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README.md
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README.md
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@ -140,45 +140,55 @@ reme version
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curl -s http://127.0.0.1:23333/version -H 'Content-Type: application/json' -d '{}'
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```
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#### 接入方式
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ReMe 不绑定具体 Agent 框架，启动服务后可以按三种方式接入：
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| 方式 | 适用场景 | 使用说明 |
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|-----------------------|---------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|
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| `skill.md + cli` | 任意支持读取 skill/system prompt 的 Agent 框架 | 将 [reme_memory skill](reme/skills/reme_memory/SKILL.md) 加入 Agent，并允许 Agent 调用 `reme search/read/write/auto_memory/proactive` 等 CLI。 |
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| `background` / `cron` | 索引更新、资源监听、定时 dream 等自动流程 | 执行 `reme start` 后自动运行，无需在 Agent 侧手动调用。 |
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| `hook` | 需要把对话、资源或主动主题接入 Agent 生命周期的流程 | 在 Agent 框架中手动加入 `auto_memory`、`auto_resource`、`auto_dream`、`proactive` 调用点。 |
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QwenPaw 2.0 将会集成新版 ReMe；未来也会推出 Claude Code plugin，降低手动接入成本。
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更多细节见 [快速开始](docs/zh/quick_start.md)。
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---
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## 核心能力
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| 能力 | 说明 |
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|---------------------------------------------|-------------------------------------------------------------------------------|
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| [Memory as File](docs/zh/memory_as_file.md) | 用 vault 目录、Markdown、frontmatter 和 wikilink 表达记忆分层与文件关系。 |
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| [Memory Search](docs/zh/memory_search.md) | 持续索引 `daily/`、`digest/`、`resource/`，支持 BM25、可选向量召回和链接展开。 |
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| [Auto Memory](docs/zh/auto_memory.md) | 将对话按 `session_id` 保存为原始 JSONL，并整理成 daily 记忆卡片。 |
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| [Auto Resource](docs/zh/auto_resource.md) | 将 `resource/` 中的外部资料解读为 daily 资源卡片，保留原始资料出处。 |
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| [Auto Dream](docs/zh/auto_dream.md) | 从 daily 输入中抽取长期记忆单元，沉淀到 `digest/personal`、`digest/procedure` 和 `digest/wiki`。 |
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| [Auto Link](docs/zh/auto_link.md) | 在写入 digest 时召回相关节点，完成去重、来源链接和 digest 之间的 wikilink 织入。 |
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| [Proactive](docs/zh/proactive.md) | 读取 `auto_dream` 生成的 `interests.yaml`，向上层 Agent 暴露当天值得主动关注的主题。 |
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### Memory as File
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<p align="center">
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<img src="docs/figure/memory-as-file.svg" alt="Memory as File model" width="78%">
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</p>
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### Auto Memory & Auto Resource(BETA)
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<p align="center">
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<img src="docs/figure/auto-memory-resource.svg" alt="Auto Memory and Auto Resource flow" width="78%">
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</p>
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### Auto Dream & Auto Link & Proactive
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<p align="center">
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<img src="docs/figure/auto-dream.svg" alt="Auto Dream flow" width="78%">
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</p>
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### Memory Search
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<p align="center">
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<img src="docs/figure/memory-search.svg" alt="Memory Search flow" width="78%">
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</p>
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| 类型 | name | 描述 | 参数 |
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|------------|---------------------------------------------|---------------------------------------------------------------------------|--------------------------------------------------------|
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| background | `index_update_loop` | 后台监听 `daily/`、`digest/`、`resource/` 中的 Markdown/JSONL 变化，并持续更新检索索引。 | 配置项：`watch_dirs`、`watch_suffixes` |
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| background | `resource_watch_loop` | 后台监听 `resource/` 资料变化，更新 resource catalog，并触发资源解读。 | 配置项：`watch_dirs`、`watch_suffixes` |
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| background | `digest_watch_loop` | 后台监听 `daily/` 与 `digest/` 的 Markdown 变化，更新 digest catalog 并记录变化。 | 配置项：`watch_dirs`、`watch_suffixes` |
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| cron | `dream_cron` | 每天 23:00 定时执行 dream 流程：抽取长期记忆、整合 digest、生成兴趣主题并持久化 catalog。 | 配置项：`cron` |
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| hook | [`auto_dream`](docs/zh/auto_dream.md) | 扫描当天 day-index 与 daily notes，抽取并整合长期记忆单元，写入 `interests.yaml`。 | `date`、`hint`、`topic_count`、`topic_diversity_days` |
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| hook | [`auto_memory`](docs/zh/auto_memory.md) | 将对话消息记录并整理为 daily 记忆卡片。 | 必填：`messages`；可选：`session_id`、`memory_hint` |
