diff --git a/README.md b/README.md index 9837c3b7..885d5479 100644 --- a/README.md +++ b/README.md @@ -19,15 +19,15 @@

- A memory management toolkit for AI agents — Remember Me, Refine Me.
+ An agent memory layer that turns conversations and resources into readable, editable, searchable Markdown memory.

> Previous versions: [0.3.x](https://github.com/agentscope-ai/ReMe/tree/reme_v3) · > [0.2.x](https://github.com/agentscope-ai/ReMe/tree/v0.2.0.6) · > [MemoryScope](https://github.com/agentscope-ai/ReMe/tree/memoryscope_branch) -🧠 ReMe is a memory management toolkit for **AI agents**. It turns conversations and resources into readable, editable, -and searchable file-based long-term memory. +🧠 ReMe is a local-first memory layer for **AI agents**. It turns conversations and resources into file-based long-term +memory, then continuously indexes, links, and consolidates that memory for future recall. ## ✨ Core Ideas @@ -44,23 +44,21 @@ and searchable file-based long-term memory. ReMe Design Philosophy

-
-Use Cases +## 🔭 Use Cases -
- -- **Personal assistants**: Provide long-term memory for agents such - as [QwenPaw](https://github.com/agentscope-ai/QwenPaw). -- **Coding assistants**: Preserve coding style, project background, and workflow experience across sessions. -- **Knowledge QA**: Progressively transform resources and conversations into a searchable, traceable, and linked - Markdown knowledge base. -- **Task automation**: Reuse successful paths, lessons from failures, and operating procedures from past tasks. - -
+- **Personal assistants**: Give personal assistants such as + [QwenPaw](https://github.com/agentscope-ai/QwenPaw), [OpenClaw](https://github.com/openclaw/openclaw), and + [Hermes](https://github.com/nousresearch/hermes-agent) a user-editable long-term memory layer. +- **Coding agents**: Preserve coding style, project background, repository decisions, and workflow + experience across sessions when integrating with coding agents such as [Claude Code](plugins/reme). +- **LLM Wiki**: Turn conversations, notes, and resources into a searchable, traceable, and linked Markdown + knowledge base that both users and agents can maintain. +- **Self-evolving agents**: Support agents that learn from experience by saving successful paths, failed attempts, + reusable procedures, and periodic reflections as memory. ## 📰 News -- Our paper [Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution](https://aclanthology.org/2026.findings-acl.829/) has been accepted to Findings of ACL 2026. +- [2026.07] - Our paper [Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution](https://aclanthology.org/2026.findings-acl.829/) has been accepted to Findings of ACL 2026. ## 🚀 Quick Start @@ -84,17 +82,22 @@ pip install -e ".[core]" ### Environment Variables -Configure environment variables: +Configure environment variables when you want LLM-powered memory evolution or embedding retrieval: ```bash cat > .env <<'EOF' +# Optional: enables semantic retrieval when the embedding store is configured. EMBEDDING_API_KEY=sk-xxx EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1 + +# Required for auto_memory, auto_resource, and auto_dream. LLM_API_KEY=sk-xxx LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1 EOF ``` +Basic file operations, BM25 search, wikilink traversal, and reading proactive topics can run without LLM credentials. + ### Start the Service ```bash @@ -115,58 +118,46 @@ reme version curl -s http://127.0.0.1:2333/version -H 'Content-Type: application/json' -d '{}' ``` -### Agent Integration +### 5-Minute Memory Demo -ReMe runs as a service and exposes memory through CLI / MCP jobs. Agents can adopt it in whichever way fits them: deep -SDK integration, plugin integration, or a lightweight Skill + CLI integration. They can wire `auto_memory` / `proactive` -into their lifecycle so conversations are consolidated into memory and surfaced at the right time. Indexing ( -`auto_index`) and resource processing (`auto_resource`) run automatically through file watching, and `auto_dream` -consolidates daily memories into long-term digests on a schedule. +With the service running, write a memory node, let ReMe index it, then retrieve it: -

