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memory-tencentdb

四层记忆系统插件 — 自动捕获对话、提取结构化记忆、归纳场景、生成用户画像。支持 L0→L1→L2→L3 渐进式管线，本地 SQLite + 腾讯云向量数据库双后端。

OpenClaw
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Rank

62

Safety

84

Downloads

982

Updated

Oct 11, 2026

Version

0.3.7

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 982 downloads reported by the source. Last updated 10/11/2026.

Avoid when

  • Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor
Clawhubvendor · observed Oct 11, 2026
Protocol compatibility
OpenClawcompatibility · observed Oct 11, 2026
Adoption signal
982 downloadsadoption · observed Oct 11, 2026
Latest release
0.3.7release · observed Jun 8, 2026
Handshake status
UNKNOWNsecurity

What changed

Newest first. Only changes that point at a source are listed.

  • Release 0.3.7release

    Remove standalone LLM mode use Gateway built-in model

    Observed Jun 8, 2026 · source

Install and run

Setup complexity: low.

clawhub skill install s172x1s50dh0bgmm3yc9516nr586kk4b:memory-tencentdb
  1. Install using `clawhub skill install s172x1s50dh0bgmm3yc9516nr586kk4b:memory-tencentdb` in an isolated environment before connecting it to live workloads.
  2. No published capability contract is available yet, so validate auth and request/response behavior manually.
  3. Review the upstream CLAWHUB listing at https://clawhub.ai/paudyyin/memory-tencentdb before using production credentials.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-paudyyin-memory-tencentdb/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

150,505 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: memory-tencentdb
description: 四层记忆系统插件 — 自动捕获对话、提取结构化记忆、归纳场景、生成用户画像。支持 L0→L1→L2→L3 渐进式管线，本地 SQLite + 腾讯云向量数据库双后端。
version: 0.3.6
tags: ["memory", "long-term-memory", "persona", "sqlite", "vector-search"]
author: paudyyin
---

# memory-tencentdb — 四层记忆系统

为 OpenClaw AI Agent 提供 L0→L1→L2→L3 长期记忆能力。

## v0.3.6 变更（2026-06-08）

- **移除独立 LLM 模式**：删除 `llm.enabled` 配置和 StandaloneLLMRunner，L1/L2/L3 提取统一走 Gateway 内置模型（runEmbeddedPiAgent）
- **简化配置**：不再需要单独配置 LLM API 端点和 Key，复用 Gateway 已有的模型认证
- **修复 L1 提取静默失败**：之前独立 LLM 因 apiKey 为空返回 401，导致提取管线完全停止

## 架构

```
对话开始
  → Auto-Recall: 向量/混合搜索相关记忆 + 加载 Persona → 注入系统上下文

对话结束
  → L0: 录制对话消息 → SQLite + JSONL 双写
  → Pipeline Scheduler: 达到 N 轮后触发
     ├── L1: LLM 提取结构化记忆 + 向量去重 → JSONL + SQLite
     ├── L2: LLM 归纳场景块 → Markdown 文件
     └── L3: LLM 生成/更新用户画像 → persona.md
```

## 四层记忆

| 层级 | 名称 | 说明 |
|------|------|------|
| L0 | 对话录制 | 原始对话消息捕获 |
| L1 | 记忆提取 | 结构化记忆（persona/episodic/instruction） |
| L2 | 场景归纳 | 场景块（Scene Block） |
| L3 | 用户画像 | 用户人格画像（Persona） |

## 记忆类型

- **persona**：用户的稳定属性、偏好、技能、价值观
- **episodic**：客观发生的动作、决定、计划或结果
- **instruction**：用户对 AI 的长期行为规则

## 安装

```bash
clawhub install memory-tencentdb
```

## 配置

在 openclaw.json 中添加：

```json
{
  "plugins": {
    "allow": ["mx", "memory-tencentdb"],
    "load": {
      "paths": ["<插件路径>"]
    },
    "entries": {
      "memory-tencentdb": {
        "enabled": true,
        "config": {
          "pipeline": {
            "everyNConversations": 3,
            "enableWarmup": true,
            "l1IdleTimeoutSeconds": 10
          }
        }
      }
    }
  }
}
```

> **注意**：v0.3.6 起不再需要 `llm` 配置段，L1/L2/L3 提取自动使用 Gateway 内置模型。

## 存储后端

- `sqlite`（默认）：本地 SQLite + sqlite-vec
- `tcvdb`：腾讯云向量数据库

## CLI 命令

```bash
openclaw memory-tdai seed --input conversations.json
```

## 故障排查

如果 L1 提取为空：
1. 检查 `plugins.allow` 包含 `"memory-tencentdb"`
2. 检查 `plugins.load.paths` 包含插件路径
3. 检查 Gateway 模型配置正常（`models.providers` 和 `agents.defaults.model`）
4. 检查 vectors.db 中 l1_fts 与 l1_records 行数一致

hermes-plugin/memory/memory_tencentdb/README.md

# memory-tencentdb Memory Provider (Hermes)

Hermes-side [`MemoryProvider`](../../../../../hermes-agent/agent/memory_provider.py)
adapter for the **memory-tencentdb** four-layer memory system
(L0 conversation capture → L1 episodic extraction → L2 scene blocks → L3 persona synthesis).