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| hook | [`auto_resource`](docs/zh/auto_resource.md) | 将 resource 文件变更批次解读为 daily 资源卡片。 | 必填：`changes`；每项可含 `path`、`file_path`、`change` |
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| hook | [`proactive`](docs/zh/proactive.md) | 读取 `daily/<date>/interests.yaml`，向上层 Agent 暴露最新用户兴趣主题。 | `date`、`include_content` |
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| cli | `version` | 返回 ReMe 包版本。 | 无 |
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| cli | `health_check` | 返回 ReMe 组件健康检查摘要。 | 无 |
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| cli | `help` | 列出已注册 jobs 及其 metadata。 | 无 |
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| cli | `traverse` | 从指定路径出发遍历 wikilink 图谱。 | 必填：`path`；可选：`depth`、`direction` |
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| cli | `reindex` | 清空 file store，并基于现有文件重建索引。 | 配置项：`watch_dirs`、`watch_suffixes` |
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| cli | [`search`](docs/zh/memory_search.md) | 在 vault 中执行混合检索，结合向量召回、BM25 和 RRF 融合。 | 必填：`query`；可选：`limit`、`min_score` |
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| cli | `node_search` | 根据候选抽象的名称与描述，召回相似 digest 节点用于去重或关联。 | 必填：`query`；可选：`limit` |
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| cli | `daily_create` | 创建 daily session note：`daily/<date>/<session_id>.md` 或 `daily/<date>.md`。 | `session_id`、`date` |
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| cli | `daily_list` | 列出某一天的 notes。 | `date` |
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| cli | `daily_reindex` | 重建 day-index 页面 `daily/<date>.md`。 | `date` |
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| cli | `frontmatter_delete` | 删除文件 frontmatter 中的指定 keys。 | 必填：`path`、`keys` |
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| cli | `frontmatter_read` | 读取文件 frontmatter。 | 必填：`path` |
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| cli | `frontmatter_update` | 合并 key-values 到文件 frontmatter。 | 必填：`path`、`metadata` |
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| cli | `stat` | 获取 vault 路径状态，包括大小、mtime、是否存在、是否目录或文件。 | 必填：`path` |
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| cli | `list` | 列出 vault 路径下的文件。 | `path`、`recursive`、`limit` |
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| cli | `move` | 移动或重命名 vault 文件，并默认重写入站 wikilink。 | 必填：`src_path`、`dst_path`；可选：`overwrite`、`retarget` |
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| cli | `delete` | 删除 vault 文件或文件夹，并返回仍存在的入站 wikilink。 | 必填：`path` |
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| cli | `read` | 读取 vault 下的 Markdown 文件。 | 必填：`path`；可选：`start_line`、`end_line` |
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| cli | `read_image` | 读取 vault 下的图片文件并返回 base64。 | 必填：`path` |
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| cli | `write` | 创建或覆盖 Markdown 文件，并写入 name/description frontmatter。 | 必填：`path`、`name`、`description`、`content`；可选：`metadata` |
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| cli | `edit` | 对 Markdown 文件执行全文 find-and-replace。 | 必填：`path`、`old`、`new` |
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---
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@ -1,6 +1,6 @@
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<svg xmlns="http://www.w3.org/2000/svg" width="1200" height="680" viewBox="0 0 1200 680" role="img" aria-labelledby="title desc">
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<title id="title">ReMe auto memory and auto resource flow</title>
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<desc id="desc">A concise flow where conversations and dated resources are turned into daily cards, indexed by the daily page, and then used by downstream dream and search workflows.</desc>
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<desc id="desc">A concise flow where auto memory and auto resource both create daily cards, which are indexed in the daily workbench and then used by dream and search.</desc>
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<defs>
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<style>
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.bg { fill: #f7f8fb; }
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@ -29,67 +29,52 @@
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<text class="title" x="600" y="54" text-anchor="middle">Auto Memory & Auto Resource</text>
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<text class="subtitle" x="600" y="80" text-anchor="middle">Conversations and dated resources become readable daily cards, then share one daily index and downstream memory flow.</text>
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<text class="subtitle" x="600" y="80" text-anchor="middle">Two ingestion paths create daily cards, then share one daily index and downstream memory flow.</text>
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
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|
||||
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|
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|
||||
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||||
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||||
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@ -101,6 +86,6 @@
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<text class="chip-title" x="838" y="532">Daily output</text>
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||||
<text class="chip-text" x="838" y="554">daily cards plus daily/<date>.md</text>
|
||||
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||||
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|
||||
<text class="note" x="310" y="474" text-anchor="middle">both flows preserve original sources for verification</text>
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||||
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|
||||
<text class="note" x="782" y="478" text-anchor="middle">downstream flows read the same daily workbench</text>
