據 AlphaMoat 統計,截至 2026-09,Mem0 累計下載 4,027 次、獲 4 個星標,在全部 5,078 個 AI 技能中排名第 2,534 位,在「AI 智慧體」分類(969 個)中排名第 496 位。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 124 | 3937 | 3 | - |
| SkillHub ↗ | 122 | 4027 | 4 | — |
各平台的計數口徑不同,統計時間不一致。
文件介紹如何搜尋已存記憶、在使用者明確要求時新增內容、從對話提取記憶、列出記憶和刪除指定記錄。記憶文字與搜尋查詢可能由 OpenAI 處理並在本地保留;儲存前應確認使用者意圖,不儲存秘密或敏感個人資訊,並限制列出或刪除全部記憶的許可權。
資料釋出於 2026-10-29
以下是根據技能文件生成的示例,不代表實際執行結果。
2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。
| 月份 | 累計下載 | 環比 | 星標 |
|---|---|---|---|
| 2026-09 | 4027 | — | 4 |
| 2026-08 | 3824 | +5.6% | 4 |
| 2026-07 | 3622 | +11.8% | 4 |
| 2026-06 | 3240 | +5.0% | 3 |
| 2026-05 | 3085 | — | 3 |
| 2026-03 | 2130 | — | 3 |
以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 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
將開發過程中的經驗、錯誤與修正記錄為 Markdown 檔案,供日後重用並提升為工作區記憶。
用自然語言描述需求,在本機與遠端技能市場尋找技能並取得推薦。
給 AI agent 用的瀏覽器自動化命令列技能。
將使用者明確糾正與自我檢視整理成分層記憶,並依使用情況升級或封存。
在安裝第三方技能之前,按清單審查來源、許可權與危險模式的技能。
將代理記憶與跨技能狀態整理成具型別與約束的知識圖譜。
規劃 AI 代理主動跟進、工作記憶與回顧流程的工作區設定指南。
指導如何建立與更新有效 Claude 技能的寫作指南。
資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。