Integration demos

+```bash +reme write \ + path=digest/wiki/quick-start-demo \ + name="Quick Start Demo" \ + description="A first ReMe memory node" \ + content="# Quick Start Demo - - - - - - - - - - - - - - - - -
Auto MemoryAuto Dream
QwenPaw - QwenPaw Auto Memory demo - - QwenPaw Auto Dream demo -
Claude Code - Claude Code Auto Memory demo - - Claude Code Auto Dream demo -
+ReMe stores agent memory as readable Markdown. -Integration status across agents: +Related: [[digest/wiki/memory-as-file.md]]" -| Agent | Status | How it integrates | -|-----------------------------------------------------|-------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| [QwenPaw](https://github.com/agentscope-ai/QwenPaw) | ✅ Available | [Deep SDK integration](https://github.com/agentscope-ai/QwenPaw/blob/main/src/qwenpaw/agents/memory/reme_light_memory_manager.py) — embeds the ReMe app in-process, drives `search` / `auto_memory` / `auto_dream` jobs via `run_job`, and reuses the agent's own model (no separate server). | -| [Claude Code](plugins/reme) | ✅ Available | Plugin: HTTP MCP server for recall, a `reme-memory` skill, and a Stop hook that records each session via `auto_memory_cc`. | -| Skill + CLI integration | ✅ Available | [Skill + CLI](skills/reme_memory/SKILL.md): install or copy the `reme_memory` skill, then use `reme version` to check the version and `reme search query="xxx" limit=5` to search memory. | +reme search query="agent memory markdown" limit=5 +reme read path=digest/wiki/quick-start-demo start_line=1 end_line=20 +``` -For more details, see the [Quick Start](docs/zh/quick_start.md). +The generated file is ordinary Markdown with frontmatter: + +```markdown +--- +name: Quick Start Demo +description: A first ReMe memory node +--- + +# Quick Start Demo + +ReMe stores agent memory as readable Markdown. + +Related: [[digest/wiki/memory-as-file.md]] +``` ## 📁 Memory System > Memory as File, File as Memory. ReMe treats **memory as files**, progressively processing raw conversations and external resources from `session/` and -`resource/` into `daily/`, then consolidating them into reusable long-term knowledge nodes under `digest/`. +`resource/` into `daily/`, then consolidating them into reusable long-term memory nodes under `digest/`. ### Directory Structure @@ -200,20 +191,24 @@ ReMe treats **memory as files**, progressively processing raw conversations and ReMe file-based memory system overview

