Claude Skill

oceanbase/powermem

PowerMem 为智能体和聊天机器人提供精准、敏捷且经济高效的AI驱动长期记忆,支持OpenClaw记忆插件和基于向量的存储。

概览

Stars733
Forks91
语言Python
最后更新2026-07-02
最近同步2026-07-03
前往 GitHub

仓库信息

拥有者oceanbase
仓库powermem
完整名称oceanbase/powermem
Repo ID1,093,180,734

安装这个 Skill

git clone https://github.com/oceanbase/powermem

Registry 信息

类型mcp_server
质量分80/100
验证状态readme_parsed
最近验证2026-06-07
平台
ClaudeMCPOpenClawCodexCursor
能力
pdfmemorysearchimageterminalagenticagentsaiai-agentsai-companion
识别文件
README.mddocsexamplespyproject.tomltests
安装方式
  • git clone https://github.com/oceanbase/powermem
  • pip install powermem langchain langchain-openai
  • pip install powermem
  • pip install "powermem[cli]"
  • pip install "powermem[server]"

项目简介

PowerMem 是一个专为AI智能体和聊天机器人设计的AI驱动长期记忆系统,提供精准、敏捷且经济高效的记忆管理。它为OpenClaw(Clawdbot)记忆插件提供友好支持。

英文描述

PowerMem: AI Memory Plugin— Accurate, Agile, Affordable. Make AI Agent smarter.

要点

  • 为智能体提供AI驱动的长期记忆
  • 精准、敏捷且经济高效的设计
  • 支持OpenClaw(Clawdbot)记忆插件
  • 使用Python构建,适用于AI/LLM应用
  • 基于向量的记忆存储与检索
  • 上下文工程能力

使用场景

  • AI智能体记忆管理
  • 聊天机器人对话历史存储
  • 多智能体系统协调
  • LLM上下文窗口扩展
  • 个人AI伴侣开发
  • 智能体工作流记忆持久化

README 摘要

# PowerMem **Persistent, self-evolving memory for AI agents and applications.** [![PyPI version](https://img.shields.io/pypi/v/powermem)](https://pypi.org/project/powermem/) [![PyPI downloads](https://img.shields.io/pypi/dm/powermem)](https://pypi.org/project/powermem/) [![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://pypi.org/project/powermem/) [![License Apache 2.0](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](LICENSE) [![GitHub](https://img.shields.io/badge/GitHub-oceanbase%2Fpowermem-181717?logo=github)](https://github.com/oceanbase/powermem) [![Discord](https://img.shields.io/badge/Discord-community-5865F2?logo=discord&logoColor=white)](https://discord.com/invite/74cF8vbNEs) *English · [中文](README_CN.md) · [日本語](README_JP.md)* PowerMem combines vector, full-text, and graph retrieval with LLM-driven memory extraction and Ebbinghaus-style time decay. It ships **two-layer Experience + Skill distillation** for self-evolving memory, multi-agent isolation, user profiles, and multimodal signals (text, image, audio). --- ## Benchmarks ### [LOCOMO](https://github.com/snap-research/locomo) | Metric | PowerMem | Baseline | Improvement | |--------|----------|-------------------------|-------------| | Accuracy | **87.79%** | 52.9% | **+65.9%** | | Search p95 latency | **1.44 s** | 17.12 s | **-91.6%** | | Tokens | **~0.9 k** | 26 k | **-96.5%** | ### [AppWorld](https://github.com/StonyBrookNLP/appworld) | Metric | PowerMem | Baseline | Improvement | |--------|----------|-------------------------|-------------| | Pass | **39%** | 24% | **+62.5%** | | Avg steps | **6.2** | 9.5 | **-34.7%** | | Total tokens | **1.74 M** | 2.56 M | **-32.0%** | Reproduce: [`benchmark/`](benchmark/). Under the hood: **two-layer Experience + Skill

话题

探索更多

数据来自 GitHub,同步时间:2026-07-03