|
||||
</svg>
|
||||
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|
|
|||
|
Before Width: | Height: | Size: 7.2 KiB After Width: | Height: | Size: 6.2 KiB |
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@ -1,691 +0,0 @@
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|||
<p align="center">
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<img src="docs/_static/figure/reme_logo.png" alt="ReMe 标志" width="50%">
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||||
</p>
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||||
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||||
<p align="center">
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||||
<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/python-3.10+-blue" alt="Python Version"></a>
|
||||
<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/pypi/v/reme-ai.svg?logo=pypi" alt="PyPI Version"></a>
|
||||
<a href="https://pepy.tech/project/reme-ai/"><img src="https://img.shields.io/pypi/dm/reme-ai" alt="PyPI Downloads"></a>
|
||||
<a href="https://github.com/agentscope-ai/ReMe"><img src="https://img.shields.io/github/commit-activity/m/agentscope-ai/ReMe?style=flat-square" alt="GitHub commit activity"></a>
|
||||
</p>
|
||||
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||||
<p align="center">
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||||
<a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-black" alt="License"></a>
|
||||
<a href="./README.md"><img src="https://img.shields.io/badge/English-Click-yellow" alt="English"></a>
|
||||
<a href="./README_ZH.md"><img src="https://img.shields.io/badge/简体中文-点击查看-orange" alt="简体中文"></a>
|
||||
<a href="https://github.com/agentscope-ai/ReMe"><img src="https://img.shields.io/github/stars/agentscope-ai/ReMe?style=social" alt="GitHub Stars"></a>
|
||||
<a href="https://deepwiki.com/agentscope-ai/ReMe"><img src="https://img.shields.io/badge/DeepWiki-Ask_Devin-navy.svg" alt="DeepWiki"></a>
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<a href="https://trendshift.io/repositories/20528" target="_blank"><img src="https://trendshift.io/api/badge/repositories/20528" alt="agentscope-ai%2FReMe | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<strong>面向智能体的记忆管理工具包，Remember Me, Refine Me.</strong><br>
|
||||
</p>
|
||||
|
||||
> 老版本请参阅 [0.2.x 版本文档](docs/README_0_2_x_ZH.md)
|
||||
|
||||
---
|
||||
|
||||
## 📰 最新文章
|
||||
|
||||
| 日期 | 标题 |
|
||||
|------------|----------------------------------------------------|
|
||||
| 2026-03-30 | [CoPaw 上下文管理设计解析](docs/copaw_context_design_zh.md) |
|
||||
|
||||
---
|
||||
|
||||
🧠 ReMe 是一个专为 **AI 智能体** 打造的记忆管理框架，同时提供基于[文件系统](#-基于文件的记忆系统-remelight)
|
||||
和基于[向量库](#-基于向量库的记忆系统)的记忆系统。
|
||||
|
||||
它解决智能体记忆的两类核心问题：**上下文窗口有限**（长对话时早期信息被截断或丢失）、**会话无状态**（新对话无法继承历史，每次从零开始）。
|
||||
|
||||
ReMe 让智能体拥有**真正的记忆力**——旧对话自动浓缩，重要信息持久保存，下次对话自动想起来。
|
||||
|
||||
在 LoCoMo 与 HaluMem 基准测试中，ReMe 取得了领先结果，详见[实验效果](#实验效果)。
|
||||
|
||||
<details>
|
||||
<summary><b>你可以用 ReMe 做什么</b></summary>
|
||||
|
||||
<br>
|
||||
|
||||
- **个人助理**：为 [CoPaw](https://github.com/agentscope-ai/CoPaw) 等智能体提供长期记忆，记住用户偏好和历史对话。
|
||||
- **编程助手**：记录代码风格偏好、项目上下文，跨会话保持一致的开发体验。
|
||||
- **客服机器人**：记录用户问题历史、偏好设置，提供个性化服务。
|
||||
- **任务自动化**：从历史任务中学习成功/失败模式，持续优化执行策略。
|
||||
- **知识问答**：构建可检索的知识库，支持语义搜索和精确匹配。
|
||||
- **多轮对话**：自动压缩长对话，在有限上下文窗口内保留关键信息。
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
|
||||
## 📁 基于文件的记忆系统 (ReMeLight)
|
||||
|
||||
> 记忆即文件，文件即记忆
|
||||
|
||||
将**记忆视为文件**——可读、可编辑、可复制。
|
||||
[CoPaw](https://github.com/agentscope-ai/CoPaw) 通过继承 `ReMeLight` 实现了长期记忆和上下文的管理。
|
||||
|
||||
| 传统记忆系统 | File Based ReMe |
|
||||
|-----------|-----------------|
|
||||
| 🗄️ 数据库存储 | 📝 Markdown 文件 |
|
||||
| 🔒 不可见 | 👀 随时可读 |
|
||||
| ❌ 难修改 | ✏️ 直接编辑 |
|
||||
| 🚫 难迁移 | 📦 复制即迁移 |
|
||||
|
||||
```
|
||||
working_dir/
|
||||
├── MEMORY.md # 长期记忆：用户偏好等持久信息
|
||||
├── memory/
|
||||
│ └── YYYY-MM-DD.md # 每日日记：对话结束后自动写入
|
||||
├── dialog/ # 原始对话记录：压缩前的完整对话
|
||||
│ └── YYYY-MM-DD.jsonl # 按日期存储的对话消息（JSONL 格式）
|
||||
└── tool_result/ # 超长工具输出缓存（自动管理，超期自动清理）
|
||||
└── <uuid>.txt
|
||||
```
|
||||
|
||||
### 核心能力
|
||||
|
||||
[ReMeLight](reme/reme_light.py) 是该记忆系统的核心类，为 AI Agent 提供完整的记忆管理能力：
|
||||
|
||||
<table>
|
||||
<tr><th>类别</th><th>方法</th><th>功能</th><th>关键组件</th></tr>
|
||||
<tr><td rowspan="4">上下文管理</td><td><code>check_context</code></td><td>📊 检查上下文大小</td><td><a href="reme/memory/file_based/components/context_checker.py">ContextChecker</a> — 检查上下文是否超出阈值并拆分 Message</td></tr>
|
||||
<tr><td><code>compact_memory</code></td><td>📦 压缩历史对话为摘要</td><td><a href="reme/memory/file_based/components/compactor.py">Compactor</a> — ReActAgent 生成结构化上下文摘要</td></tr>
|
||||
<tr><td><code>compact_tool_result</code></td><td>✂️ 压缩超长工具输出</td><td><a href="reme/memory/file_based/components/tool_result_compactor.py">ToolResultCompactor</a> — 截断超长的工具调用结果并转存到 <code>tool_result/</code>，消息中保留文件引用</td></tr>
|
||||
<tr><td><code>pre_reasoning_hook</code></td><td>🔄 推理前预处理钩子</td><td>compact_tool_result + check_context + compact_memory + summary_memory(async)</td></tr>
|
||||
<tr><td rowspan="2">长期记忆</td><td><code>summary_memory</code></td><td>📝 将重要记忆写入文件</td><td><a href="reme/memory/file_based/components/summarizer.py">Summarizer</a> — ReActAgent + 文件工具（read / write / edit）</td></tr>
|
||||
<tr><td><code>memory_search</code></td><td>🔍 语义搜索记忆</td><td><a href="reme/memory/file_based/tools/memory_search.py">MemorySearch</a> — 向量 + BM25 混合检索</td></tr>
|
||||
<tr><td rowspan="2">会话内存</td><td><code>get_in_memory_memory</code></td><td>💾 创建会话内存实例</td><td>返回 ReMeInMemoryMemory，自动配置 dialog_path 实现对话持久化</td></tr>
|
||||
<tr><td><code>await_summary_tasks</code></td><td>⏳ 等待异步摘要任务</td><td>阻塞等待所有后台摘要任务完成</td></tr>
|
||||
<tr><td>-</td><td><code>start</code></td><td>🚀 启动记忆系统</td><td>初始化文件存储、文件监控、Embedding 缓存；清理过期工具结果文件</td></tr>
|
||||