+## 🧭 Memory Design Philosophy + +> Capture raw dialogs and resources, refine them into long-term preferences, reusable experience, and valuable knowledge, +> while keeping the result editable by humans and agents. + ### Automatic Memory Flow -ReMe's automatic memory flow gradually turns raw conversations and resources into searchable, traceable, and reusable -file-based memory. During normal operation, background watchers maintain indexes and process resources, agent hooks -trigger conversation memory, and long-term consolidation plus proactive reminders run through scheduled tasks or -on-demand calls. +ReMe follows a capture → index → consolidate → recall loop. Conversations and resources first become daily memory cards; +background jobs keep files searchable; `auto_dream` distills stable knowledge into `digest/`; agents recall memory +through search, wikilinks, or proactive topics. -| Capability | How it runs | Purpose | Main parameters | -|---------------------------------------------|-----------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------| -| [`auto_index`](docs/zh/memory_search.md) | Background maintenance via `index_update_loop` | Scans on startup and continuously watches Markdown/JSONL changes in `daily/`, `digest/`, and `resource/`; updates chunk, BM25, embedding, and wikilink graph indexes. | Config: `watch_dirs`, `watch_suffixes` | -| [`auto_memory`](docs/zh/auto_memory.md) | Agent after-reply hook; also callable on demand | Saves raw conversation text and turns long-term valuable information into `daily//.md` memory cards. | Required: `messages`; optional: `session_id`, `memory_hint` | -| [`auto_resource`](docs/zh/auto_resource.md) | Automatically triggered by resource watching; also callable on demand | Reads resource changes under `resource//` and creates or updates LLM-named daily resource cards linked by `source_resource`. | Required: `changes`; each item may include `path`, `file_path`, `change` | -| [`auto_dream`](docs/zh/auto_dream.md) | Scheduled by `dream_cron`; also callable on demand | Scans daily input for a given date, extracts long-term memory units, integrates them into `digest/`, and writes `daily//interests.yaml`. | `date`, `hint`, `topic_count`, `topic_diversity_days` | -| [`proactive`](docs/zh/proactive.md) | Read on demand before agent proactive reminders | Reads `interests.yaml` generated by `auto_dream` and exposes topics worth attention to the upper-level agent; the caller decides whether to remind the user. | `date`, `include_content` | +| Capability | Entry point | What it does | Output | +|---------------------------------------------|--------------------------------------------------|-----------------------------------------------------------------------------------------------|----------------------------------------------------------| +| [`auto_memory`](docs/en/auto_memory.md) | Agent hook or `reme auto_memory` | Distills useful conversation facts while preserving the raw session. | `session/dialog/*.jsonl`, `daily//.md` | +| [`auto_resource`](docs/en/auto_resource.md) | Resource watcher or `reme auto_resource` | Turns files under `resource//` into source-linked daily cards. | `daily//.md` | +| [`auto_index`](docs/en/memory_search.md) | Background watcher or `reme reindex` | Maintains chunks, BM25/embedding indexes, and the wikilink graph. | Searchable `daily/`, `digest/`, and `resource/` content | +| [`auto_dream`](docs/en/auto_dream.md) | `dream_cron` or `reme auto_dream` | Consolidates changed daily cards into long-term personal, procedure, and wiki memory. | `digest/**`, `daily//interests.yaml` | +| [`proactive`](docs/en/proactive.md) | `reme proactive` before an agent decides to act | Reads topics generated by `auto_dream`; the host agent decides whether and how to mention them. | Structured topics from `daily//interests.yaml` | @@ -234,44 +229,71 @@ on-demand calls.
-### ReMe Operations +## 🤝 Agent-friendly Integration -ReMe operates the workspace through a unified CLI / Service Job interface. Agents usually only need retrieval, reading, -writing, editing, and automatic memory commands. Lower-level indexing, frontmatter, and file operation commands are -mainly for maintenance, debugging, or advanced integration. +ReMe runs as a local memory service and offers multiple integration paths: CLI, HTTP API, MCP server, and SDK. Different +agents can choose the path that fits their runtime while sharing the same local memory workspace. -| Category | Command | Description | Parameters | -|----------------|-------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------| -| System status | `reme version` | Returns the ReMe package version. | None | -| System status | `reme health_check` | Returns a health-check summary for ReMe components. | None | -| System status | `reme help` | Lists registered jobs and their metadata. | None | -| Retrieval/read | [`reme search`](docs/zh/memory_search.md) | Performs hybrid retrieval in the workspace with vector recall, BM25, and RRF fusion. | Required: `query`; optional: `limit`, `min_score` | -| Retrieval/read | `reme node_search` | Recalls similar digest nodes by candidate abstraction name and description, mainly for `auto_dream` deduplication or association. | Required: `query`; optional: `limit` | -| Retrieval/read | `reme traverse` | Traverses the wikilink graph from a specified path. | Required: `path`; optional: `depth`, `direction` | -| Retrieval/read | `reme read` | Reads a Markdown file under the workspace. | Required: `path`; optional: `start_line`, `end_line` | -| Retrieval/read | `reme read_image` | Reads an image file under the workspace and returns base64. | Required: `path` | -| Index | `reme reindex` | Clears file-store indexes and rebuilds indexes from existing files. | Config: `watch_dirs`, `watch_suffixes` | -| Daily | `reme daily_list` | Lists notes for a day. | `date` | -| Daily | `reme daily_reindex` | Rebuilds the day-index page `daily/.md`. | `date` | -| Metadata | `reme frontmatter_read` | Reads file frontmatter. | Required: `path` | -| Metadata | `reme frontmatter_update` | Merges key-values into file frontmatter. | Required: `path`, `metadata` | -| Metadata | `reme frontmatter_delete` | Deletes specified keys from file frontmatter. | Required: `path`, `keys` | -| File operation | `reme stat` | Gets workspace path status, including size, mtime, existence, and file/directory type. | Required: `path` | -| File operation | `reme list` | Lists files under a workspace path. | `path`, `recursive`, `limit` | -| File operation | `reme write` | Creates or overwrites a Markdown file and writes name/description frontmatter. | Required: `path`, `name`, `description`, `content`; optional: `metadata` | -| File operation | `reme edit` | Performs full-text find-and-replace on a Markdown file. | Required: `path`, `old`, `new` | -| File operation | `reme move` | Moves or renames a workspace file and rewrites inbound wikilinks by default. | Required: `src_path`, `dst_path`; optional: `overwrite`, `retarget` | -| File operation | `reme delete` | Deletes a workspace file or folder and returns inbound wikilinks that still exist. | Required: `path` | +| Agents | Recommended path | What works out of the box | +|------------------------------------------------------|-----------------------------------------------------------------------------|------------------------------------------------------------------------------------------------| +| **QwenPaw** | Embed ReMe via the Python SDK. | Reuse the app's own lifecycle and model config while keeping memory local and file-based. | +| **Claude Code** | Start ReMe as an MCP service and install [plugins/reme](plugins/reme). | MCP recall tools, a `reme-memory` skill, and a Stop hook that records sessions automatically. | +| **Other CLI-capable agents (OpenClaw/Hermes/Codex)** | Copy or install [skills/reme_memory/SKILL.md](skills/reme_memory/SKILL.md). | Search/read/write memory and call `auto_memory`, `auto_dream`, and `proactive` via the CLI. | + +

