据 AlphaMoat 统计,截至 2026-09,Persistent Memory 累计下载 2,919 次、获 0 个星标,在全部 5,078 个 AI 技能中排名第 3,790 位,在「AI 智能体」分类(969 个)中排名第 763 位。
| 平台 | 累计安装 | 累计下载 | 星标 | 评论 |
|---|---|---|---|---|
| ClawHub ↗ | 86 | 2,919 | - | - |
| SkillHub ↗ | 119 | 2,881 | - | — |
各平台的计数口径不同,统计时间不一致。
把 Markdown 记忆文件作为来源,由索引器生成 ChromaDB 向量索引和 NetworkX 关系图;可检索记录、查看索引状态,并在编辑记忆文件后重新索引。按文档示例通过统一脚本安装和配置,依赖包括 ChromaDB、NetworkX 与 sentence-transformers。内置检索默认不覆盖所有工作区文件;按说明扩充配置并重新索引。
资料发布于 2026-10-26
以下是根据技能文档生成的示例,不代表实际运行结果。
依据技能文档中的声明整理,未经实际安装或运行验证。
2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。
| 月份 | 累计下载 | 环比 | 星标 |
|---|---|---|---|
| 2026-09 | 2,919 | — | - |
| 2026-08 | 2,765 | +7.8% | - |
| 2026-07 | 2,566 | +17.1% | - |
| 2026-06 | 2,191 | +11.6% | - |
| 2026-05 | 1,964 | — | - |
| 2026-03 | 1,065 | — | - |
以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。
--- name: persistent-memory version: 3.0.0 description: Three-layer persistent memory system (Markdown + ChromaDB vectors + NetworkX knowledge graph) for long-term agent recall across sessions. One-command setup with automatic OpenClaw integration. Use when the agent needs to remember decisions, facts, context, or institutional knowledge between sessions. --- # Persistent Memory Adds persistent three-layer memory to any OpenClaw workspace. The agent gains semantic recall across sessions — decisions, facts, lessons, and institutional knowledge survive restarts.
## Architecture
| Layer | Technology | Purpose |
|-------|-----------|---------|
| L1: Markdown | MEMORY.md + daily logs + reference/ | Human-readable curated knowledge |
| L2: Vector | ChromaDB + all-MiniLM-L6-v2 | Semantic search across all memories |
| L3: Graph | NetworkX | Relationship traversal between concepts |
All three layers sync together. The indexer updates L2 and L3 from L1 automatically.
## ⚠️ Critical Integration: OpenClaw Memory Configuration
**Problem:** OpenClaw has its own built-in memory search system, but by default it only indexes `MEMORY.md` and `memory/*.md` files. Critical workspace files like `SOUL.md` (agent directives), `AGENTS.md` (behavior rules), and `PROJECTS.md` (active work) are **ignored**.
**Impact:** Agents can violate explicit directives because they're not found in memory searches. This causes operational failures where agents ignore their own rules.
**Solution:** The `configure_openclaw.py` script adds a `memorySearch` configuration block to OpenClaw that indexes all critical workspace files. This makes directive compliance **automatic** rather than optional.
## Setup
**One command from workspace root:**
```bash
bash skills/persistent-memory/scripts/unified_setup.sh
```
This automatically:
- ✅ Creates 3-layer memory system (Markdown + Vector + Graph)
- ✅ Installs all Python dependencies (ChromaDB, NetworkX, sentence-transformers)
- ✅ Configures OpenClaw memorySearch integration (directive compliance)
- ✅ Indexes existing MEMORY.md if present
- ✅ Sets up daily maintenance automation
**No manual configuration needed.** The script handles everything including OpenClaw integration that prevents agents from ignoring workspace directives (SOUL.md, AGENTS.md, etc.).
## Daily Usage
### Writing Memories
- **MEMORY.md** — Curated long-term knowledge (decisions, architecture, lessons learned). Update after significant events.
- **memory/YYYY-MM-DD.md** — Daily logs. Raw notes of what happened each day.
- **reference/*.md** — Institutional facts (people, repos, infrastructure, business rules). The agent's encyclopedia.
### Indexing (after editing any memory file)
```bash
vector_memory/venv/bin/python vector_memory/indexer.py
```
The indexer parses MEMORY.md, reference/*.md, and memory/*.md into vector embeddings and rebuilds the knowledge graph. Run after every edit to keep layers in sync.
### Searching
```bash
vector_memory/venv/bin/python vector_memory/search.py "your query"
```
Returns top-3 semantically similar chunks with source file and section.