<tr><td>-</td><td><code>close</code></td><td>📕 关闭并清理</td><td>清理工具结果文件、停止文件监控、保存 Embedding 缓存</td></tr>
|
||||
</table>
|
||||
|
||||
---
|
||||
|
||||
### 🚀 快速开始
|
||||
|
||||
#### 安装
|
||||
|
||||
**从源码安装：**
|
||||
|
||||
```bash
|
||||
git clone https://github.com/agentscope-ai/ReMe.git
|
||||
cd ReMe
|
||||
pip install -e ".[light]"
|
||||
```
|
||||
|
||||
**更新到最新版本：**
|
||||
|
||||
```bash
|
||||
git pull
|
||||
pip install -e ".[light]"
|
||||
```
|
||||
|
||||
#### 环境变量
|
||||
|
||||
`ReMeLight` 环境变量配置 Embedding 和存储后端
|
||||
|
||||
| Variable | Description | Example |
|
||||
|----------------------|-------------------------|-----------------------------------------------------|
|
||||
| `LLM_API_KEY` | LLM API key | `sk-xxx` |
|
||||
| `LLM_BASE_URL` | LLM base URL | `https://dashscope.aliyuncs.com/compatible-mode/v1` |
|
||||
| `EMBEDDING_API_KEY` | Embedding API key (可选) | `sk-xxx` |
|
||||
| `EMBEDDING_BASE_URL` | Embedding base URL (可选) | `https://dashscope.aliyuncs.com/compatible-mode/v1` |
|
||||
|
||||
#### Python 使用
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
|
||||
from reme.reme_light import ReMeLight
|
||||
|
||||
|
||||
async def main():
|
||||
# 初始化 ReMeLight
|
||||
reme = ReMeLight(
|
||||
default_as_llm_config={"model_name": "qwen3.5-35b-a3b"},
|
||||
# default_embedding_model_config={"model_name": "text-embedding-v4"},
|
||||
default_file_store_config={"fts_enabled": True, "vector_enabled": False},
|
||||
enable_load_env=True,
|
||||
)
|
||||
await reme.start()
|
||||
|
||||
messages = [...] # 对话消息列表
|
||||
|
||||
# 1. 检查上下文大小（Token 计数，判断是否需要压缩）
|
||||
messages_to_compact, messages_to_keep, is_valid = await reme.check_context(
|
||||
messages=messages,
|
||||
memory_compact_threshold=90000, # 触发压缩的阈值（tokens）
|
||||
memory_compact_reserve=10000, # 保留的近期消息 token 数
|
||||
)
|
||||
|
||||
# 2. 将历史对话压缩为结构化摘要（可传入上轮摘要，实现增量更新）
|
||||
summary = await reme.compact_memory(
|
||||
messages=messages,
|
||||
previous_summary="",
|
||||
max_input_length=128000, # 模型上下文窗口（tokens）
|
||||
compact_ratio=0.7, # 达到 max_input_length * 0.7 时触发压缩
|
||||
language="zh", # 摘要语言（zh / ""）
|
||||
)
|
||||
|
||||
# 3. 压缩超长工具输出（防止工具结果撑爆上下文）
|
||||
messages = await reme.compact_tool_result(messages)
|
||||
|
||||
# 4. 推理前预处理钩子（自动压缩工具结果 + 检查上下文 + 生成摘要）
|
||||
processed_messages, compressed_summary = await reme.pre_reasoning_hook(
|
||||
messages=messages,
|
||||
system_prompt="你是一个有帮助的 AI 助手。",
|
||||
compressed_summary="",
|
||||
max_input_length=128000,
|
||||
compact_ratio=0.7,
|
||||
memory_compact_reserve=10000,
|
||||
enable_tool_result_compact=True,
|
||||
tool_result_compact_keep_n=3,
|
||||
)
|
||||
|
||||
# 5. 将重要记忆写入文件（摘要写入 memory/YYYY-MM-DD.md）
|
||||
summary_result = await reme.summary_memory(
|
||||
messages=messages,
|
||||
language="zh",
|
||||
)
|
||||
|
||||
# 6. 语义搜索记忆（向量 + BM25 混合检索）
|
||||
result = await reme.memory_search(query="Python 版本偏好", max_results=5)
|
||||
|
||||
# 7. 创建会话内存实例（管理单次对话的上下文）
|
||||
from reme.memory.file_based.reme_in_memory_memory import ReMeInMemoryMemory
|
||||
memory = reme.get_in_memory_memory() # 自动配置 dialog_path
|
||||
for msg in messages:
|
||||
await memory.add(msg)
|
||||
token_stats = await memory.estimate_tokens(max_input_length=128000)
|
||||
print(f"当前上下文使用率: {token_stats['context_usage_ratio']:.1f}%")
|
||||
print(f"消息 Token 数: {token_stats['messages_tokens']}")
|
||||
print(f"预估总 Token 数: {token_stats['estimated_tokens']}")
|
||||
|
||||
# 8. 标记消息为压缩状态（自动持久化到 dialog/YYYY-MM-DD.jsonl）
|
||||
# await memory.mark_messages_compressed(messages_to_compact)
|
||||
|
||||
# 关闭 ReMeLight
|
||||
await reme.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
> 📂 完整示例代码：[test_reme_light.py](tests/light/test_reme_light.py)
|
||||
> 📋 运行结果示例：[test_reme_light_log.txt](tests/light/test_reme_light_log.txt)（223,838 tokens → 1,105 tokens，压缩率99.5%）
|
||||
|
||||
### 基于文件的 ReMeLight 记忆系统架构
|
||||
|
||||
#### 上下文数据结构
|
||||
|
||||
```mermaid
|
||||
flowchart TD
|
||||
A[Context] --> B[compact_summary]
|
||||
B --> C[dialog 路径引导 + Goal/Constraints/Progress/KeyDecisions/NextSteps]
|
||||
A --> E[messages: 完整对话历史]
|
||||
A --> F[文件系统缓存]
|
||||
F --> G[dialog/YYYY-MM-DD.jsonl]
|
||||
F --> H[tool_result/uuid.txt N天TTL]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
[CoPaw MemoryManager](https://github.com/agentscope-ai/CoPaw/blob/main/src/copaw/agents/memory/reme_light_memory_manager.py)
|
||||
继承
|
||||
`ReMeLight`，将记忆能力集成到 Agent 推理流程中：
|
||||
|
||||
```mermaid
|
||||
graph LR
|
||||
Agent[Agent] -->|每轮推理前| Hook[pre_reasoning_hook]
|
||||
Hook --> TC[compact_tool_result<br>压缩工具输出]
|
||||
TC --> CC[check_context<br>Token 计数]
|
||||
CC -->|超限| CM[compact_memory<br>生成摘要]
|
||||
CC -->|超限| SM[summary_memory<br>异步持久化]
|
||||
SM -->|ReAct + FileIO| Files[memory/*.md]
|
||||
CC -->|超限| MMC[mark_messages_compressed<br>持久化原始对话]
|
||||
MMC --> Dialog[dialog/*.jsonl]
|
||||
Agent -->|主动调用| Search[memory_search<br>向量+BM25]
|
||||
Agent -->|会话内存| InMem[ReMeInMemoryMemory<br>Token感知内存]
|
||||
InMem -->|压缩/清空| Dialog
|
||||
Files -.->|FileWatcher| Store[(FileStore<br>向量+FTS索引)]
|
||||
Search --> Store
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
#### 1. check_context — 上下文检查
|
||||
|
||||
[ContextChecker](reme/memory/file_based/components/context_checker.py) 基于 Token 计数判断上下文是否超限，自动拆分为「待压缩」和「保留」两组消息。
|
||||
|
||||
```mermaid
|
||||
graph LR
|
||||
M[messages] --> H[AsMsgHandler<br>Token 计数]
|
||||
H --> C{total > threshold?}
|
||||
C -->|否| K[返回全部消息]
|
||||
C -->|是| S[从尾部向前保留<br>reserve tokens]
|
||||
S --> CP[messages_to_compact<br>早期消息]
|
||||
S --> KP[messages_to_keep<br>近期消息]
|
||||
S --> V{is_valid<br>工具调用对齐?}
|
||||
```
|
||||
|
||||
- **核心逻辑**：从尾部向前保留 `reserve` tokens，超出部分标记为待压缩
|
||||
- **完整性保证**：不拆分 user-assistant 对话对，不拆分 tool_use/tool_result 配对
|
||||
|
||||
---
|
||||
|
||||
#### 2. compact_memory — 对话压缩
|
||||
|
||||
[Compactor](reme/memory/file_based/components/compactor.py) 使用 ReActAgent 将历史对话压缩为**结构化上下文摘要**。