Integration demos

+ + + + + + + + + + + + + + + + + +
Auto MemoryAuto Dream
QwenPaw + QwenPaw Auto Memory demo + + QwenPaw Auto Dream demo +
Claude Code + Claude Code Auto Memory demo + + Claude Code Auto Dream demo +
+ +## 🛠️ ReMe Operations + +ReMe operates the workspace through a unified job interface exposed by the CLI. Agents usually only need retrieval, +reading, writing, editing, and automatic memory commands. Lower-level indexing, frontmatter, and file operation commands +are mainly for maintenance, debugging, or advanced integration. Run `reme help` for the full job list. + +| Command | Purpose | +|-------------------------------------------|--------------------------------------------------------------------------------------| +| `reme start` | Start the local ReMe service. | +| `reme version` / `reme health_check` | Check package and component status. | +| [`reme search`](docs/en/memory_search.md) | Retrieve memory with hybrid search. | +| `reme read` / `reme write` / `reme edit` | Inspect and maintain Markdown memory files. | +| `reme auto_memory` | Turn conversation messages into daily memory cards. Requires LLM credentials. | +| `reme auto_resource` | Interpret files under `resource/` into daily resource cards. Requires LLM credentials. | +| `reme auto_dream` / `reme proactive` | Consolidate daily memory into long-term digest and surface topics worth attention. | +| `reme reindex` | Rebuild search and wikilink indexes from existing files. | ## 🤝 Community and Support - **Issues and requests**: Check [Open Issues](https://github.com/agentscope-ai/ReMe/issues) first. If there is no related discussion, open a new issue with background, expected behavior, and impact scope. -- **Code contributions**: Before making changes, read the [contribution guide](docs/zh/contributing.md) - and [code framework](docs/zh/framework.md), and follow the CLI / Service / Application / Job / Step / Component +- **Code contributions**: Before making changes, read the [contribution guide](docs/en/contributing.md) + and [code framework](docs/en/framework.md), and follow the CLI / Service / Application / Job / Step / Component layering. - **Documentation contributions**: For user-visible installation, configuration, invocation, or behavior changes, update - `docs/zh/` or `README.md` accordingly. + `docs/en/`, `docs/zh/`, or the README files accordingly. - **Commit convention**: Conventional Commits are recommended, for example `feat(search): add link expansion option` or `docs(zh): update quick start`. - **Pre-submit checks**: Before submitting a PR, try to run `pre-commit run --all-files` and `pytest`. If tests that @@ -290,28 +312,12 @@ Thanks to everyone who has contributed to ReMe: ## 📄 Citation ```bibtex -@software{AgentscopeReMe2026, - title = {AgentscopeReMe: Memory Management Kit for Agents}, +@software{ReMe2026, + title = {Remember me, Refine me: Memory Management Kit for Agents}, author = {ReMe Team}, url = {https://reme.agentscope.io}, year = {2026} } - -@inproceedings{cao-etal-2026-remember, - title = "Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution", - author = "Cao, Zouying and - Deng, Jiaji and - Yu, Li and - Zhou, Weikang and - Liu, Zhaoyang and - Ding, Bolin and - Zhao, Hai", - booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026", - year = "2026", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2026.findings-acl.829/", - pages = "16803--16822" -} ``` ## ⚖️ License diff --git a/README_ZH.md b/README_ZH.md index 0b25431d..f048bfdb 100644 --- a/README_ZH.md +++ b/README_ZH.md @@ -19,14 +19,14 @@

- A memory management toolkit for AI agents — Remember Me, Refine Me.
+ 一个将对话和资料转化为可读、可编辑、可检索 Markdown 记忆的 Agent 记忆层。