The heavy lifting — capture, extraction, storage, recall, pipeline scheduling —
runs in a Node.js **Gateway** sidecar (shipped by the same package as the
OpenClaw plugin). This Python provider is a thin HTTP client + process
supervisor that plugs the Gateway into Hermes's lifecycle.

## Architecture

```
Hermes Agent (Python)
  └─ MemoryManager
       └─ MemoryTencentdbProvider        (this directory)
            ├─ GatewaySupervisor          — starts / health-checks the sidecar
            └─ MemoryTencentdbSdkClient   — POST /recall, /capture, /search/*, /session/end
                    │
                    ▼  HTTP (127.0.0.1:8420 by default)
            memory-tencentdb Gateway (Node.js)
               └─ memory-tencentdb Core
                    ├─ L0  Conversation store      (SQLite / TCVDB + JSONL)
                    ├─ L1  Episodic extraction     (LLM + vector dedup)
                    ├─ L2  Scene blocks            (Markdown under data dir)
                    ├─ L3  Persona synthesis       (persona.md)
                    └─ Storage backends: SQLite + sqlite-vec  OR  Tencent VectorDB
```

Hermes lifecycle → Gateway mapping:

| Hermes hook / call          | Gateway endpoint | Behavior                                                   |
|-----------------------------|------------------|------------------------------------------------------------|
| `prefetch(query)`           | `POST /recall`   | Synchronous. Returns `<memory-context>` text for injection |
| `sync_turn(user, assistant)`| `POST /capture`  | Fire-and-forget on a background daemon thread (max 4 in-flight) |
| `shutdown()` / `on_session_end` | `POST /session/end` | Flush pending pipeline work                             |
| `get_tool_schemas()`        | —                | Advertises two LLM tools (see below)                       |

Reliability features baked into the provider:

- **Circuit breaker** — 5 consecutive Gateway failures → pause all calls for 60 s.
- **Back-pressure on capture** — at most 4 in-flight `sync_turn` threads; a 5th
  waits up to 5 s for the oldest one before starting (Gateway hangs can't grow
  threads unboundedly).
- **Supervised startup** — if `MEMORY_TENCENTDB_GATEWAY_CMD` is set (or the
  provider auto-discovers `src/gateway/server.ts`, see below), it starts the
  sidecar, polls `/health` for up to 30 s, and tails `gateway.stderr.log` on
  crash for diagnostics.
- **Zero-config auto-discovery** — when `MEMORY_TENCENTDB_GATEWAY_CMD` is
  unset, the provider looks for `src/gateway/server.ts` next to the plugin
  checkout (in-tree) and, as a last resort, under
  `~/.memory-tencentdb/tdai-memory-openclaw-plugin/` (preferred),
  `~/tdai-memory-openclaw-plugin/` (legacy),

README.md

# @tencentdb-agent-memory/memory-tencentdb

**Four-layer memory system plugin for [OpenClaw](https://github.com/openclaw/openclaw).**

为 AI Agent 提供长期记忆能力。通过 L0→L1→L2→L3 四层渐进式管线，自动将对话内容提炼为结构化记忆、场景块和用户画像。支持纯本地 SQLite 和远端腾讯云向量数据库（TCVDB）两种存储后端。

## ✨ 核心功能

- **L0 — 对话录制**：自动捕获每轮对话原始消息，IMemoryStore + JSONL 双写
- **L1 — 记忆提取**：由 LLM 从对话中提取结构化记忆，支持向量去重与冲突检测
- **L2 — 场景归纳**：基于 L1 记忆自动归纳场景块（Scene Block），由 LLM 增量提取
- **L3 — 用户画像**：基于场景块自动生成/更新用户画像（Persona）
- **自动召回（Auto-Recall）**：对话开始前自动注入相关记忆和用户画像到上下文
- **多后端存储**：支持 `sqlite`（本地 SQLite + sqlite-vec）和 `tcvdb`（腾讯云向量数据库，服务端 embedding + hybridSearch）
- **BM25 稀疏向量**：内置 BM25 编码器（tcvdb-text），支持中英文混合搜索
- **关键词+向量混合搜索**：hybrid（关键词 + 向量 RRF 融合）搜索策略
- **语义搜索工具**：Agent 可调用 `tdai_memory_search`（L1 记忆搜索）和 `tdai_conversation_search`（L0 对话搜索）
- **Seed CLI**：`openclaw memory-tdai seed` 命令，支持导入历史对话数据（详见 [CLI 文档](src/cli/README.md)）
- **Session 隔离**：不同渠道/Agent 的对话独立调度、独立提取
- **本地数据清理**：可配置 L0/L1 数据保留天数，定时自动清理过期文件
- **Manifest 元数据**：数据目录自动生成 `.metadata/manifest.json`，记录 store 绑定信息和 seed 运行记录
- **支持零配置**：支持零配置工作，简单易用