|
||||
|
||||
```mermaid
|
||||
graph LR
|
||||
M[messages] --> H[AsMsgHandler<br>format_msgs_to_str]
|
||||
H --> A[ReActAgent<br>reme_compactor]
|
||||
P[previous_summary] -->|增量更新| A
|
||||
A --> S[结构化摘要<br>Goal/Progress/Decisions...]
|
||||
```
|
||||
|
||||
**摘要结构**（上下文检查点）：
|
||||
|
||||
| 字段 | 说明 |
|
||||
|-----------------------|--------------------|
|
||||
| `## Goal` | 用户目标 |
|
||||
| `## Constraints` | 约束和偏好 |
|
||||
| `## Progress` | 任务进展 |
|
||||
| `## Key Decisions` | 关键决策 |
|
||||
| `## Next Steps` | 下一步计划 |
|
||||
| `## Critical Context` | 文件路径、函数名、错误信息等关键数据 |
|
||||
|
||||
- **增量更新**：传入 `previous_summary` 时，自动将新对话与旧摘要合并
|
||||
- **思考增强**：`add_thinking_block=True`（默认）时，在生成摘要前加入思考步骤，提升摘要质量
|
||||
|
||||
---
|
||||
|
||||
#### 3. summary_memory — 记忆持久化
|
||||
|
||||
[Summarizer](reme/memory/file_based/components/summarizer.py) 采用 **ReAct + 文件工具** 模式，让 AI 自主决定写什么、写到哪。
|
||||
|
||||
```mermaid
|
||||
graph LR
|
||||
M[messages] --> A[ReActAgent<br>reme_summarizer]
|
||||
A -->|read| R[读取 memory/YYYY-MM-DD.md]
|
||||
R --> T{思考: 如何合并?}
|
||||
T -->|write| W[覆盖写入]
|
||||
T -->|edit| E[精确替换]
|
||||
W --> F[memory/YYYY-MM-DD.md]
|
||||
E --> F
|
||||
```
|
||||
|
||||
**文件工具**（[FileIO](reme/memory/file_based/tools/file_io.py)）：
|
||||
|
||||
| 工具 | 功能 |
|
||||
|---------|---------|
|
||||
| `read` | 读取文件内容 |
|
||||
| `write` | 覆盖写入文件 |
|
||||
| `edit` | 精确匹配后替换 |
|
||||
|
||||
---
|
||||
|
||||
#### 4. compact_tool_result — 工具结果压缩
|
||||
|
||||
[ToolResultCompactor](reme/memory/file_based/components/tool_result_compactor.py) 解决工具输出过长导致上下文膨胀的问题。根据消息是否在
|
||||
`recent_n` 范围内，采用不同的截断策略：
|
||||
|
||||
```mermaid
|
||||
graph LR
|
||||
M[messages] --> B{属于 recent_n 范围?}
|
||||
B -->|是 近期消息| C[低截断 recent_max_bytes=100KB<br>完整内容写入 tool_result/uuid.txt<br>消息追加: 从第N行开始读]
|
||||
B -->|否 历史消息| D[高截断 old_max_bytes=3KB<br>引用已有文件路径<br>更激进截断]
|
||||
C --> E[cleanup_expired_files<br>清理过期文件]
|
||||
D --> E
|
||||
```
|
||||
|
||||
| 参数 | 默认值 | 说明 |
|
||||
|--------------------|---------------------|----------------------------------------------|
|
||||
| `recent_n` | `1` | 末尾连续工具结果消息的最小数量，视为"近期"，使用低截断阈值 |
|
||||
| `recent_max_bytes` | `100 * 1024`（100KB） | 近期消息的截断阈值；超出部分转存到 `tool_result/` 并附注文件路径和起始行 |
|
||||
| `old_max_bytes` | `3000`（3KB） | 历史消息的截断阈值，截断更激进 |
|
||||
| `retention_days` | `3` | 工具结果文件的保留天数，过期自动清理 |
|
||||
|
||||
- **自动清理**：过期文件（超过 `retention_days`）在 `start`/`close`/`compact_tool_result` 时自动删除
|
||||
|
||||
---
|
||||
|
||||
#### 5. memory_search — 记忆检索
|
||||
|
||||
[MemorySearch](reme/memory/file_based/tools/memory_search.py) 提供**向量 + BM25 混合检索**能力。
|
||||
|
||||
```mermaid
|
||||
graph LR
|
||||
Q[query] --> E[Embedding<br>向量化]
|
||||
E --> V[vector_search<br>语义相似]
|
||||
Q --> B[BM25<br>关键词匹配]
|
||||
V -->|" weight: 0.7 "| M[去重 + 加权融合]
|
||||
B -->|" weight: 0.3 "| M
|
||||
M --> F[min_score 过滤]
|
||||
F --> R[Top-N 结果]
|
||||
```
|
||||
|
||||
- **融合机制**：向量权重 0.7 + BM25 权重 0.3，兼顾语义相似和精确匹配
|
||||
|
||||
---
|
||||
|
||||
#### 6. ReMeInMemoryMemory — 会话内存
|
||||
|
||||
[ReMeInMemoryMemory](reme/memory/file_based/reme_in_memory_memory.py) 扩展 AgentScope 的 `InMemoryMemory`，提供 Token
|
||||
感知的内存管理和原始对话持久化能力。
|
||||
|
||||
```mermaid
|
||||
graph LR
|
||||
C[content] --> G[get_memory<br>exclude_mark=COMPRESSED]
|
||||
G --> F[排除已压缩消息]
|
||||
F --> P{prepend_summary?}
|
||||
P -->|是| S[头部插入 previous-summary]
|
||||
S --> O[输出 messages]
|
||||
P -->|否| O
|
||||
M[mark_messages_compressed] --> D[持久化到 dialog/YYYY-MM-DD.jsonl]
|
||||
D --> R[从内存移除]
|
||||
```
|
||||
|
||||
| 功能 | 说明 |
|
||||
|----------------------------------|-----------------------|
|
||||
| `get_memory` | 按标记过滤，自动追加压缩摘要 |
|
||||
| `estimate_tokens` | 估算上下文 Token 用量 |
|
||||
| `state_dict` / `load_state_dict` | 状态序列化/反序列化（会话持久化） |
|
||||
| `mark_messages_compressed` | 标记消息压缩并持久化到 dialog 目录 |
|
||||
| `clear_content` | 持久化所有消息后清空内存 |
|
||||
|
||||
**原始对话持久化**：当消息被压缩或清空时，自动保存到 `{dialog_path}/{date}.jsonl`，每行一条 JSON 格式的消息记录。
|
||||
|
||||
---
|
||||
|
||||
#### 7. pre_reasoning_hook — 推理前预处理
|
||||
|
||||
整合上述组件的统一入口，在每轮推理前自动管理上下文。
|
||||
|
||||
```mermaid
|
||||
graph LR
|
||||
M[messages] --> TC[compact_tool_result<br>压缩超长工具输出]
|
||||
TC --> CC[check_context<br>计算剩余空间]
|
||||
CC --> D{messages_to_compact<br>非空?}
|
||||
D -->|否| K[返回原消息 + 原摘要]
|
||||
D -->|是| V{is_valid?}
|
||||
V -->|否| K
|
||||
V -->|是| CM[compact_memory<br>同步生成摘要]
|
||||
V -->|是| SM[add_async_summary_task<br>异步持久化]
|
||||
CM --> R[返回 messages_to_keep + 新摘要]
|
||||
```
|
||||
|
||||
**执行流程**：
|
||||
|
||||
1. `compact_tool_result` — 对除最近 `tool_result_compact_keep_n` 条消息之外的历史消息压缩超长工具输出
|
||||
2. `check_context` — 检查上下文是否超限（扣除 system_prompt 和 compressed_summary 的 token 后计算剩余空间）
|
||||
3. `compact_memory` — 生成压缩摘要（同步），结果追加到 `compact_summary`
|
||||
4. `summary_memory` — 持久化记忆到 `memory/*.md`（异步后台，不阻塞推理）
|
||||
|
||||
| 关键参数 | 默认值 | 说明 |
|
||||
|------------------------------|---------|--------------------------------------------------|
|
||||
| `tool_result_compact_keep_n` | `3` | 最近 N 条消息跳过工具结果压缩（保留完整内容） |
|
||||
| `memory_compact_reserve` | `10000` | 保留近期消息的 token 数，超出部分触发压缩 |
|
||||
| `compact_ratio` | `0.7` | 压缩阈值比例：`max_input_length × compact_ratio × 0.95` |
|
||||
|
||||
---
|
||||
|
||||
## 🗃️ 基于向量库的记忆系统
|
||||
|
||||
[ReMe Vector Based](reme/reme.py) 是基于向量库的记忆系统核心类，支持三种记忆类型的统一管理：
|
||||
|
||||
| 记忆类型 | 用途 |
|
||||
|--------------|------------------|
|
||||
| **个人记忆** | 记录用户偏好、习惯 |
|
||||
| **任务/程序性记忆** | 记录任务执行经验、成功/失败模式 |
|
||||
| **工具记忆** | 记录工具使用经验、参数优化 |
|
||||
|
||||
### 核心能力
|
||||
|
||||
| 方法 | 功能 | 说明 |
|
||||
|--------------------|----------|----------------|
|
||||
| `summarize_memory` | 🧠 记忆总结 | 从对话中自动提取并存储记忆 |
|
||||
| `retrieve_memory` | 🔍 记忆检索 | 根据查询检索相关记忆 |
|
||||
| `add_memory` | ➕ 添加记忆 | 手动添加记忆到向量库 |
|
||||
| `get_memory` | 📖 获取记忆 | 通过 ID 获取单条记忆 |
|
||||
| `update_memory` | ✏️ 更新记忆 | 更新已有记忆的内容或元数据 |
|
||||
| `delete_memory` | 🗑️ 删除记忆 | 删除指定记忆 |
