From ee2ae4ba0e1d49777276941d2f5eeb4d9c5c1f56 Mon Sep 17 00:00:00 2001 From: chrishuan Date: Sat, 11 Apr 2026 17:40:11 +0800 Subject: [PATCH] Update README.md --- README.md | 200 ++++++++++++++++++++++++++++++++++++++++++++++++++ README_CN.md | 201 +++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 401 insertions(+) create mode 100644 README.md create mode 100644 README_CN.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..a445c7b --- /dev/null +++ b/README.md @@ -0,0 +1,200 @@ +[中文](README_CN.md) + +

TencentDB Agent Memory

+ +

AI without memory is just a tool. AI with memory becomes an asset.

+ +

+ OpenClaw Plugin + MIT License +

+ + +**TencentDB Agent Memory is an Agent memory system built by the Tencent Cloud Database team**, adding persistent long-term memory to OpenClaw. Through a 4-layer progressive memory pyramid, it automatically handles memory capture, layered distillation, on-demand recall and injection — turning an Agent from "chat-only" into a long-term, cross-session AI assistant that continuously learns and understands its users. + +## Benchmark + +Evaluated on [PersonaMem](https://github.com/jiani-huang/PersonaMem) (UPenn, COLM 2025) — 589 questions, 20 actors. + +| Category | OpenClaw Native Memory | TencentDB Agent Memory | +| :--- | :---: | :---: | +| Recall Update Reason | 70.97% | **88.89%** | +| Preference Evolution | 66.67% | **83.45%** | +| Personalized Recommendation | 46.67% | **76.36%** | +| Scenario Generalization | 31.58% | **78.95%** | +| Recall User Facts | 29.63% | **79.07%** | +| Recall Facts | 25.00% | **76.47%** | +| Creative Suggestion | 24.00% | **45.16%** | +| **Overall** | **47.85%** | **76.10%** | + +## Highlights + +- **OpenClaw native plugin** — package name `@tencentdb-agent-memory/memory-tencentdb`, one command to install +- **4-layer memory pipeline**: L0 Raw Dialogue → L1 Structured Memory → L2 Scenario Synthesis → L3 User Profile +- **Hybrid recall**: supports `keyword`, `embedding`, and `hybrid` strategies +- **Two retrieval tools**: `tdai_memory_search` (structured memory) and `tdai_conversation_search` (raw conversations) +- **Local-first storage**: JSONL + SQLite, data is directly inspectable on disk +- **Operational features**: deduplication, checkpoint, backup, scheduled cleanup, metrics logging +- **MIT License** + +## Quick Start + +### Requirements + +- Node.js `>= 22.16.0` +- OpenClaw `>= 2026.3.13` + +### Install + +```bash +openclaw plugins install @tencentdb-agent-memory/memory-tencentdb +``` + +Once installed, the plugin hooks into the OpenClaw conversation lifecycle and automatically handles conversation capture, memory recall, and L1/L2/L3 processing. + +### Development from Source + +No build step required. Node.js 22.16+ natively supports TypeScript type stripping, and OpenClaw loads `.ts` source files directly. + +```bash +git clone https://github.com/TencentCloud/TencentDB-Agent-Memory.git +cd TencentDB-Agent-Memory +npm install +openclaw plugins install --link . +``` + +`install --link` registers the current directory as a local plugin in OpenClaw. Source changes take effect after restarting the Gateway. + +### Optional: Enable Embedding Recall + +To use vector retrieval or hybrid recall, add an embedding configuration. Currently supports remote embedding services compatible with the OpenAI API. + +```jsonc +{ + "plugins": { + "entries": { + "memory-tencentdb": { + "enabled": true, + "config": { + "embedding": { // Embedding model config (not LLM model) + "enabled": true, // Enable vector search + "provider": "openai", // Only OpenAI-compatible API is supported + "baseUrl": "https://xxx", // API Base URL + "apiKey": "xxx", // API Key + "model": "text-embedding-3-large", // Model name + "dimensions": 1024 // Vector dimensions (must match the chosen model) + } + } + } + } + } +} +``` + + +## Architecture + +```text + ┌─────────────────┐ + │ L3 Profile │ Preferences & behavioral patterns + ├─────────────────┤ + │ L2 Scenarios │ Cross-session task / scenario blocks + ├─────────────────┤ + │ L1 Structured │ Facts, constraints, preferences, decisions + ├─────────────────┤ + │ L0 Dialogue │ Complete conversation records + └─────────────────┘ +``` + +Each layer serves a different purpose: + +- **L0** preserves raw conversations for replay and precise retrieval +- **L1** extracts high-value information for direct recall +- **L2** organizes scattered memories into scenario blocks across sessions +- **L3** maintains a user profile for long-term preference modeling + +## Lifecycle + +| Stage | Trigger | Action | +|---|---|---| +| Recall | `before_prompt_build` | Recall relevant memory and inject into context | +| L0 | `agent_end` | Write raw conversation logs | +| L1 | Scheduled | Extract structured memory, deduplicate, persist | +| L2 | After L1 | Update scenario blocks | +| L3 | Threshold reached | Generate or refresh user profile | +| Shutdown | `gateway_stop` | Clean up resources | + +The plugin also registers two tools for the Agent to call directly: + +- `tdai_memory_search`: queries L1 structured memory. Useful for questions like "what does the user prefer" or "what constraints were confirmed earlier". +- `tdai_conversation_search`: queries L0 raw conversations. Useful when exact original wording is needed. + +## Retrieval + +Three recall strategies: + +| Strategy | Implementation | +|---|---| +| `keyword` | FTS5 full-text search with jieba for Chinese tokenization | +| `embedding` | sqlite-vec vector similarity search | +| `hybrid` | Merged keyword and vector results | + +All backed by SQLite. + +## Configuration + +Grouped by capability: + +| Config Group | Purpose | +|---|---| +| `capture` | L0 conversation capture, exclusion rules, retention | +| `extraction` | L1 extraction, deduplication, per-run limit | +| `persona` | L2/L3 trigger frequency, scenario limit, backup count | +| `pipeline` | L1/L2/L3 scheduling | +| `recall` | Auto-recall toggle, result count, threshold, strategy | +| `embedding` | Vector retrieval service configuration | +| `report` | Metrics logging | + +Minimum configuration is just installing the plugin. Add `embedding` and scheduling parameters for better recall quality. + +## Data Directory + +```text +/ +├── conversations/ # L0 raw conversations +├── records/ # L1 structured memory +├── scene_blocks/ # L2 scenario blocks +├── .metadata/ # checkpoints, indexes, metadata +└── .backup/ # backups +``` + +## Scope + +This repository is the core OpenClaw plugin implementation. + +**Includes**: plugin entry and lifecycle hooks, 4-layer memory pipeline, retrieval tools and auto-recall, JSONL + SQLite local storage, checkpoint / backup / cleanup / logging. + +### Code Structure + +```text +TencentDB-Agent-Memory/ +├── index.ts # Plugin registration, tool registration, lifecycle hooks +├── openclaw.plugin.json +├── package.json +├── CHANGELOG.md +└── src/ + ├── hooks/ # Auto-recall and auto-capture + ├── conversation/ # L0 conversation management + ├── record/ # L1 extraction and persistence + ├── scene/ # L2 scenario synthesis + ├── persona/ # L3 user profile + ├── store/ # SQLite / FTS / vector retrieval + ├── tools/ # Retrieval tool registration + ├── prompts/ # Prompt templates + ├── report/ # Metrics reporting + └── utils/ +``` + +## License + +MIT. See [LICENSE](LICENSE). diff --git a/README_CN.md b/README_CN.md new file mode 100644 index 0000000..57adef6 --- /dev/null +++ b/README_CN.md @@ -0,0 +1,201 @@ +[English](README.md) + +