> 历史版本：[0.3.x](https://github.com/agentscope-ai/ReMe/tree/reme_v3) · > [0.2.x](https://github.com/agentscope-ai/ReMe/tree/v0.2.0.6) · > [MemoryScope](https://github.com/agentscope-ai/ReMe/tree/memoryscope_branch) -🧠 ReMe 是一个面向 **AI 智能体** 的记忆管理工具，可将对话和资料沉淀为可读、可编辑、可检索的文件化长期记忆。 +🧠 ReMe 是一个面向 **AI 智能体** 的 local-first 记忆层。它把对话和资料沉淀为文件化长期记忆，并持续完成索引、链接和整理，让后续 Agent 能够可靠召回。 ## ✨ 核心创新 @@ -39,23 +39,18 @@ ReMe 设计理念

-
-适用场景 +## 🔭 适用场景 -
- -- **个人助理**：为 [QwenPaw](https://github.com/agentscope-ai/QwenPaw) 等 Agent 提供长期记忆。 -- **编程助手**：沉淀代码风格、项目背景和流程经验，跨会话保持一致。 -- **知识问答**：把资料和对话渐进加工成可检索、可追溯、可链接的 Markdown 知识库。 -- **任务自动化**：复用历史任务中的成功路径、失败教训和操作流程。 - -
+- **Personal assistants**：为 [QwenPaw](https://github.com/agentscope-ai/QwenPaw)、 + [OpenClaw](https://github.com/openclaw/openclaw)、[Hermes](https://github.com/nousresearch/hermes-agent) + 等个人助理提供用户可编辑的长期记忆层。 +- **Coding agents**：在接入 [Claude Code](plugins/reme) 等 coding agent 时，跨会话保留代码风格、项目背景、仓库决策和流程经验。 +- **LLM Wiki**：把对话、笔记和资料转化为可检索、可追溯、可链接的 Markdown 知识库，由用户和 Agent 共同维护。 +- **Self-evolving agents**：帮助 Agent 从经验中学习，把成功路径、失败尝试、可复用流程和阶段性反思沉淀为记忆。 ## 📰 新闻 -- - -我们的论文 [Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution](https://aclanthology.org/2026.findings-acl.829/) +- [2026.07] - 我们的论文 [Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution](https://aclanthology.org/2026.findings-acl.829/) 已被 Findings of ACL 2026 接收。 ## 🚀 快速开始 @@ -80,17 +75,22 @@ pip install -e ".[core]" ### 环境变量 -配置环境变量： +如果需要 LLM 驱动的记忆演化或 embedding 检索，可以配置环境变量： ```bash cat > .env <<'EOF' +# 可选：配置 embedding store 后启用语义检索。 EMBEDDING_API_KEY=sk-xxx EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1 + +# 必须：auto_memory、auto_resource 和 auto_dream 需要 LLM。 LLM_API_KEY=sk-xxx LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1 EOF ``` +基础文件读写、BM25 检索、wikilink 遍历和 proactive topics 读取可以先不配置 LLM 凭证。 + ### 启动服务 ```bash @@ -111,49 +111,39 @@ reme version curl -s http://127.0.0.1:2333/version -H 'Content-Type: application/json' -d '{}' ``` -### 快速接入 +### 5 分钟记忆 Demo -ReMe 以服务形式运行，通过 CLI / MCP job 对外暴露记忆。各 Agent 可以按适合自身的方式接入：SDK 深度集成、plugin 集成，或轻量的 -Skill + CLI 集成。它们可以把 `auto_memory` / `proactive` 接入自身生命周期，让对话沉淀为记忆并在合适时机浮现。索引（ -`auto_index`）与资源加工（`auto_resource`）由文件监控自动触发，`auto_dream` 则按计划把 daily 记忆整理为可长期复用的 digest。 +服务运行后，可以写入一个记忆节点，让 ReMe 索引并检索它： -