|
||||
| `list_memory` | 📋 列出记忆 | 列出某类记忆，支持过滤和排序 |
|
||||
|
||||
### 安装与环境变量
|
||||
|
||||
安装和环境变量配置与 [ReMeLight 一致](#安装)，通过环境变量设置 API 密钥，可写在项目根目录的 `.env` 文件中。
|
||||
|
||||
### Python 使用
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
|
||||
from reme import ReMe
|
||||
|
||||
|
||||
async def main():
|
||||
# 初始化 ReMe
|
||||
reme = ReMe(
|
||||
working_dir=".reme",
|
||||
default_llm_config={
|
||||
"backend": "openai",
|
||||
"model_name": "qwen3.5-plus",
|
||||
},
|
||||
default_embedding_model_config={
|
||||
"backend": "openai",
|
||||
"model_name": "text-embedding-v4",
|
||||
"dimensions": 1024,
|
||||
},
|
||||
default_vector_store_config={
|
||||
"backend": "local", # 支持 local/chroma/qdrant/elasticsearch/obvec/zvec
|
||||
},
|
||||
)
|
||||
await reme.start()
|
||||
|
||||
messages = [
|
||||
{"role": "user", "content": "帮我写一个 Python 脚本", "time_created": "2026-02-28 10:00:00"},
|
||||
{"role": "assistant", "content": "好的，我来帮你写", "time_created": "2026-02-28 10:00:05"},
|
||||
]
|
||||
|
||||
# 1. 从对话中总结记忆（自动提取用户偏好、任务经验等）
|
||||
result = await reme.summarize_memory(
|
||||
messages=messages,
|
||||
user_name="alice", # 个人记忆
|
||||
# task_name="code_writing", # 任务记忆
|
||||
)
|
||||
print(f"总结结果: {result}")
|
||||
|
||||
# 2. 检索相关记忆
|
||||
memories = await reme.retrieve_memory(
|
||||
query="Python 编程",
|
||||
user_name="alice",
|
||||
# task_name="code_writing",
|
||||
)
|
||||
print(f"检索结果: {memories}")
|
||||
|
||||
# 3. 手动添加记忆
|
||||
memory_node = await reme.add_memory(
|
||||
memory_content="用户喜欢简洁的代码风格",
|
||||
user_name="alice",
|
||||
)
|
||||
print(f"添加的记忆: {memory_node}")
|
||||
memory_id = memory_node.memory_id
|
||||
|
||||
# 4. 通过 ID 获取单条记忆
|
||||
fetched_memory = await reme.get_memory(memory_id=memory_id)
|
||||
print(f"获取的记忆: {fetched_memory}")
|
||||
|
||||
# 5. 更新记忆内容
|
||||
updated_memory = await reme.update_memory(
|
||||
memory_id=memory_id,
|
||||
user_name="alice",
|
||||
memory_content="用户喜欢简洁且带注释的代码风格",
|
||||
)
|
||||
print(f"更新后的记忆: {updated_memory}")
|
||||
|
||||
# 6. 列出用户的所有记忆（支持过滤和排序）
|
||||
all_memories = await reme.list_memory(
|
||||
user_name="alice",
|
||||
limit=10,
|
||||
sort_key="time_created",
|
||||
reverse=True,
|
||||
)
|
||||
print(f"用户记忆列表: {all_memories}")
|
||||
|
||||
# 7. 删除指定记忆
|
||||
await reme.delete_memory(memory_id=memory_id)
|
||||
print(f"已删除记忆: {memory_id}")
|
||||
|
||||
# 8. 删除所有记忆（谨慎使用）
|
||||
# await reme.delete_all()
|
||||
|
||||
await reme.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
### 技术架构
|
||||
|
||||
```mermaid
|
||||
graph LR
|
||||
User[用户 / Agent] --> ReMe[Vector Based ReMe]
|
||||
ReMe --> Summarize[记忆总结]
|
||||
ReMe --> Retrieve[记忆检索]
|
||||
ReMe --> CRUD[增删改查]
|
||||
Summarize --> PersonalSum[PersonalSummarizer]
|
||||
Summarize --> ProceduralSum[ProceduralSummarizer]
|
||||
Summarize --> ToolSum[ToolSummarizer]
|
||||
Retrieve --> PersonalRet[PersonalRetriever]
|
||||
Retrieve --> ProceduralRet[ProceduralRetriever]
|
||||
Retrieve --> ToolRet[ToolRetriever]
|
||||
PersonalSum --> VectorStore[向量数据库]
|
||||
ProceduralSum --> VectorStore
|
||||
ToolSum --> VectorStore
|
||||
PersonalRet --> VectorStore
|
||||
ProceduralRet --> VectorStore
|
||||
ToolRet --> VectorStore
|
||||
```
|
||||
|
||||
### 实验效果
|
||||
|
||||
本实验部分在 LoCoMo和HaluMem 两个数据集上进行评测，实验设置如下：
|
||||
|
||||
1. **ReMe 使用模型**：如各表 backbone 列所示。
|
||||
2. **评估使用模型**：采用 LLM-as-a-Judge 协议（参照 MemOS）——每条回答由 GPT-4o-mini 裁判模型打分。
|
||||
|
||||
实验设置尽量与各基线论文保持一致，以复用其公开结果。
|
||||
|
||||
### LoCoMo
|
||||
|
||||
| Method | Single Hop | Multi Hop | Temporal | Open Domain | Overall |
|
||||
|----------|------------|-----------|-----------|-------------|-----------|
|
||||
| MemoryOS | 62.43 | 56.50 | 37.18 | 40.28 | 54.70 |
|
||||
| Mem0 | 66.71 | 58.16 | 55.45 | 40.62 | 61.00 |
|
||||
| MemU | 72.77 | 62.41 | 33.96 | 46.88 | 61.15 |
|
||||
| MemOS | 81.45 | 69.15 | 72.27 | 60.42 | 75.87 |
|
||||
| HiMem | 89.22 | 70.92 | 74.77 | 54.86 | 80.71 |
|
||||
| Zep | 88.11 | 71.99 | 74.45 | 66.67 | 81.06 |
|
||||
| TiMem | 81.43 | 62.20 | 77.63 | 52.08 | 75.30 |
|
||||
| TSM | 84.30 | 66.67 | 71.03 | 58.33 | 76.69 |
|
||||
| MemR3 | 89.44 | 71.39 | 76.22 | 61.11 | 81.55 |
|
||||
| **ReMe** | **89.89** | **82.98** | **83.80** | **71.88** | **86.23** |
|
||||
|
||||
### HaluMem
|
||||
|
||||
| Method | Memory Integrity | Memory Accuracy | QA Accuracy |
|
||||
|-------------|------------------|-----------------|-------------|
|
||||
| MemoBase | 14.55 | 92.24 | 35.53 |
|
||||
| Supermemory | 41.53 | 90.32 | 54.07 |
|
||||
| Mem0 | 42.91 | 86.26 | 53.02 |
|
||||
| ProMem | **73.80** | 89.47 | 62.26 |
|
||||
| **ReMe** | 67.72 | **94.06** | **88.78** |
|
||||
|
||||
---
|
||||
|
||||
## 🧪 程序化记忆论文
|
||||
|
||||
> 我们的程序性（任务）记忆论文已在 [arXiv](https://arxiv.org/abs/2512.10696) 发布
|
||||
|
||||
### 🌍 [Appworld 实验](benchmark/appworld/quickstart.md)
|
||||
|
||||
我们在 Appworld 环境上使用 Qwen3-8B（非思考模式）进行评测：
|
||||
|
||||
| 方法 | Avg@4 | Pass@4 |
|
||||
|---------|---------------------|---------------------|
|
||||
| 无 ReMe | 0.1497 | 0.3285 |
|
||||
| 使用 ReMe | 0.1706 **(+2.09%)** | 0.3631 **(+3.46%)** |
|
||||
|
||||
Pass@K 衡量在生成 K 个候选中，至少一个成功完成任务（score=1）的概率。
|
||||
当前实验使用的是内部 AppWorld 环境，可能与对外版本存在轻微差异。
|
||||
|
||||
关于如何复现实验的更多细节，见 [quickstart.md](benchmark/appworld/quickstart.md)
|
||||
|
||||
### 🔧 [BFCL-V3 实验](benchmark/bfcl/quickstart.md)
|
||||
|
||||
我们在 BFCL-V3 multi-turn-base 任务（随机划分 50 train / 150 val）上，使用 Qwen3-8B（思考模式）进行评测：
|
||||
|
||||
| 方法 | Avg@4 | Pass@4 |
|
||||
|---------|---------------------|---------------------|
|
||||
| 无 ReMe | 0.4033 | 0.5955 |
|
||||
| 使用 ReMe | 0.4450 **(+4.17%)** | 0.6577 **(+6.22%)** |
|
||||
|
||||
关于如何复现实验的更多细节，见 [quickstart.md](benchmark/bfcl/quickstart.md)
|
||||
|
||||
## ⭐ 社区与支持
|
||||
|
||||
- **Star 与 Watch**：Star 可让更多智能体开发者发现 ReMe；Watch 可助你第一时间获知新版本与特性。
|
||||
- **分享你的成果**：在 Issue 或 Discussion 中分享 ReMe 为你的智能体解锁了什么——我们非常乐意展示社区的优秀案例。
|
