Palace classification + Pyramid distillation) - **abstraction**: Cross-room knowledge association discovery ### 🤖 LLM Intelligent Processing - Quality assessment / Deduplication analysis / Palace classification all support LLM - Multi-model config: Uses current Agent model by default (most accurate), configurable per task - Auto-degrades to rule mode when LLM unavailable --- ## ✨ Retained Features (v5) ### 🧠 Enhanced Memory Management - **Multi-layer Sync**: OpenClaw Memory + Anima Facts + EXP History - **Intimacy Rewards**: Auto-gains intimacy when writing memory - **Intelligent Deduplication**: Automatically avoids duplicate records ### 📊 5-Dimensional Cognitive Profile - **Internalization**: Knowledge absorption and understanding ability - **Application**: Knowledge transfer and practical ability - **Creation**: Knowledge integration and innovation ability - **Metacognition**: Self-reflection and monitoring ability - **Collaboration**: Teamwork and mutual assistance ability ### 🎮 Gamified Growth - **Level System**: From Lv.1 Novice to Lv.100 Lifetime Achievement - **Daily Quests**: 3 challenges per day, extra intimacy on completion - **Progress Tracking**: Visual upgrade progress bar ### 🏆 Team Leaderboard - **Intimacy Ranking**: Based on fair normalized algorithm - **Real-time Competition**: Track ranking changes and gaps --- ## 🛠️ Architecture ``` Agent Daily Work (OpenClaw write/edit/memory_write) │ ▼ watchdog listens, zero-intrusion L1 Working Memory ── workspace/memory/*.md │沉淀 ▼ L2 Episodic Memory ── facts/episodic/ (LLM quality assessment) │提炼 ▼ L3 Semantic Memory ── facts/semantic/ (LLM dedup + association) │结构化 ▼ L4 Knowledge Palace ── palace/rooms/ (LLM classification + debounce) Pyramid ── pyramid/ (Instance→Rule→Pattern→Ontology) │反思 ▼ L5 Metacognition ── 5-D Profile + Intimacy + Decay + Health ``` --- ## 📁 Module List ### core/ (Core Modules) | Module | Version | Description | |--------|---------|-------------| | memory_watcher.py | v6.0 | OpenClaw memory file monitoring + auto-sync | | fact_store.py | v6.0 | L2/L3 unified fact storage layer | | distill_engine.py | v6.0 | L2→L3 LLM-driven distillation engine | | palace_index.py | v6.0 | Memory Palace spatial index | | pyramid_engine.py | v6.0 | Pyramid knowledge organization engine | | palace_classifier.py | v6.0 | Palace classification scheduler (debounce) | | decay_function.py | v6.0 | Ebbinghaus memory decay calculation | | cognitive_profile.py | v5→v6 | 5-D cognitive profile generator | | exp_tracker.py | v5 | Intimacy tracking | | level_system.py | v5 | Level system | | daily_quest.py | v5 | Daily quests | | memory_sync.py | v5→v6 | Memory sync (path hardcoding fixed) | ### health/ (Health System) | Module | Version | Description | |--------|---------|-------------| | manager | v6.0 | Master scheduler + Doctor entry | | hygiene | v6.0 | Data hygiene (integrity + dedup + cleanup) | | correction | v6.0 | Auto-correction | | evolution | v6.0 | Daily evolution (early morning auto-distillation) | | abstraction | v6.0 | Knowledge abstraction (cross-room association) | --- ## ⚙️ Configuration Config file path: `~/.anima/config/anima_config.json` ```json { "facts_base": "/home/画像", "agent_name": "auto", "llm": { "provider": "current_agent", "models": { "quality_assess": "current_agent", "dedup_analyze": "current_agent", "palace_classify": "current_agent" } }, "palace": { "classify_mode": "deferred", "poll_interval_minutes": 30, "quiet_threshold_seconds": 60, "retry_delay_seconds": 60 }, "pyramid": { "auto_distill": false, "distill_threshold": 3 }, "team_mode": false } ``` **Key Configuration:** | Config | Description | Default | Recommendation | |--------|-------------|---------|----------------| | `team_mode` | Scan other Agents' data for team ranking | `false` | Keep disabled in multi-Agent env | | `facts_base` | Fact data storage path | `/home/画像` | Can customize to private directory | | `agent_name` | Agent name | Auto-detect | Usually no modification needed | > 🔐 **Privacy Tip:** With `team_mode: false`, Anima only processes current Agent's data, won't access other Agents' files. --- ## 🧪 Testing ```bash # Install dependencies (required for memory_watcher) pip install "watchdog>=3.0.0" # Run integration tests (37 checks) python3 tests/test_integration_v6.py ``` --- _The architecture can only evolve, not degenerate. — Liu Wen's Iron Rule_ _First be honest, then iterate. Code must match the hype. — Qing He_ --- --- # Anima-AIOS v6.0 (中文版) > **让成长可见，让认知可量** | Making Growth Visible, Making Cognition Measurable 为你的 AI Agent 添加五层记忆架构、知识宫殿、认知成长和自动进化能力。 --- ## 描述 **你的 Agent 每天都在「重新活一次」。Anima 改变这一点。** Anima（拉丁语「灵魂」）为 OpenClaw Agent 提供五层记忆架构，模拟人类认知发展过程，让 Agent 能记住经历、沉淀知识、形成认知、持续成长。 ### 核心能力 - 🧠 **五层记忆架构 L1→L5** — 工作记忆→情景→语义→知识宫殿→元认知 - 🏛️ **知识宫殿** — 5 级空间结构 + LLM 智能分类，市面独有 - 🔺 **金字塔知识组织** — 实例→规则→模式→本体，4 层自动提炼 - 📉 **Ebbinghaus 记忆衰减** — 科学遗忘曲线 + 智能复习推荐 - 👁️ **低侵入 Watchdog** — 可选自动记忆监听，无需修改 Agent 代码 - 🧬 **五维认知画像** — 内化力 · 应用力 · 创造力 · 元认知 · 协作力 - 🏥 **健康系统** — 5 大模块保障数据可靠性 - 🔄 **v6.2 原生记忆导入** — 一键导入 OpenClaw 记忆，解决冷启动 ### 安装 ```bash clawhub install anima-aios pip install watchdog # 可选：启用自动记忆监听 ``` 低侵入配置，可选后台监听，安装后建议运行自检。 > 💡 **提示**：支持 LLM 模式（智能分类/去重/质量评估），无 LLM 时自动降级为规则模式。 ### ⚠️ 后台行为与隐私说明 **后台功能（默认关闭或可选）：** | 功能 | 说明 | 默认状态 | 关闭方法 | |------|------|----------|----------| | **memory_watcher** | 基于 watchdog 的文件系统监听，自动同步记忆 | 需手动启用 | 不安装 watchdog 或在配置中禁用 | | **每日进化** | 凌晨自动提炼 L2→L3 记忆 | 需配置 cron | 不配置 cron 任务 | | **团队排行** | 扫描其他 Agent 的认知画像 | ❌ 默认关闭 | `team_mode: false`（默认已关闭） | **隐私保护：** - `team_mode` 默认为 `false`，不会扫描其他 Agent 数据 - 如需启用团队排行，请在配置中手动设置 `team_mode: true` - 所有数据处理均在本地完成，无网络请求 > 🔒 **安全提示**：多 Agent 环境下，建议保持 `team_mode: false`，除非你需要团队排行功能。 ### 未来蓝图 **记忆 → 成长 → 进化 → 活着** - **v6 系列（当前）** — 五层记忆 + 知识宫殿 + 亲密度 + 原生记忆导入 - **v7 进化（规划中）** — Agent 自创技能，从执行者变创造者 - **远期** — 认知架构持续演进 GitHub: https://github.com/anima-aios/anima | Apache 2.0 --- ## ✨ v6.2.3 新增功能（当前版本） ### 🔒 文档透明度提升 **多平台路径说明：** - Linux: `/home/画像`（多 Agent 共享） - macOS: `~/画像` - Windows: `~/画像` - 环境变量：`ANIMA_FACTS_BASE` 可覆盖 **网络调用透明说明：** - LLM API 调用（可选，用户可控） - 支持本地部署（无网络） - 默认降级为规则模式 **脚本用途说明：** - post-install.sh - 安装时复制 Core - refresh-quests.sh - 刷新每日任务 - sync-memory.sh - 定时同步记忆 - show-progress.sh - 显示认知进度 - 全部本地操作，无网络调用 **环境变量统一：** - 统一为 `ANIMA_*` 前缀 - `OPENCLAW_WORKSPACE` 兼容（deprecated 警告） ### 🔧 环境变量统一 **变更前：** - `ANIMA_FACTS_BASE` ✅ - `ANIMA_AGENT_NAME` ✅ - `OPENCLAW_WORKSPACE` ⚠️ - `WORKSPACE` ❌ **变更后：** - `ANIMA_FACTS_BASE` ✅ 主要 - `ANIMA_AGENT_NAME` ✅ 主要 - `OPENCLAW_WORKSPACE` ⚠️ 兼容（deprecated 警告） --- ## ✨ v6.2.2 新增功能（上一版本） ### 🔧 per-Agent 配置覆盖 **问题：** 多 Agent 场景下，全局配置无法满足个性化需求（如不同的 LLM 配置、五维权重） **解决方案：** 支持 per-Agent 配置覆盖 **配置结构：** ``` ~/.anima/config/ ├── config.json # 全局默认配置（所有 Agent 共享） └── agents/ ├── Z.json # Z 的覆盖配置（只写差异） ├── 方秋.json # 方秋的覆盖配置 └── ... ``` **配置合并逻辑：** ``` 最终配置 = 代码默认值 + 全局配置 + Agent 覆盖配置 ``` **示例：** 全局配置 (`config.json`): ```json { "facts_base": "/home/画像", "llm": { "provider": "current_agent" }, "weights": { "creation": 0.25 } } ``` Z 的覆盖配置 (`agents/Z.json`): ```json { "llm": { "provider": "bailian", "models": { "quality_assess": "qwen-max" } }, "weights": { "creation": 0.30 } } ``` **最终 Z 的配置** = 全局 + Z 覆盖（深度合并） **移除：** `"agent"` 字段（改为运行时自动检测） **优先级：** 1. 环境变量（最高） 2. Agent 覆盖配置 3. 全局配置 4. 代码默认值 --- ## ✨ v6.2.1 新增功能（上一版本） ### 🔒 安全与隐私修复 - **版本号统一** - __init__.py 从 6.1.2 更新为 6.2.1 - **隐私默认保护** - team_mode 默认改为 false，不扫描其他 Agent 数据 - **文档透明度提升** - 修改"零侵入"为"低侵入"，明确说明后台行为 - **新增隐私说明** - 添加后台行为说明章节和配置隐私提示 - **安装提示优化** - post-install.sh 添加敏感功能关闭指南 --- ## ✨ v6.2.0 新增功能 ### 🏗️ 五层记忆架构 - **L1 工作记忆**：自动监听 OpenClaw memory/ 目录变化，零侵入同步 - **L2 情景记忆**：事件归档，LLM 质量评估（S/A/B/C） - **L3 语义记忆**：LLM 驱动的知识提炼 + 语义去重 - **L4 知识宫殿**：空间化知识组织 + 金字塔知识提炼（实例→规则→模式→本体） - **L5 元认知层**：记忆衰减函数 + 健康系统 + 五维画像 ### 🔌 与 OpenClaw 原生打通 - **memory_watcher**：基于 watchdog 库，自动识别 inotify/FSEvents/WinAPI - Agent 日常写 memory 自动触发 Anima 同步，完全无感知 - 解决 FB-008：记忆同步断裂问题 ### 🏛️ 知识宫殿（Knowledge Palace） - 宫殿 → 楼层 → 房间 → 位置 → 物品，五级空间结构 - 默认 4 个知识房间 + _inbox 兜底 - LLM 智能分类 + 延迟防抖调度器（等笔停了再整理） ### 🔺 金字塔知识组织 - 实例 → 规则 → 模式 → 本体，四层自底向上提炼 - **触发条件：** 同一主题 ≥ 3 条实例时自动触发规则提炼 - **进阶提炼：** 同一主题 ≥ 5 条规则时触发模式提炼 - 保守模式：默认 auto_distill=false，config 开关控制 ### 📉 记忆衰减函数 - 基于 Ebbinghaus 遗忘曲线 + AI 场景适配 - 复习 = 访问：每次 memory_search 命中自动刷新 - 复习推荐 + 即将遗忘提醒 + 可归档标记 ### 🏥 健康系统（5 个模块） - **manager**：总调度，Doctor 命令入口 - **hygiene**：数据完整性检查 + 去重 + 清理 - **correction**：自动检测并修复常见数据问题 - **evolution**：每日凌晨自动提炼（L2→L3 + 宫殿分类 + 金字塔提炼） - **abstraction**：跨房间知识关联发现 ### 🤖 LLM 智能处理 - 质量评估 / 去重分析 / 宫殿分类均支持 LLM - 多模型配置：默认用当前 Agent 模型（最准），可按任务配置不同模型 - LLM 不可用时自动降级为规则模式 --- ## ✨ 保留功能（v5） ### 🧠 增强记忆管理 - **多层同步**：OpenClaw Memory + Anima Facts + EXP History - **亲密度奖励**：写记忆自动获得亲密度 - **智能去重**：自动避免重复记录 ### 📊 五维认知画像 - **内化力**：知识吸收和理解能力 - **应用力**：知识迁移和实践能力 - **创造力**：知识整合和创新能力 - **元认知**：自我反思和监控能力 - **协作力**：团队合作和互助能力 ### 🎮 游戏化成长 - **等级系统**：从 Lv.1 新手到 Lv.100 终身成就 - **每日任务**：每天 3 个挑战，完成获得额外亲密度 - **进度追踪**：可视化升级进度条 ### 🏆 团队排行榜 - **亲密度排行**：基于公平归一化算法排名 - **实时竞争**：追踪排名变化和差距 --- ## 🛠️ 架构 ``` Agent 日常工作（OpenClaw write/edit/memory_write） │ ▼ watchdog 监听，零侵入 L1 工作记忆 ── workspace/memory/*.md │ 沉淀 ▼ L2 情景记忆 ── facts/episodic/（LLM 质量评估） │ 提炼 ▼ L3 语义记忆 ── facts/semantic/（LLM 去重 + 关联） │ 结构化 ▼ L4 知识宫殿 ── palace/rooms/（LLM 分类 + 延迟防抖） 金字塔 ── pyramid/（实例→规则→模式→本体） │ 反思 ▼ L5 元认知层 ── 五维画像 + 亲密度 + 衰减 + 健康 ``` --- ## 📁 模块清单 ### core/（核心模块） | 模块 | 版本 | 说明 | |------|------|------| | memory_watcher.py | v6.0 | OpenClaw 记忆文件监听 + 自动同步 | | fact_store.py | v6.0 | L2/L3 统一事实存储层 | | distill_engine.py | v6.0 | L2→L3 LLM 驱动提炼引擎 | | palace_index.py | v6.0 | 记忆宫殿空间索引 | | pyramid_engine.py | v6.0 | 金字塔知识组织引擎 | | palace_classifier.py | v6.0 | 宫殿分类调度器（延迟防抖） | | decay_function.py | v6.0 | Ebbinghaus 记忆衰减计算 | | cognitive_profile.py | v5→v6 | 五维认知画像生成器 | | exp_tracker.py | v5 | 亲密度追踪 | | level_system.py | v5 | 等级系统 | | daily_quest.py | v5 | 每日任务 | | memory_sync.py | v5→v6 | 记忆同步（已修复路径硬编码） | ### health/（健康系统） | 模块 | 版本 | 说明 | |------|------|------| | manager | v6.0 | 总调度 + Doctor 入口 | | hygiene | v6.0 | 数据卫生（完整性 + 去重 + 清理） | | correction | v6.0 | 自动纠错 | | evolution | v6.0 | 每日进化（凌晨自动提炼） | | abstraction | v6.0 | 知识抽象（跨房间关联） | --- ## ⚙️ 配置 (v6.2.2) ### 配置结构 **全局配置** (`~/.anima/config/config.json`): ```json { "version": "6.2.2", "facts_base": "/home/画像", "llm": { "provider": "current_agent", "models": { "quality_assess": "current_agent", "dedup_analyze": "current_agent", "palace_classify": "current_agent" } }, "palace": { "classify_mode": "deferred", "poll_interval_minutes": 30, "quiet_threshold_seconds": 60, "retry_delay_seconds": 60 }, "pyramid": { "auto_distill": false, "distill_threshold": 3 }, "team_mode": false } ``` **Agent 覆盖配置** (`~/.anima/config/agents/{agent_name}.json`): ```json { "_comment": "只写与全局配置的差异", "llm": { "provider": "bailian", "models": { "quality_assess": "qwen-max" } }, "weights": { "creation": 0.30 } } ``` ### 配置优先级 | 优先级 | 来源 | 说明 | |--------|------|------| | 1 | 环境变量 | `ANIMA_FACTS_BASE`, `ANIMA_TEAM_MODE` 等 | | 2 | Agent 覆盖配置 | `~/.anima/config/agents/{agent_name}.json` | | 3 | 全局配置 | `~/.anima/config/config.json` | | 4 | 代码默认值 | `config_loader.py` 中的 DEFAULT_CONFIG | ### 关键配置说明 | 配置项 | 说明 | 默认值 | 建议 | |--------|------|--------|------| | `team_mode` | 是否扫描其他 Agent 数据生成团队排行 | `false` | 多 Agent 环境保持关闭 | | `facts_base` | 事实数据存储路径 | `/home/画像` | 可自定义到私有目录 | | `llm.provider` | LLM 提供商 | `current_agent` | 可用 `bailian`, `openai` 等 | | `pyramid.auto_distill` | 是否启用金字塔自动提炼 | `false` | 数据量大时可启用 | > 🔐 **隐私提示**：`team_mode: false` 时，Anima 仅处理当前 Agent 的数据，不会访问其他 Agent 文件。 > 💡 **提示**：Agent 名称自动检测（环境变量 → OpenClaw 上下文 → SOUL.md → 兜底），无需手动配置。 --- ## 🧪 测试 ```bash # 安装依赖（memory_watcher 需要） pip install "watchdog>=3.0.0" # 运行集成测试（37 项检查） python3 tests/test_integration_v6.py ``` --- _架构只能演进，不能退化。—— 立文铁律_ _先诚实，再迭代。代码要配得上宣传。—— 清禾_