### Sync Status Check
```bash
vector_memory/venv/bin/python vector_memory/auto_retrieve.py --status
```
Reports sync health: MEMORY.md hash vs indexed state, chunk count, graph size. Use in heartbeats to detect drift.
## Agent Behavior Rules
Add these to AGENTS.md or SOUL.md:
### Pre-Response (mandatory)
Before answering questions about prior work, decisions, dates, people, or preferences — search memory first. Use `memory_search` or run `auto_retrieve.py` with the query. Never say "I don't remember" without checking.
**CRITICAL:** OpenClaw's built-in memory search should now automatically find directive files (SOUL.md, AGENTS.md) if `configure_openclaw.py` was run. If memory searches are not finding agent rules or workspace directives, the OpenClaw integration is missing or broken.
### Pre-Action (mandatory)
Before executing any action that references an external identifier (URL, handle, email, repo name, address) — query `reference/` files for the exact value. If not found, query vector memory. If still not found, ask the user. **Never fabricate identifiers.**
### Post-Edit (mandatory)
After editing MEMORY.md or any file in reference/ or memory/ — re-index:
```bash
vector_memory/venv/bin/python vector_memory/indexer.py
```
### Heartbeat Integration
Add to HEARTBEAT.md:
```
## Memory Sync Check
Run `vector_memory/venv/bin/python vector_memory/auto_retrieve.py --status` and if status is OUT_OF_SYNC, re-index with `vector_memory/venv/bin/python vector_memory/indexer.py`.
```
## Reference Directory (Optional but Recommended)
Create `reference/` in the workspace root as the agent's institutional knowledge base:
```
reference/
├── people.md — Contacts, roles, communication details
├── repos.md — GitHub repositories, URLs, status
├── infrastructure.md — Hosts, IPs, ports, services
├── business.md — Company info, strategies, rules
└── properties.md — Domain-specific entities (deals, products, etc.)
```
These files are vector-indexed alongside MEMORY.md. The agent queries them before any action involving external identifiers. Facts accumulate over time — the agent that never forgets.
## File Structure After Setup
```
workspace/
├── MEMORY.md — Curated long-term memory (L1)
├── memory/
│ ├── 2026-02-17.md — Daily log
│ └── heartbeat-state.json — Sync tracking
├── reference/ — Institutional knowledge (optional)
│ ├── people.md
│ └── ...
└── vector_memory/
├── indexer.py — Index all markdown into vectors + graph
├── search.py — Semantic search CLI
├── graph.py — NetworkX knowledge graph
├── auto_retrieve.py — Status checker + auto-retrieval
├── chroma_db/ — Vector database (gitignored)
├── memory_graph.json — Knowledge graph (auto-generated)
└── venv/ — Python venv (gitignored)
```
## Troubleshooting
- **"No module named chromadb"** — Run setup.sh again or activate the venv: `source vector_memory/venv/bin/activate`
- **OUT_OF_SYNC status** — Run the indexer: `vector_memory/venv/bin/python vector_memory/indexer.py`
- **Empty search results** — Check that MEMORY.md has content and the indexer has been run at least once
- **SIGSEGV on indexing** — Usually caused by incompatible ML libs. The setup script pins known-good versions.
- **Agent ignoring SOUL.md/AGENTS.md directives** — OpenClaw integration missing. Run `python skills/persistent-memory/scripts/configure_openclaw.py` to fix.
- **Memory searches not finding workspace files** — Check OpenClaw configuration: `openclaw config get | grep memorySearch`
- **"Configuration verification failed"** — Restart OpenClaw manually: `openclaw gateway restart`
记录自身发现以实现自我改进的技能
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自我反思+自我批评+自我学习+自组织记忆。智能体评估自身工作、发现错误并持续改进。
在安装第三方技能之前,按清单审查来源、权限与危险模式的技能。
类型化知识图谱,用于结构化智能体记忆与可组合技能。适用于以下场景:创建/查询实体(人物、项目、任务、事件、文档)、关联相关对象、强制执行约束、将多步操作规划为图谱变换,或当技能需要共享状态时。触发关键词包括"记住""我知道关于什么""将X链接到Y""显示依赖关系"、实体增删改查操作,以及跨技能数据访问。
将 AI 代理从任务执行者转变为能够预见需求、持续优化的主动伙伴。现已支持 WAL Protocol、Working Buffer、Autonomous Crons 及经过实战验证的模式。属于 Hal Stack 🦞。
创建有效技能指南。当用户希望创建新技能(或更新现有技能)以利用专业知识、工作流程或工具集成扩展 Claude 的能力时,应使用此技能。
数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。