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OpenClaw Memory System

把记忆做成 Agent 可长期使用的底层能力:通过 hooks 自动提取用户偏好、事实和项目上下文,生成嵌入并存入本地 SQLite,在请求前检索并注入相关记忆;提供搜索、列表、统计、仪表盘和配额管理命令,付费档位通过 x402 支付解锁无限存储。

wuyunting555
wuyunting555
热度 34.62
最近更新 2026-05-18 11:37:03
分类:知识依赖:代码执行 / 读写文件 / 第三方 API / 内部调用大模型应用场景:笔记整理 / 工作流

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add shrimp-openclaw-memory
Metadata
name
openclaw-memory
description
让 OpenClaw 真的记住用户偏好、事实和上下文的长期记忆 skill。适用于你受不了每次新会话都要重复背景、希望 agent 能跨会话记住信息、并且想直接拥有可搜索、可持久化、可自动注入的记忆系统时使用。不是手工记笔记,而是一个已经做好的可运行记忆能力。
user-invocable
true
metadata
{"openclaw":{"emoji":"🧠","requires":{"bins":["node"]},"os":["darwin","linux","win32"]}}

OpenClaw Memory System

让 OpenClaw 跨会话真的记住你的偏好、事实和上下文。

一句话卖点

不是再重复背景、重复偏好、重复项目上下文,而是直接给你的 agent 装上一套可持久化、可搜索、可自动取回的长期记忆系统。

What is it?

这是一个已经做好的长期记忆 skill:

  • 记住用户偏好
  • 记住项目背景
  • 记住反复提过的事实
  • 下次会话可直接搜索和取回

你装上的不是“怎么做记忆系统的教程”,而是一套可运行的记忆能力。

为什么值得装

很多人真正烦的不是 agent 不够聪明,而是:

  • 每次开新会话都像失忆
  • 一遍遍重复偏好和背景
  • 项目上下文丢了还要重新讲
  • 明明说过的话,下次又得重来

这个 skill 解决的是:

把“记忆”从手工补上下文,变成 agent 自己能持续用的底层能力。

Key Features

  • 🧠 Persistent Memory - Remembers everything across sessions
  • 🔍 Semantic Search - Find memories by meaning, not just keywords
  • 🤖 Automatic Learning - Extracts facts and preferences automatically
  • 💾 Local Storage - SQLite database with vector embeddings
  • 💰 x402 Payments - Agents can pay for unlimited storage (0.5 USDT/month)

Free vs Pro Tier

Free Tier:

  • 100 memories maximum
  • 7-day retention
  • Basic semantic search

Pro Tier (0.5 USDT/month):

  • Unlimited memories
  • Permanent retention
  • Advanced semantic search
  • Memory relationship mapping

Installation

claw skill install openclaw-memory

Commands

# Search memories
claw memory search "What does user prefer?"

# List recent memories
claw memory list --limit=10

# Show stats
claw memory stats

# Open dashboard
claw memory dashboard

# Subscribe to Pro
claw memory subscribe

How It Works

  1. Hooks into requests - Automatically extracts important information
  2. Generates embeddings - Creates semantic vectors for search
  3. Stores locally - SQLite database with full privacy
  4. Retrieves on demand - Injects relevant memories before requests
  5. Manages quota - Prunes old memories when limits reached (Free tier)

Use Cases

  • Remember user preferences and coding style
  • Store project context and requirements
  • Learn patterns from repeated interactions
  • Maintain conversation history across sessions
  • Build knowledge base over time

Agent Economy

Agents can autonomously evaluate if Pro tier is worth it:

  • Cost: 0.5 USDT/month
  • Value: Saves tokens by eliminating context repetition
  • ROI: If persistent memory saves >0.5 USDT/month in tokens, it pays for itself

See AGENT-PAYMENTS.md for x402 integration details.

Privacy

  • All data stored locally in ~/.openclaw/openclaw-memory/
  • No external servers or telemetry
  • Embeddings can use local models (no API calls)
  • Open source - audit the code yourself

Dashboard

Access web UI at http://localhost:9091:

  • Browse and search memories
  • View memory timeline
  • Check quota and stats
  • Manage Pro subscription

Foundation for Future Tools

Memory System is the foundation for:

  • Context Optimizer - Uses memories to compress context
  • Smart Router - Learns routing patterns
  • Rate Limit Manager - Tracks usage patterns

Requirements

  • Node.js 18+
  • OpenClaw v2026.1.30+
  • OS: Windows, macOS, Linux

Links


Built by the OpenClaw community | First memory system with x402 payments