集成演示

+```bash +reme write \ + path=digest/wiki/quick-start-demo \ + name="Quick Start Demo" \ + description="第一个 ReMe 记忆节点" \ + content="# Quick Start Demo - - - - - - - - - - - - - - - - -
Auto MemoryAuto Dream
QwenPaw - QwenPaw Auto Memory 演示 - - QwenPaw Auto Dream 演示 -
Claude Code - Claude Code Auto Memory 演示 - - Claude Code Auto Dream 演示 -
+ReMe 会把 Agent 记忆保存为可读的 Markdown。 -各 Agent 接入状态： +相关链接：[[digest/wiki/memory-as-file.md]]" -| Agent | 状态 | 接入方式 | -|-----------------------------------------------------|-------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| [QwenPaw](https://github.com/agentscope-ai/QwenPaw) | ✅ 已支持 | [SDK 深度集成](https://github.com/agentscope-ai/QwenPaw/blob/main/src/qwenpaw/agents/memory/reme_light_memory_manager.py)：进程内嵌入 ReMe 应用，通过 `run_job` 驱动 `search` / `auto_memory` / `auto_dream`，并复用 Agent 自身模型（无需独立 server）。 | -| [Claude Code](plugins/reme) | ✅ 已支持 | Plugin：通过 HTTP MCP server 做召回，提供 `reme-memory` skill，并用 Stop hook 在会话结束时经 `auto_memory_cc` 录入记忆。 | -| Skill + CLI 集成 | ✅ 已支持 | [Skill + CLI](skills/reme_memory/SKILL.md)：安装或复制 `reme_memory` skill 后，直接使用 `reme version` 检查版本，使用 `reme search query="xxx" limit=5` 检索记忆。 | +reme search query="agent memory markdown" limit=5 +reme read path=digest/wiki/quick-start-demo start_line=1 end_line=20 +``` -更多细节见 [快速开始](docs/zh/quick_start.md)。 +生成的文件是普通 Markdown，并带有 frontmatter： + +```markdown +--- +name: Quick Start Demo +description: 第一个 ReMe 记忆节点 +--- + +# Quick Start Demo + +ReMe 会把 Agent 记忆保存为可读的 Markdown。 + +相关链接：[[digest/wiki/memory-as-file.md]] +``` ## 📁 记忆系统 @@ -194,18 +184,22 @@ ReMe 将**记忆视为文件**，让原始对话和外部资料从 `session/`、 ReMe 文件化记忆系统总览

