Anamnesis Hub

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。

Audits

Warn

ClawScanWarn

Agentic behavior and permission review.

Static analysisReview

Pattern checks against bundled files.

VirusTotalPass

Multi-engine malware detections and file reputation.

Install

openclaw skills install anamnesis-hub

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 scripts/auto-setup.sh

Options

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:

{
  "plugins": {
    "disabled": ["subconscious-personality-guardian"],
    "deny": ["subconscious-personality-guardian"]
  }
}

✅ 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