Skill: victorqr/anamnesis-hub (version 1.13.2)

--- name: anamnesis-hub description: "四层记忆架构 (Four-tier memory architecture for OpenClaw AI agents). 提供 L0 运行时语义检索 (Ollama bge-m3 + SQLite-vec 向量库)、L1 工作记忆 (每日 Markdown 日志)、L2 长期记忆 (MEMORY.md 索引 + ARCHIVE.md 档案 + facts.db 结构化知识图谱)、Dreaming 自动化提炼管线、三方同步 (Cloud ↔ Markdown ↔ Vector)、Active Memory 主动召回、auto-memory v3 两阶段提取 (ARCHIVE.md 归档 → MEMORY.md 摘要)、cross-platform-writer 跨平台写入。适用于首次配置持久化记忆、安装 memory-core/MemOS Cloud 插件、搭建多层记忆系统。" version: 1.13.2 --- # anamnesis-hub Four-tier memory architecture with automated Dreaming pipeline, three-way synchronization, Active Memory recall, and two-stage auto-extraction. ## Overview | Tier | Layer | Technology | Purpose | |------|-------|-----------|---------| | **L0** | Runtime Retrieval | memory-core plugin (Ollama bge-m3 → SQLite + sqlite-vec) | Real-time semantic + BM25 hybrid search | | **L0** | Cloud Recall | MemOS Cloud plugin (*optional*) | Cross-device memory capture and recall | | **L0** | Active Memory | Built-in OpenClaw plugin | Pre-reply sub-agent memory search + context injection | | **L1** | Working Memory | `memory/YYYY-MM-DD.md` files | Daily summaries, todos, technical notes | | **L2a** | Long-term Index | `MEMORY.md` (~90 lines, read-only base) | Bidirectional index → ARCHIVE.md, 0% Auto-Extracted pollution | | **L2b** | Detailed Archive | `ARCHIVE.md` (~220 lines) | Full records with ← MEMORY.md:XX reverse references | | **L2c** | Structured KB | `facts.sqlite` (entity/key/value) | Precise lookup for IPs, ports, versions; activation/decay tracking | ### Automated Pipelines | Pipeline | Schedule | Description | |----------|----------|-------------| | **Dreaming** | 03:00 UTC daily | Scan logs → DeepSeek analysis → promote to L2 | | **Three-way Sync** | 18:00 / 20:00 / 22:00 CST | Cloud ↔ Markdown ↔ Vector alignment | | **auto-memory v3** | 18:30 / 22:30 CST | MemOS Cloud facts → qwen3 filter → Stage1: ARCHIVE.md (SHA-256 dedup) → Stage2: MEMORY.md (summary) | | **session-extract** | 22:00 CST | Scan session JSONL → MemOS extractor/reranker → `.learnings/` + `memory/YYYY-MM-DD.md` (two-pass) | ### Memory Flow (Unified Daily Pipeline) ``` agent_end → MemOS Cloud (cloud extraction) ↓ ┌─ 18:00 sync-pull (Cloud → local cache) ─┐ │ 18:30 auto-memory.py │ │ → Stage 1: ARCHIVE.md (SHA-256 dedup) │ │ → Stage 2: MEMORY.md (one-line summary) │ │ │ │ 20:00 sync-push + reindex │ │ 22:00 session-extract.py │ │ → Pass 1: .learnings/ERRORS.md │ │ → Pass 2: memory/YYYY-MM-DD.md │ │ → archive analyzed → trash │ └───────────────────────────────────────────┘ before_agent_start → MemOS Cloud recall + Active Memory sub-agent + memory-core (bge-m3 + BM25) ↓ Layered context injected before reply ``` ## Scripts | Script | Purpose | |--------|--------| | `auto_memory_extract.py` | v3 two-stage pipeline: ARCHIVE.md archive → MEMORY.md summary | | `session-extract.py` | Scan session JSONL → .learnings/ + memory/ (two-pass) | | `seed-facts-db.py` | Initialize facts.sqlite from ARCHIVE.md | | `facts_activation.py` | Hebbian activation + daily decay + Hot/Warm/Cool | | `daily-memory-pipeline.sh` | 6-stage unified daily pipeline | | `write_file.py` | Cross-platform text writer (UTF-8/BOM/CRLF) | | `auto-setup.sh` | One-command Ollama + memory-core + MemOS Cloud setup | ## Setup ### One-command auto-setup ```bash bash scripts/auto-setup.sh ``` ### Options ```bash bash scripts/auto-setup.sh --skip-ollama # Skip Ollama install bash scripts/auto-setup.sh --skip-memos # Skip MemOS Cloud bash scripts/auto-setup.sh --dry-run # Preview only ``` ### Manual