+## 🧭 记忆设计理念 + +> 捕获原始对话和资料，将其整理为长期偏好、可复用经验和有价值的知识，并让结果始终能被用户和 Agent 直接编辑。 + ### 自动记忆流程 -ReMe 的自动记忆流程会把原始对话和资料逐步加工成可检索、可追溯、可长期复用的文件化记忆。常规运行时，后台监听负责维护索引和处理资源，Agent -hook 负责触发对话记忆，长期整理与主动提醒则通过定时任务或按需调用完成。 +ReMe 遵循 capture → index → consolidate → recall 的循环。对话和资料先变成 daily 记忆卡片；后台任务保持文件可检索； +`auto_dream` 将稳定知识沉淀到 `digest/`；Agent 再通过搜索、wikilink 或 proactive topics 召回记忆。 -| 能力 | 运行方式 | 作用 | 主要参数 | -|---------------------------------------------|-------------------------------|---------------------------------------------------------------------------------------------------------|----------------------------------------------------| -| [`auto_index`](docs/zh/memory_search.md) | 后台维护；对应 `index_update_loop` | 启动时扫描并持续监听 `daily/`、`digest/`、`resource/` 中的 Markdown/JSONL 变化，更新 chunk、BM25、embedding 与 wikilink 图谱索引。 | 配置项：`watch_dirs`、`watch_suffixes` | -| [`auto_memory`](docs/zh/auto_memory.md) | Agent after-reply hook；也可按需调用 | 保存对话原文，并把有长期价值的信息整理成 `daily//.md` 记忆卡片。 | 必填：`messages`；可选：`session_id`、`memory_hint` | -| [`auto_resource`](docs/zh/auto_resource.md) | 资源监听自动触发；也可按需调用 | 解读 `resource//` 下的资源变更，生成或更新由 LLM 命名、通过 `source_resource` 关联的 daily 资源卡片。 | 必填：`changes`；每项可含 `path`、`file_path`、`change` | -| [`auto_dream`](docs/zh/auto_dream.md) | 定时任务 `dream_cron`；也可按需调用 | 扫描指定日期的 daily 输入，抽取长期记忆单元并整合进 `digest/`，同时写入 `daily//interests.yaml`。 | `date`、`hint`、`topic_count`、`topic_diversity_days` | -| [`proactive`](docs/zh/proactive.md) | Agent 主动提醒前按需读取 | 读取 `auto_dream` 生成的 `interests.yaml`，将当天值得关注的主题暴露给上层 Agent；是否提醒用户由调用方决定。 | `date`、`include_content` | +| 能力 | 入口 | 作用 | 输出 | +|---------------------------------------------|-----------------------------------|-------------------------------------|----------------------------------------------------------| +| [`auto_memory`](docs/zh/auto_memory.md) | Agent hook 或 `reme auto_memory` | 提炼有长期价值的对话事实，同时保留原始 session。 | `session/dialog/*.jsonl`、`daily//.md` | +| [`auto_resource`](docs/zh/auto_resource.md) | 资源监听或 `reme auto_resource` | 将 `resource//` 下的文件转为带来源链接的 daily 卡片。 | `daily//.md` | +| [`auto_index`](docs/zh/memory_search.md) | 后台监听或 `reme reindex` | 维护 chunks、BM25/embedding 索引和 wikilink 图谱。 | 可检索的 `daily/`、`digest/`、`resource/` 内容 | +| [`auto_dream`](docs/zh/auto_dream.md) | `dream_cron` 或 `reme auto_dream` | 将变化的 daily 卡片整理为长期 personal、procedure 和 wiki 记忆。 | `digest/**`、`daily//interests.yaml` | +| [`proactive`](docs/zh/proactive.md) | Agent 决定主动行动前调用 `reme proactive` | 读取 `auto_dream` 生成的 topics；是否以及如何提醒用户由宿主 Agent 决定。 | 来自 `daily//interests.yaml` 的结构化 topics | @@ -226,33 +220,60 @@ hook 负责触发对话记忆，长期整理与主动提醒则通过定时任务
-### ReMe Operations +## 🤝 Agent-friendly Integration -ReMe 通过统一的 CLI / Service Job 接口操作 workspace。Agent 通常只需要使用检索、读取、写入、编辑和自动记忆相关命令；更底层的索引、frontmatter -和文件操作接口主要用于维护、调试或高级集成。 +ReMe 作为本地记忆服务运行，并提供 CLI、HTTP API、MCP server 和 SDK 等多种接入方式。不同 Agent 可以选择适合自身 runtime +的路径，同时共享同一个本地 memory workspace。 -| 分类 | 命令 | 描述 | 参数 | -|-------|-------------------------------------------|-----------------------------------------------------|--------------------------------------------------------| -| 系统状态 | `reme version` | 返回 ReMe 包版本。 | 无 | -| 系统状态 | `reme health_check` | 返回 ReMe 组件健康检查摘要。 | 无 | -| 系统状态 | `reme help` | 列出已注册 jobs 及其 metadata。 | 无 | -| 检索读取 | [`reme search`](docs/zh/memory_search.md) | 在 workspace 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` | -| 检索读取 | `reme node_search` | 根据候选抽象的名称与描述召回相似 digest 节点，主要用于 `auto_dream` 去重或关联。 | 必填：`query`；可选：`limit` | -| 检索读取 | `reme traverse` | 从指定路径出发遍历 wikilink 图谱。 | 必填：`path`；可选：`depth`、`direction` | -| 检索读取 | `reme read` | 读取 workspace 下的 Markdown 文件。 | 必填：`path`；可选：`start_line`、`end_line` | -| 检索读取 | `reme read_image` | 读取 workspace 下的图片文件并返回 base64。 | 必填：`path` | -| 索引维护 | `reme reindex` | 清空文件存储索引，并基于现有文件重建索引。 | 配置项：`watch_dirs`、`watch_suffixes` | -| Daily | `reme daily_list` | 列出某一天的 notes。 | `date` | -| Daily | `reme daily_reindex` | 重建 day-index 页面 `daily/.md`。 | `date` | -| 元数据 | `reme frontmatter_read` | 读取文件 frontmatter。 | 必填：`path` | -| 元数据 | `reme frontmatter_update` | 合并 key-values 到文件 frontmatter。 | 必填：`path`、`metadata` | -| 元数据 | `reme frontmatter_delete` | 删除文件 frontmatter 中的指定 keys。 | 必填：`path`、`keys` | -| 文件操作 | `reme stat` | 获取 workspace 路径状态，包括大小、mtime、是否存在、是否目录或文件。 | 必填：`path` | -| 文件操作 | `reme list` | 列出 workspace 路径下的文件。 | `path`、`recursive`、`limit` | -| 文件操作 | `reme write` | 创建或覆盖 Markdown 文件，并写入 name/description frontmatter。 | 必填：`path`、`name`、`description`、`content`；可选：`metadata` | -| 文件操作 | `reme edit` | 对 Markdown 文件执行全文 find-and-replace。 | 必填：`path`、`old`、`new` | -| 文件操作 | `reme move` | 移动或重命名 workspace 文件，并默认重写入站 wikilink。 | 必填：`src_path`、`dst_path`；可选：`overwrite`、`retarget` | -| 文件操作 | `reme delete` | 删除 workspace 文件或文件夹，并返回仍存在的入站 wikilink。 | 必填：`path` | +| Agent | 推荐接入方式 | 开箱可用能力 | +|------------------------------------------------------|-----------------------------------------------------------------------|--------------------------------------------------------------------| +| **QwenPaw** | 通过 Python SDK 嵌入 ReMe。 | 复用应用自身生命周期和模型配置，同时保持 memory 本地、文件化。 | +| **Claude Code** | 以 MCP service 启动 ReMe，并安装 [plugins/reme](plugins/reme)。 | MCP recall tools、`reme-memory` skill，以及自动记录会话的 Stop hook。 | +| **Other CLI-capable agents (OpenClaw/Hermes/Codex)** | 复制或安装 [skills/reme_memory/SKILL.md](skills/reme_memory/SKILL.md)。 | 通过 CLI 搜索/读取/写入记忆，并调用 `auto_memory`、`auto_dream` 和 `proactive`。 | + +