||||
- **需要新功能？** 提交 Feature Request，我们将与社区一起完善。
|
||||
- **代码贡献**：欢迎任何形式的代码贡献，请参阅 [贡献指南](docs/contribution.md)。
|
||||
- **致谢**：感谢 OpenClaw、Mem0、MemU、CoPaw 等优秀的开源项目，为项目带来诸多启发与帮助。
|
||||
|
||||
### 贡献者
|
||||
|
||||
感谢所有为 ReMe 做出贡献的朋友们：
|
||||
|
||||
<a href="https://github.com/agentscope-ai/ReMe/graphs/contributors">
|
||||
<img src="https://contrib.rocks/image?repo=agentscope-ai/ReMe" alt="贡献者" />
|
||||
</a>
|
||||
|
||||
---
|
||||
|
||||
## 📄 引用
|
||||
|
||||
```bibtex
|
||||
@software{AgentscopeReMe2025,
|
||||
title = {AgentscopeReMe: Memory Management Kit for Agents},
|
||||
author = {ReMe Team},
|
||||
url = {https://reme.agentscope.io},
|
||||
year = {2025}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ⚖️ 许可证
|
||||
|
||||
本项目基于 Apache License 2.0 开源，详情参见 [LICENSE](./LICENSE) 文件。
|
||||
|
||||
---
|
||||
|
||||
## 🤔 为什么叫 ReMe？
|
||||
|
||||
ReMe 是 **Remember Me** 和 **Refine Me** 的缩写，寓意让 AI 智能体「记住我」并在交互中「精进自我」。我们希望 ReMe
|
||||
不只是一个冷冰冰的记忆模块，而是能让智能体真正理解用户、积累经验、持续进化的伙伴。
|
||||
|
||||
---
|
||||
|
||||
## 📈 Star 历史
|
||||
|
||||
[](https://www.star-history.com/#agentscope-ai/ReMe&Date)
|
||||
|
|
@ -52,6 +52,117 @@ jobs:
|
|||
file_catalog: digest
|
||||
- backend: log_changes_step
|
||||
|
||||
dream_cron:
|
||||
backend: cron
|
||||
cron: "0 23 * * *"
|
||||
steps:
|
||||
- backend: dream_extract_step
|
||||
file_catalog: dream
|
||||
topic_session_id: interests
|
||||
- backend: dream_integrate_step
|
||||
- backend: dream_topics_step
|
||||
topic_count: 3
|
||||
topic_diversity_days: 7
|
||||
- backend: dream_finish_step
|
||||
file_catalog: dream
|
||||
|
||||
auto_dream:
|
||||
backend: base
|
||||
description: "Auto-dream: scan today's day-index and daily notes, globally extract merged units/topics, integrate digest units, write interests.yaml, and persist the dream catalog."
|
||||
parameters:
|
||||
type: object
|
||||
properties:
|
||||
date:
|
||||
type: string
|
||||
description: "YYYY-MM-DD to scan; defaults to today in the dreamer's timezone"
|
||||
default: ""
|
||||
hint:
|
||||
type: string
|
||||
description: "caller guidance passed through to dream extract/integrate"
|
||||
default: ""
|
||||
topic_count:
|
||||
type: integer
|
||||
description: "maximum number of final daily interest topics"
|
||||
default: 3
|
||||
topic_diversity_days:
|
||||
type: integer
|
||||
description: "number of previous interests.yaml days to avoid repeating"
|
||||
default: 7
|
||||
steps:
|
||||
- backend: dream_extract_step
|
||||
file_catalog: dream
|
||||
topic_session_id: interests
|
||||
- backend: dream_integrate_step
|
||||
- backend: dream_topics_step
|
||||
topic_count: 3
|
||||
topic_diversity_days: 7
|
||||
- backend: dream_finish_step
|
||||
file_catalog: dream
|
||||
|
||||
auto_memory:
|
||||
backend: base
|
||||
description: "Auto-memory: record conversation facts into a daily note"
|
||||
parameters:
|
||||
type: object
|
||||
properties:
|
||||
messages:
|
||||
type: array
|
||||
description: "messages"
|
||||
items:
|
||||
type: object
|
||||
session_id:
|
||||
type: string
|
||||
description: "session identifier passed to daily_create"
|
||||
default: ""
|
||||
memory_hint:
|
||||
type: string
|
||||
description: "optional hint"
|
||||
required:
|
||||
- messages
|
||||
steps:
|
||||
- backend: auto_memory_step
|
||||
|
||||
auto_resource:
|
||||
backend: base
|
||||
description: "Auto-resource: interpret resource files into daily notes"
|
||||
parameters:
|
||||
type: object
|
||||
properties:
|
||||
changes:
|
||||
type: array
|
||||
description: "resource change batch, each item has path/file_path and change"
|
||||
items:
|
||||
type: object
|
||||
properties:
|
||||
path:
|
||||
type: string
|
||||
file_path:
|
||||
type: string
|
||||
change:
|
||||
type: string
|
||||
description: "added/modified/deleted"
|
||||
required:
|
||||
- changes
|
||||
steps:
|
||||
- backend: auto_resource_step
|
||||
|
||||
proactive:
|
||||
backend: base
|
||||
description: "Proactive: read daily/<date>/interests.yaml and expose the latest user-interest topics."
|
||||
parameters:
|
||||
type: object
|
||||
properties:
|
||||
date:
|
||||
type: string
|
||||
description: "YYYY-MM-DD to read; defaults to today in the dreamer's timezone"
|
||||
default: ""
|
||||
include_content:
|
||||
type: boolean
|
||||
description: "whether to include the raw YAML content in response metadata"
|
||||
default: true
|
||||
steps:
|
||||
- backend: proactive_step
|
||||
|
||||
version:
|
||||
backend: base
|
||||
description: "return reme package version"
|
||||
|
|
@ -426,103 +537,6 @@ jobs:
|
|||
steps:
|
||||
- backend: edit_step
|
||||
|
||||
auto_dream:
|
||||
backend: base
|
||||
description: "Auto-dream: scan today's day-index and daily notes, globally extract merged units/topics, integrate digest units, write interests.yaml, and persist the dream catalog."
|
||||
parameters:
|
||||
type: object
|
||||
properties:
|
||||
date:
|
||||
type: string
|
||||
description: "YYYY-MM-DD to scan; defaults to today in the dreamer's timezone"
|
||||
default: ""
|
||||
hint:
|
||||
type: string
|
||||
description: "caller guidance passed through to dream extract/integrate"
|
||||
default: ""
|
||||
topic_count:
|
||||
type: integer
|
||||
description: "maximum number of final daily interest topics"
|
||||
default: 3
|
||||
topic_diversity_days:
|
||||
type: integer
|
||||
description: "number of previous interests.yaml days to avoid repeating"
|
||||
default: 7
|
||||
steps:
|
||||
- backend: dream_extract_step
|
||||
file_catalog: dream
|
||||
topic_session_id: interests
|
||||
- backend: dream_integrate_step
|
||||
- backend: dream_topics_step
|
||||
topic_count: 3
|
||||
topic_diversity_days: 7
|
||||
- backend: dream_finish_step
|
||||
file_catalog: dream
|
||||
|
||||
proactive:
|
||||
backend: base
|
||||
description: "Proactive: read daily/<date>/interests.yaml and expose the latest user-interest topics."
