据 AlphaMoat 统计,截至 2026-09,Mem0 累计下载 4,027 次、获 4 个星标,在全部 5,078 个 AI 技能中排名第 2,534 位,在「AI 智能体」分类(969 个)中排名第 496 位。
| 平台 | 累计安装 | 累计下载 | 星标 | 评论 |
|---|---|---|---|---|
| ClawHub ↗ | 124 | 3,937 | 3 | - |
| SkillHub ↗ | 122 | 4,027 | 4 | — |
各平台的计数口径不同,统计时间不一致。
文档介绍如何搜索已存记忆、在用户明确要求时添加内容、从对话提取记忆、列出记忆和删除指定记录。记忆文本与搜索查询可能由 OpenAI 处理并在本地保留;保存前应确认用户意图,不存储秘密或敏感个人信息,并限制列出或删除全部记忆的权限。
资料发布于 2026-10-29
以下是根据技能文档生成的示例,不代表实际运行结果。
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 | 4,027 | — | 4 |
| 2026-08 | 3,824 | +5.6% | 4 |
| 2026-07 | 3,622 | +11.8% | 4 |
| 2026-06 | 3,240 | +5.0% | 3 |
| 2026-05 | 3,085 | — | 3 |
| 2026-03 | 2,130 | — | 3 |
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: mem0 description: >- Intelligent memory layer for Clawdbot using Mem0. Provides semantic search and automatic storage of user preferences, patterns, and context across conversations. Use when (1) User explicitly says "remember this", (2) Learning user preferences or patterns during conversation, (3) Searching for past context about user's choices/preferences, (4) Building adaptive responses based on learned user behavior. Complements MEMORY.md (structured facts) with dynamic, conversational memory (learned preferences, patterns, adaptive context). --- # Mem0 Memory Integration Mem0 adds an intelligent, adaptive memory layer to Clawdbot that automatically learns and recalls user preferences, patterns, and context across all interactions.
## Core Workflow
### 1. Search Before Responding
Before answering user questions, search mem0 for relevant context:
```bash
node scripts/mem0-search.js "user preferences" --limit=3
```
Use retrieved memories to:
- Personalize responses
- Remember preferences
- Recall past patterns
- Adapt communication style
### 2. Store After Interactions
**Explicit Storage** (when user says "remember this"):
```bash
node scripts/mem0-add.js "Abhay prefers concise updates"
```
**Conversation Storage** (for context learning):
```bash
# Pass messages as JSON
node scripts/mem0-add.js --messages='[{"role":"user","content":"I like brief updates"},{"role":"assistant","content":"Got it!"}]'
```
## Available Commands
### Search Memories
```bash
node scripts/mem0-search.js "query text" [--limit=3] [--user=abhay]
```
Searches semantically across stored memories. Returns relevant memories ranked by relevance.
### Add Memory
```bash
# Simple text
node scripts/mem0-add.js "memory text" [--user=abhay]
# Conversation messages (auto-extracts memories)
node scripts/mem0-add.js --messages='[{...}]' [--user=abhay]
```
Mem0's LLM automatically extracts, deduplicates, and merges related memories.
### List All Memories
```bash
node scripts/mem0-list.js [--user=abhay]
```
Shows all stored memories for the user with IDs and creation dates.
### Delete Memories
```bash
# Delete specific memory
node scripts/mem0-delete.js <memory_id>
# Delete all memories for user
node scripts/mem0-delete.js --all --user=abhay
```
## What to Store vs Not Store
### ✅ Store These:
- **Explicit requests**: "Remember that I..."
- **Preferences**: Communication style, format choices
- **Personal context**: Work info, interests, family (non-sensitive)
- **Usage patterns**: Frequent requests, timing preferences
- **Corrections**: When user corrects your mistakes
- **Adaptive facts**: Current projects, recent interests
### ❌ Don't Store:
- Secrets, passwords, API keys
- Temporary context (unless explicitly requested)
- System errors or debug info
- Information already in MEMORY.md (avoid duplication)
## Complementing Clawdbot Memory
**Clawdbot MEMORY.md** (Structured, Deliberate):
- Permanent facts: Name = Abhay, Location = Singapore
- Reference data: Email, blog URL, Twitter handle
- Structured knowledge: Project details, credentials
**Mem0** (Dynamic, Learned):
- Preferences: "Abhay prefers concise updates"
- Patterns: "Usually asks for bus info at 8:30am"
- Adaptive context: "Currently interested in AI news"
- Behavioral: "Likes direct answers, minimal fluff"
**Use both together**: Check MEMORY.md for facts, check mem0 for preferences/patterns.
## Performance Benefits
- **+26% accuracy** over OpenAI Memory (LOCOMO benchmark)
- **91% faster** than full-context retrieval
- **90% fewer tokens** than including all conversation history
- **Sub-50ms** semantic search retrieval
## Configuration
Located in `scripts/mem0-config.js`:
```javascript
{
embedder: "openai/text-embedding-3-small",
llm: "openai/gpt-4o-mini",
vectorStore: "memory" (local),
historyDb: "~/.mem0/history.db",
userId: "abhay"
}
```
Uses Clawdbot's OpenAI API key from environment (`OPENAI_API_KEY`).
## Integration Patterns
For detailed workflow patterns, error handling, and best practices, see:
- `references/integration-patterns.md`
## Programmatic Use
All scripts support `JSON_OUTPUT` environment variable for programmatic access:
```bash
JSON_OUTPUT=1 node scripts/mem0-search.js "query"
```
Returns JSON after human-readable output (look for `---JSON---` marker).
## Resources
### scripts/
- `mem0-config.js` - Configuration and instance initialization
- `mem0-search.js` - Search memories semantically
- `mem0-add.js` - Add new memories
- `mem0-list.js` - List all memories
- `mem0-delete.js` - Delete memories
### references/
- `integration-patterns.md` - Detailed best practices and patterns
把开发过程中的经验、错误与纠正记录为 Markdown 文件,供后续复用并晋升为工作区记忆。
用自然语言描述需求,在本地与远程技能市场里找技能并给出推荐的技能。
给 AI agent 用的浏览器自动化命令行技能。
把用户纠正与自我复盘沉淀成分层记忆、并按使用情况升降级的技能。
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。
把 agent 的记忆与跨技能状态存成带类型和约束的知识图谱的技能。
一份为 AI 代理规划主动跟进、工作记忆与复盘流程的工作区配置指南。
指导如何创建与更新有效 Claude 技能的写作指南。
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。