集成演示

+ + + + + + + + + + + + + + + + + +
Auto MemoryAuto Dream
QwenPaw + QwenPaw Auto Memory 演示 + + QwenPaw Auto Dream 演示 +
Claude Code + Claude Code Auto Memory 演示 + + Claude Code Auto Dream 演示 +
+ +## 🛠️ ReMe Operations + +ReMe 通过 CLI 暴露的统一 job interface 操作 workspace。Agent 通常只需要使用检索、读取、写入、编辑和自动记忆相关命令；更底层的索引、 +frontmatter 和文件操作接口主要用于维护、调试或高级集成。完整 job 列表可以运行 `reme help` 查看。 + +| 命令 | 作用 | +|-----------------------------------------|---------------------------------------------| +| `reme start` | 启动本地 ReMe 服务。 | +| `reme version` / `reme health_check` | 检查包版本和组件状态。 | +| [`reme search`](docs/zh/memory_search.md) | 执行混合记忆检索。 | +| `reme read` / `reme write` / `reme edit` | 检查和维护 Markdown 记忆文件。 | +| `reme auto_memory` | 将对话 messages 转为 daily 记忆卡片；需要 LLM 凭证。 | +| `reme auto_resource` | 将 `resource/` 下的文件解读为 daily 资料卡片；需要 LLM 凭证。 | +| `reme auto_dream` / `reme proactive` | 将 daily 记忆整理为长期 digest，并暴露值得关注的主题。 | +| `reme reindex` | 基于已有文件重建检索和 wikilink 索引。 | ## 🤝 社区与支持 @@ -260,7 +281,7 @@ ReMe 通过统一的 CLI / Service Job 接口操作 workspace。Agent 通常只 说明背景、目标行为和影响范围。 - **代码贡献**：改动前建议阅读 [贡献指南](docs/zh/contributing.md) 和 [代码框架](docs/zh/framework.md)，遵循 CLI / Service / Application / Job / Step / Component 的分层。 -- **文档贡献**：用户可见的安装、配置、调用或行为变化，请同步更新 `docs/zh/` 或 `README.md`。 +- **文档贡献**：用户可见的安装、配置、调用或行为变化，请同步更新 `docs/en/`、`docs/zh/` 或 README 文件。 - **提交规范**：建议使用 Conventional Commits，例如 `feat(search): add link expansion option`、 `docs(zh): update quick start`。 - **提交前检查**：提交 PR 前请尽量运行 `pre-commit run --all-files` 和 `pytest`；如有依赖 LLM、embedding 或外部服务的测试无法运行，请在 @@ -279,28 +300,12 @@ ReMe 通过统一的 CLI / Service Job 接口操作 workspace。Agent 通常只 ## 📄 引用 ```bibtex -@software{AgentscopeReMe2026, - title = {AgentscopeReMe: Memory Management Kit for Agents}, +@software{ReMe2026, + title = {Remember me, Refine me: Memory Management Kit for Agents}, author = {ReMe Team}, url = {https://reme.agentscope.io}, year = {2026} } - -@inproceedings{cao-etal-2026-remember, - title = "Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution", - author = "Cao, Zouying and - Deng, Jiaji and - Yu, Li and - Zhou, Weikang and - Liu, Zhaoyang and - Ding, Bolin and - Zhao, Hai", - booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026", - year = "2026", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2026.findings-acl.829/", - pages = "16803--16822" -} ``` ## ⚖️ 许可证