|
||||
parameters:
|
||||
type: object
|
||||
properties:
|
||||
date:
|
||||
type: string
|
||||
description: "YYYY-MM-DD to read; defaults to today in the dreamer's timezone"
|
||||
default: ""
|
||||
include_content:
|
||||
type: boolean
|
||||
description: "whether to include the raw YAML content in response metadata"
|
||||
default: true
|
||||
steps:
|
||||
- backend: proactive_step
|
||||
|
||||
auto_memory:
|
||||
backend: base
|
||||
description: "Auto-memory: record conversation facts into a daily note"
|
||||
parameters:
|
||||
type: object
|
||||
properties:
|
||||
messages:
|
||||
type: array
|
||||
description: "messages"
|
||||
items:
|
||||
type: object
|
||||
session_id:
|
||||
type: string
|
||||
description: "session identifier passed to daily_create"
|
||||
default: ""
|
||||
memory_hint:
|
||||
type: string
|
||||
description: "optional hint"
|
||||
required:
|
||||
- messages
|
||||
steps:
|
||||
- backend: auto_memory_step
|
||||
|
||||
auto_resource:
|
||||
backend: base
|
||||
description: "Auto-resource: interpret resource files into daily notes"
|
||||
parameters:
|
||||
type: object
|
||||
properties:
|
||||
changes:
|
||||
type: array
|
||||
description: "resource change batch, each item has path/file_path and change"
|
||||
items:
|
||||
type: object
|
||||
properties:
|
||||
path:
|
||||
type: string
|
||||
file_path:
|
||||
type: string
|
||||
change:
|
||||
type: string
|
||||
description: "added/modified/deleted"
|
||||
required:
|
||||
- changes
|
||||
steps:
|
||||
- backend: auto_resource_step
|
||||
|
||||
components:
|
||||
tokenizer:
|
||||
default:
|
||||
|
|
|
|||
4
reme/skills/claude_code_memory/SKILL.md
Normal file
4
reme/skills/claude_code_memory/SKILL.md
Normal file
|
|
@ -0,0 +1,4 @@
|
|||
---
|
||||
name: claude_code_memory
|
||||
description: claude_code_memory
|
||||
---
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
---
|
||||
name: qwenpaw_memory
|
||||
description: 记录、检索、更新重要信息
|
||||
description: qwenpaw_memory
|
||||
---
|
||||
|
||||
## 记忆
|
||||
|
|
|
|||
99
reme/skills/reme_memory/SKILL.md
Normal file
99
reme/skills/reme_memory/SKILL.md
Normal file
|
|
@ -0,0 +1,99 @@
|
|||
---
|
||||
name: reme_memory
|
||||
description: Use ReMe as a file-native long-term memory system through the reme CLI.
|
||||
---
|
||||
|
||||
## ReMe Memory
|
||||
|
||||
Use ReMe as the persistent memory layer for this Agent. ReMe stores raw sessions, daily notes, resources, and long-term digest memories in a local vault. Prefer ReMe for information that should survive across conversations.
|
||||
|
||||
## Before Use
|
||||
|
||||
- ReMe should already be running with `reme start`.
|
||||
- If a command fails because the service is not running, tell the user to start ReMe.
|
||||
- Use CLI commands directly; do not edit the vault files by hand unless the user explicitly asks.
|
||||
|
||||
Useful health checks:
|
||||
|
||||
```bash
|
||||
reme find_reme
|
||||
reme health_check
|
||||
reme version
|
||||
```
|
||||
|
||||
## Retrieval
|
||||
|
||||
Before answering questions about previous conversations, user preferences, project history, decisions, resources, or long-term context, search ReMe first:
|
||||
|
||||
```bash
|
||||
reme search query="<question or keywords>" limit=5
|
||||
```
|
||||
|
||||
When search results point to a useful file, read the relevant file or range:
|
||||
|
||||
```bash
|
||||
reme read path="<vault-relative-path>"
|
||||
reme read path="<vault-relative-path>" start_line=1 end_line=80
|
||||
```
|
||||
|
||||
Use `traverse` when wikilink neighbors may matter:
|
||||
|
||||
```bash
|
||||
reme traverse path="<vault-relative-path>" depth=1 direction=both
|
||||
```
|
||||
|
||||
## Writing Memory
|
||||
|
||||
Record memory when the conversation includes durable facts, user preferences, important decisions, project context, or lessons learned. Avoid storing secrets or sensitive personal data unless the user explicitly requests it.
|
||||
|
||||
For ordinary conversation memory, call `auto_memory` with the current conversation messages and a stable session id:
|
||||
|
||||
```bash
|
||||
reme auto_memory \
|
||||
session_id="<session-id>" \
|
||||
messages='[{"role":"user","content":"..."},{"role":"assistant","content":"..."}]' \
|
||||
memory_hint="<why this should be remembered>"
|
||||
```
|
||||
|
||||
For direct file operations, use ReMe file jobs:
|
||||
|
||||
```bash
|
||||
reme write path="daily/<YYYY-MM-DD>/<name>.md" name="<name>" description="<description>" content="<markdown>"
|
||||
reme edit path="<vault-relative-path>" old="<old text>" new="<new text>"
|
||||
```
|
||||
|
||||
Read before editing, and preserve existing content unless replacing it is explicitly intended.
|
||||
|
||||
## Resources
|
||||
|
||||
External documents should be placed under `resource/YYYY-MM-DD/`. ReMe background watchers normally process new resource files after `reme start`.
|
||||
|
||||
To trigger resource processing manually:
|
||||
|
||||
```bash
|
||||
reme auto_resource changes='[{"path":"resource/<YYYY-MM-DD>/<file>","change":"added"}]'
|
||||
```
|
||||
|
||||
## Long-Term Consolidation
|
||||
|
||||
`auto_dream` consolidates daily notes and resource interpretations into long-term digest memories. It can run from cron in ReMe, or be called manually when the Agent framework owns the schedule:
|
||||
|
||||
```bash
|
||||
reme auto_dream date="<YYYY-MM-DD>"
|
||||
```
|
||||
|
||||
Use `proactive` to read interest topics generated by `auto_dream`:
|
||||
|
||||
```bash
|
||||
reme proactive date="<YYYY-MM-DD>"
|
||||
```
|
||||
|
||||
`proactive` only returns topics; the Agent decides whether and how to mention them to the user.
|
||||
|
||||
## Integration Rules
|
||||
|
||||
- Any Agent framework can integrate ReMe through this skill plus the `reme` CLI.
|
||||
- Background and cron jobs run automatically after `reme start`.
|
||||
- Hook jobs require explicit Agent lifecycle integration: call `auto_memory` after useful conversation turns, `auto_resource` after resource ingestion, `auto_dream` on a schedule or user request, and `proactive` before generating proactive suggestions.
|
||||
- QwenPaw 2.0 will integrate the new ReMe flow directly.
|
||||
- A Claude Code plugin is planned for lower-friction setup.
|
||||
Loading…
Add table
Reference in a new issue