TencentDB Agent Memory

+ +

没有记忆的AI，只是工具；有记忆的AI，才是资产。

+ +

+ OpenClaw Plugin + MIT License +

+ + +**TencentDB Agent Memory是腾讯云数据库团队自研的 Agent 记忆系统**，为 OpenClaw 补上长期连续的记忆。通过四层渐进式记忆金字塔架构，自动完成记忆写入、分层提炼、按需召回与注入，让 Agent 从“只能聊天对话”进化为“持续学习、更懂你、跨会话不断线的长期可依赖 AI 助理”。 + +## 评测 + +基于 [PersonaMem](https://github.com/jiani-huang/PersonaMem)（UPenn，COLM 2025）评测集，589 道题，20 个角色。 + +| 题型 | OpenClaw 原生记忆 | TencentDB Agent Memory | +| :----------- | :---------------: | :---------: | +| 召回更新原因 | 70.97% | **88.89%** | +| 偏好演变跟踪 | 66.67% | **83.45%** | +| 个性化推荐 | 46.67% | **76.36%** | +| 场景泛化 | 31.58% | **78.95%** | +| 召回用户事实 | 29.63% | **79.07%** | +| 召回事实 | 25.00% | **76.47%** | +| 创意建议 | 24.00% | **45.16%** | +| **总计** | **47.85%** | **76.10%** | + +## 主要特点 + +- **OpenClaw 原生插件**，包名 `@tencentdb-agent-memory/memory-tencentdb`，一行命令即可安装 +- **四层记忆链路**：L0 原始对话 → L1 结构化记忆 → L2 场景归纳 → L3 用户画像 +- **混合召回**：支持 `keyword`、`embedding`、`hybrid` 三种策略 +- **两类检索工具**：`tdai_memory_search`（查结构化记忆）和 `tdai_conversation_search`（查原始对话） +- **本地优先存储**：JSONL + SQLite，数据在本地可直接查看和排查 +- **工程化能力**：去重、checkpoint、备份、定时清理、指标日志 +- **MIT 许可证** + +## 快速开始 + +### 环境要求 + +- Node.js `>= 22.16.0` +- OpenClaw `>= 2026.3.13` + +### 安装 + +```bash +openclaw plugins install @tencentdb-agent-memory/memory-tencentdb +``` + +安装完成后，插件接入 OpenClaw 对话生命周期，自动执行对话捕获、记忆召回和 L1/L2/L3 后续处理。 + +### 从源码开发 + +本项目无需编译。Node.js 22.16+ 原生支持 TypeScript 类型剥离，OpenClaw 直接加载 `.ts` 源码运行。 + +```bash +git clone https://github.com/TencentCloud/TencentDB-Agent-Memory.git +cd TencentDB-Agent-Memory +npm install +openclaw plugins install --link . +``` + +`install --link` 会将当前目录作为本地插件注册到 OpenClaw，修改源码后重启 Gateway 即可生效。 + +### 可选：开启 embedding 召回 + +如果需要向量检索或混合召回，补充 embedding 配置即可。当前支持兼容 OpenAI API 的远程 embedding 服务。 + +```jsonc +{ + "plugins": { + "entries": { + "memory-tencentdb": { + "enabled": true, + "config": { + "embedding": { // 需配置自定义Embedding模型信息，非LLM模型 + "enabled": true, // 是否启用向量搜索 + "provider": "openai", // 暂只支持OpenAI兼容的协议 + "baseUrl": "https://xxx", // API Base URL + "apiKey": "xxx", // API Key + "model": "text-embedding-3-large", // 模型名称 + "dimensions": 1024 // 向量维度（需与所选模型匹配） + } + } + } + } + } +} + +``` + + +## 架构 + +```text + ┌─────────────────┐ + │ L3 用户画像 │ 偏好与行为模式 + ├─────────────────┤ + │ L2 场景归纳 │ 跨会话的任务 / 场景块 + ├─────────────────┤ + │ L1 结构化记忆 │ 事实、约束、偏好、决策 + ├─────────────────┤ + │ L0 原始对话 │ 完整对话记录 + └─────────────────┘ +``` + +各层各有侧重： + +- **L0** 保留原始对话，用于回溯和精确检索 +- **L1** 抽取高价值信息，直接用于召回 +- **L2** 将零散记忆整理成场景块，跨会话聚合 +- **L3** 维护用户画像，用于长期偏好建模 + +## 生命周期 + +| 阶段 | 触发时机 | 动作 | +|---|---|---| +| Recall | `before_prompt_build` | 召回相关记忆，注入上下文 | +| L0 | `agent_end` | 写入原始对话 | +| L1 | 调度触发 | 提取结构化记忆，去重，持久化 | +| L2 | L1 完成后 | 更新场景块 | +| L3 | 达到阈值 | 生成或刷新用户画像 | +| Shutdown | `gateway_stop` | 清理资源 | + +插件还注册了两个 tool 供 Agent 主动调用： + +- `tdai_memory_search`：查 L1 结构化记忆。适合"用户偏好什么""之前确认过哪些约束"类问题。 +- `tdai_conversation_search`：查 L0 原始对话。适合需要原始措辞的场景。 + +## 检索实现 + +三种召回策略： + +| 策略 | 实现 | +|---|---| +| `keyword` | FTS5 全文检索，中文分词基于 jieba | +| `embedding` | sqlite-vec 向量相似度检索 | +| `hybrid` | 融合关键词与向量结果 | + +底层存储统一用 SQLite。 + +## 配置 + +按能力分组： + +| 配置组 | 作用 | +|---|---| +| `capture` | L0 对话捕获、排除规则、保留时间 | +| `extraction` | L1 提取、去重、单次上限 | +| `persona` | L2/L3 触发频率、场景上限、备份数量 | +| `pipeline` | L1/L2/L3 调度节奏 | +| `recall` | 自动召回开关、结果数、阈值、策略 | +| `embedding` | 向量检索服务配置 | +| `report` | 指标日志 | + +最小配置只需要安装插件。如果需要更好的召回效果，再加 `embedding` 和调度参数。 + +## 数据目录 + +```text +/ +├── conversations/ # L0 原始对话 +├── records/ # L1 结构化记忆 +├── scene_blocks/ # L2 场景块 +├── .metadata/ # checkpoint、索引、元数据 +└── .backup/ # 备份 +``` + +## 仓库范围 + +当前仓库是 OpenClaw 插件的核心实现。 + +**包含**：插件入口与生命周期钩子、四层记忆链路、检索工具与自动召回、JSONL + SQLite 本地存储、checkpoint / 备份 / 清理 / 日志。 + +### 代码结构 + +```text +TencentDB-Agent-Memory/ +├── index.ts # 插件注册、工具注册、生命周期接入 +├── openclaw.plugin.json +├── package.json +├── CHANGELOG.md +└── src/ + ├── hooks/ # 自动召回与自动捕获 + ├── conversation/ # L0 对话管理 + ├── record/ # L1 提取与持久化 + ├── scene/ # L2 场景归纳 + ├── persona/ # L3 用户画像 + ├── store/ # SQLite / FTS / 向量检索 + ├── tools/ # 检索工具注册 + ├── prompts/ # prompt 模板 + ├── report/ # 指标上报 + └── utils/ +``` + +## 许可证 + +MIT。详见 [LICENSE](LICENSE)。 \ No newline at end of file