setup See `references/setup-guide.md` for step-by-step manual configuration. ## When to Use - Setting up OpenClaw memory for the first time - Configuring memory-core plugin with local Ollama embedding - Installing MemOS Cloud plugin for cross-device sync - Enabling Active Memory for pre-reply context injection - Setting up auto-memory pipeline for MEMORY.md curation - Installing cross-platform-writer for cross-OS file compatibility - Configuring automatic Dreaming and promotion pipelines - Setting up three-way sync between cloud, files, and vector DB ## Plugin Conflicts ### ❌ subconscious-personality-guardian ↔ memory-core **Incompatible.** Both use the same OpenClaw memory slot. **Fix**: Disable in openclaw.json: ```json { "plugins": { "disabled": [[REDACTED_SECRET]], "deny": [[REDACTED_SECRET]] } } ``` ### ✅ memory-core + MemOS Cloud **Compatible — designed to work in layers.** ``` User message → MemOS Cloud (static facts) → memory-core (recent context) ``` ### ✅ Active Memory + MemOS Cloud + memory-core **Compatible — triple-layer recall.** ``` User message → Active Memory sub-agent (searches all memory stores) → MemOS Cloud (injects long-term facts & preferences) → memory-core (semantic + BM25 hybrid retrieval) → Agent receives layered context ``` ### ✅ auto-memory + MemOS Cloud + memos-extractor **Designed to work together.** MemOS captures at `agent_end`, syncs to local files, then auto-memory reads those files for MEMORY.md curation. v1.10 adds a second channel: memos-extractor-0.6b API (MemOS self-developed model) returns structured facts + preferences, cross-validated against qwen3 output. ## Components ### 1. Memory Plugins (L0) See `references/architecture.md` for full configuration. ### 2. Memory Files (L1 + L2) ``` ~/.openclaw/workspace/ ├── memory/ │ ├── YYYY-MM-DD.md # Daily working memory (auto-indexed) │ ├── MEMORY_INDEX.md # Vector BM25 cluster summaries │ └── .sync-*.json # Sync state files ├── MEMORY.md # Long-term memory index (~90 lines) ├── ARCHIVE.md # Detailed archive (~220 lines, ← bidirectional refs) ├── AGENTS.md # Runtime context + memory rules └── user_workspace/ ├── memos-cloud-cache/ # Cloud-pulled memory (isolated from index) │ └── memos-cloud-*.md ├── scripts/ │ └── sync-*.py # Sync scripts (pull/push/vector) └── skills/ └── anamnesis-hub/ # Installed from ClawHub └── scripts/ # Pipeline scripts (auto-memory, session-extract, etc.) ``` ### 3. Pipeline Scripts (in anamnesis-hub/scripts/) - `auto_memory_extract.py` — v3 two-stage pipeline (ARCHIVE.md → MEMORY.md) - `session-extract.py` — Session JSONL scan → .learnings/ + memory/ - `seed-facts-db.py` — Initialize facts.sqlite from ARCHIVE.md - `facts_activation.py` — Hebbian activation + daily decay - `daily-memory-pipeline.sh` — 6-stage unified daily pipeline - `write_file.py` — Cross-platform text file writer - `auto-setup.sh` — One-command Ollama + memory-core + MemOS Cloud setup See `references/INDEX.md` for full documentation index. ## File Reference 👉 **完整文档请查阅 `references/INDEX.md`** | 文档 | 说明 | |------|------| | `references/INDEX.md` | 📌 文档入口索引（快速定位） | | `references/architecture.md` | 四层架构设计、插件配置、协同流程 | | `references/setup-guide.md` | 环境配置、手动安装步骤、cron 设置 | | `references/memory-directory.md` | memory/ 目录结构、状态文件、LCM 机制 | | `references/sync-api.md` | MemOS Cloud API 参考 | | `references/scripts-reference.md` | 所有脚本统一说明（用途、cron、依赖） | | `references/pipeline-stages.md` | 日终管线 6 阶段详解 | | `references/candidates-review.md` | P3 记忆候选审核机制 | | `references/upgrade-reset.md` | 升级路径、重置流程、卸载步骤 |


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