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[feat]: openclaw-memory-plugin: 新增插件功能#479

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qin-ctx merged 2 commits intovolcengine:mainfrom
qin-ptr:qin-ptr
Mar 8, 2026
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[feat]: openclaw-memory-plugin: 新增插件功能#479
qin-ctx merged 2 commits intovolcengine:mainfrom
qin-ptr:qin-ptr

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@qin-ptr
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@qin-ptr qin-ptr commented Mar 8, 2026

Description

全面改进 openclaw 内存插件功能,包括重构核心实现、新增关键功能、优化文档和示例代码。

Type of Change

  • New feature (non-breaking change that adds functionality)
  • Documentation update
  • Refactoring (no functional changes)

Changes Made

新增功能

  • client.ts: 新增客户端实现,提供与内存系统交互的核心接口
  • memory-ranking.ts: 新增内存排名功能,支持对记忆内容进行排序和检索优化
  • process-manager.ts: 新增进程管理,负责内存操作的调度和并发控制
  • text-utils.ts: 新增文本工具,提供文本处理和相似度计算功能
  • demo-memory-ajie.py 和 demo-memory-xiaomei.py: 新增示例脚本,展示内存插件的使用方法
  • INSTALL-AGENT.md: 新增专门针对智能体安装的文档

优化改进

  • index.ts: 重构了主要入口文件,大幅简化代码,提升可维护性
  • config.ts: 更新配置文件,优化插件配置选项
  • package.json: 更新依赖,新增 required 字段和配置
  • openclaw.plugin.json: 更新插件配置,增强功能描述
  • tsconfig.json: 新增 TypeScript 配置文件

文档优化

  • README.md: 全面更新文档,介绍新功能和使用方法
  • INSTALL.md: 改进安装指南
  • INSTALL-ZH.md: 更新中文安装说明
  • SKILL.md: 优化技能文档,提供更详细的说明
  • install.sh 和 install.ps1: 优化安装脚本,支持更多场景

其他变更

  • memory_extraction.yaml: 更新内存提取提示模板
  • binding_client.py: 在两个位置新增对内存功能的支持

Testing

  • 代码已通过基础功能测试
  • 新增示例脚本可正常运行
  • 安装流程已优化并测试

Checklist

  • My code follows the project's coding style
  • I have performed a self-review of my code
  • I have made corresponding changes to the documentation
  • I have added examples for new features

Additional Notes

插件功能已全面重构,提供了更稳定、高效的内存管理能力。支持多种内存操作场景,包括对话记忆、知识存储和语义检索。

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@qin-ctx qin-ctx merged commit 4f32d3a into volcengine:main Mar 8, 2026
19 of 22 checks passed
@github-project-automation github-project-automation bot moved this from Backlog to Done in OpenViking project Mar 8, 2026
@qin-ctx qin-ctx changed the title [feat]: openclaw-memory-plugin: 全面改进内存插件功能 [feat]: openclaw-memory-plugin: 全面改进记忆插件功能 Mar 8, 2026
@qin-ctx qin-ctx changed the title [feat]: openclaw-memory-plugin: 全面改进记忆插件功能 [feat]: openclaw-memory-plugin: 新增插件功能 Mar 9, 2026
@wlff123
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wlff123 commented Mar 10, 2026

建议:
1.user+assistant回复一起进行记忆抽取,可能对用户原本的记忆造成污染,把大模型返回的总结或者建议型语句也沉淀成记忆,增大token消耗
2.记忆检索召回范围变大以及整段上下文的注入,可能导致输入token成本变高以及响应时间变慢

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4 participants