OpenClaw Agent 本地知识库 + 自动记忆 & 文件管理

OpenClaw Agent 本地知识库 + 自动记忆 & 文件管理。Obsidian vault 实时同步、零 LLM 成本压缩、向量检索、自愈健康监控、自动归档/分类/清理。Local knowledge base + auto memory & file management for OpenClaw agents.

让 OpenClaw Agent 拥有本地知识库和自动记忆 & 文件管理。

Obsidian vault 实时同步 → Agent 可搜索。会话观察零 LLM 成本压缩 → 不丢失。自动归档/分类/清理 → vault 自己管自己。向量检索 + 嵌入服务 + 同步链路 → 自动健康监控 + 自愈修复。

Build a local knowledge base + automatic memory & file management for your OpenClaw Agent.

Auto-sync Obsidian vault → searchable by Agent. Zero-LLM compression → nothing lost. Auto archive/categorize/cleanup → vault manages itself. Vector search + embedding + sync chain → auto health monitoring + self-healing.

关键词 / Keywords

本地知识库、自动记忆管理、长期记忆、向量检索、知识库同步、Obsidian vault、Qdrant、embedding、agent memory、零 LLM 压缩、自愈监控、vault 同步、概念聚合、记忆蒸馏、健康检查、自动维护、DAG 调度、抗体自愈

local knowledge base, auto memory management, long-term memory, vector search, knowledge sync, Obsidian vault, Qdrant, embedding, agent memory, zero-LLM compression, self-healing, vault sync, concept consolidation, memory distillation, health monitoring, auto maintenance, DAG scheduler, antibody healing

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中文

这是什么

一个 OpenClaw 的记忆增强技能。解决了三个问题：

1. Vault 内容进不了记忆 — 你用 Obsidian 记了大量笔记，但 Agent 的 memory_search 搜不到
2. 会话记忆丢失 — 工具调用、决策、发现等有价值信息，对话结束就没了
3. 记忆系统维护成本高 — 向量库、嵌入服务、同步链路，哪个断了都不知道

安装

cd ~/.openclaw/workspace/skills
git clone https://github.com/yxyujian98-png/vault-memory-system.git
cd openclaw-memory-system
pip install -r requirements.txt
docker-compose up -d
python scripts/setup.py --vault-dir /path/to/vault

前置条件

组件	必需	说明
Python 3.10+	✅	脚本运行环境
Qdrant	✅	向量数据库
嵌入服务	✅	LM Studio / Ollama / OpenAI 兼容
Obsidian Vault	✅	Markdown 知识库
LLM API	可选	高重要性记忆才需要

运行时数据流

┌─────────────────────────────────────────────────────┐
│                Layer 1: OpenClaw 内置                 │
│                                                     │
│  session-memory hook → memory/YYYY-MM-DD-HHMM.md   │
│  memory-compact hook → compaction 前提取记忆          │
│  memory-extract hook → /new、/reset 时提取           │
│                       ↓                             │
│  memory_search ← SQLite (FTS5 + sqlite-vec + 混合)  │
└─────────────────────────────────────────────────────┘
         │ sync_vault_memory.py
         ↓
┌─────────────────────────────────────────────────────┐
│                Layer 2: 自定义脚本                     │
│                                                     │
│  Cron 每 45 分钟 → orchestrator --light --parallel  │
│    → vault_guardian / extract_memories / memory_health│
│    → 12 个任务按 DAG 拓扑并行执行                      │
│                                                     │
│  Qdrant (knowledge_base)                            │
│    → vault 分块 / 工具观察 / 融合概念                  │
└─────────────────────────────────────────────────────┘

核心设计

• 零 LLM 成本：compress.py 纯规则驱动
• 三级嵌入降级：LM Studio → ONNX → numpy 哈希
• 版本追踪：version / is_latest / supersedes
• PRISM 意图路由：事实型 / 过程型 / 反思型 / 时序型
• 抗体自愈：错误模式 → 自动修复规则

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English

What is this

An OpenClaw memory enhancement skill. Solves three problems:

1. Vault content not in memory — You have extensive Obsidian notes, but Agent's memory_search can't find them
2. Session memory lost — Tool calls, decisions, discoveries — all gone when session ends
3. Memory system maintenance costly — Vector DB, embedding service, sync chain — which one broke?

Installation

cd ~/.openclaw/workspace/skills
git clone https://github.com/yxyujian98-png/vault-memory-system.git
cd openclaw-memory-system
pip install -r requirements.txt
docker-compose up -d
python scripts/setup.py --vault-dir /path/to/vault

Prerequisites

Component	Required	Description
Python 3.10+	✅	Script runtime
Qdrant	✅	Vector database
Embedding server	✅	LM Studio / Ollama / OpenAI-compatible
Obsidian Vault	✅	Markdown knowledge base
LLM API	Optional	Only for high-importance memories

Runtime data flow

┌─────────────────────────────────────────────────────┐
│                Layer 1: OpenClaw Built-in            │
│                                                     │
│  session-memory hook → memory/YYYY-MM-DD-HHMM.md   │
│  memory-compact hook → extract before compaction    │
│  memory-extract hook → extract on /new, /reset      │
│                       ↓                             │
│  memory_search ← SQLite (FTS5 + sqlite-vec + hybrid)│
└─────────────────────────────────────────────────────┘
         │ sync_vault_memory.py
         ↓
┌─────────────────────────────────────────────────────┐
│                Layer 2: Custom Scripts               │
│                                                     │
│  Cron every 45m → orchestrator --light --parallel   │
│    → vault_guardian / extract_memories / memory_health│
│    → 15 tasks in DAG topological parallel           │
│                                                     │
│  Qdrant (knowledge_base)                            │
│    → vault chunks / tool observations / fused concepts│
└─────────────────────────────────────────────────────┘

Core design

• Zero LLM cost: compress.py is purely rule-driven
• 3-level embedding fallback: LM Studio → ONNX → numpy hash
• Version tracking: version / is_latest / supersedes
• PRISM intent routing: factual / procedural / reflective / recency
• Antibody self-healing: error patterns → auto-fix rules

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License

MIT