## 🏗️ 关键原理

```
对话开始
  → Auto-Recall: 向量/混合搜索相关记忆 + 加载 Persona → 注入系统上下文

对话结束
  → Auto-Capture (L0): 录制对话消息 → IMemoryStore (SQLite/TCVDB) + JSONL 双写
  → Pipeline Scheduler: 达到 N 轮后按序触发 L1 → L2 → L3
     ├── L1: LLM 提取结构化记忆 + 向量去重 → 写入 JSONL + IMemoryStore
     ├── L2: LLM 归纳场景块 → Markdown 文件
     └── L3: LLM 生成/更新用户画像 → persona.md
```

### 数据目录结构

```
<pluginDataDir>/
├── conversations/     — L0 每日 JSONL 分片（每行一条消息）
├── records/           — L1 每日 JSONL 分片（提取的记忆）
├── scene_blocks/      — L2 场景块 .md 文件
├── vectors.db         — SQLite + vec0 向量数据库（仅 storeBackend=sqlite）
├── .metadata/
│   ├── manifest.json  — 数据目录元数据（store 绑定、seed 信息）
│   └── checkpoint.json
└── .backup/           — 滚动备份（persona, scene_blocks）
```

## 🚀 Quick Start（Docker）

使用预构建的 Docker 镜像，一行命令即可启动带记忆能力的 Hermes Agent：

```bash
docker run -d \
  --name hermes-memory \
  --restart unless-stopped \
  -p 8420:8420 \
  -e MODEL_API_KEY="your-api-key" \
  -e MODEL_BASE_URL="https://api.lkeap.cloud.tencent.com/v1" \
  -e MODEL_NAME="deepseek-v3.2" \
  -e MODEL_PROVIDER="custom" \
  -v hermes_data:/opt/data \
  agentmemory/hermes-memory:latest
```

镜像支持 `linux/amd64` 和 `linux/arm64` 架构。内置腾讯云 DeepSeek-V3.2 默认值，如使用该模型可省略 `MODEL_BASE_URL`/`MODEL_NAME`/`MODEL_PROVIDER`，只传 `MODEL_API_KEY` 即可。

### 环境变量

#### 统一模型配置（Hermes + TDAI 共用）

| 变量 | 默认值 | 说明 |
|------|--------|------|
| `MODEL_API_KEY` | - | LLM API Key（**必填**） |
| `MODEL_BASE_URL` | `https://api.lkeap.cloud.tencent.com/v1` | LLM API 地址 |
| `MODEL_NAME` | `deepseek-v3.2` | 模型名称 |
| `MODEL_PROVIDER` | `custom` | 模型 provider：`custom` / `openrouter` / `anthropic` / `openai` / `gemini` |

#### 服务配置

| 变量 | 默认值 | 说明 |
|------|--------|------|
| `TDAI_GATEWAY_PORT` | `8420` | Gateway 端口 |
| `TDAI_GATEWAY_HOST` | `0.0.0.0` | Gateway 绑定地址 |
| `TDAI_DATA_DIR` | `/opt/data/tdai-memory` | 记忆数据目录 |
| `HERMES_HOME` | `/opt/data` | Hermes 数据目录 |

### 验证运行

```bash
# 验证 Gateway 健康状态
curl http://localhost:8420/healt

scripts/migrate-sqlite-to-tcvdb/README.md

# SQLite → 腾讯云向量数据库迁移工具

离线迁移工具，用于将 memory-tdai 的数据从本地 SQLite 存储迁移到腾讯云向量数据库（TCVDB）。

## 前置条件

- Node.js >= 22.16.0
- 插件已通过 `openclaw plugins install` 安装
- 迁移脚本已编译（见下方）

## 编译

迁移脚本使用 TypeScript 编写，运行前需要先编译：

```bash
npm run build:migrate-sqlite-to-vdb
```

编译产物输出到 `scripts/migrate-sqlite-to-tcvdb/dist/`，可直接用 Node 运行。

## 使用方法

```bash
# 预检模式（仅查看源数据，不执行写入）
npm run migrate:sqlite-to-tcvdb -- \
  --plugin-data-dir ~/.openclaw/memory-tdai \
  --openclaw-config-path ~/.openclaw/openclaw.json \
  --tcvdb-url http://127.0.0.1:80 \
  --tcvdb-username root \
  --tcvdb-api-key-env TCVDB_API_KEY \
  --tcvdb-database agent_memory_prod \
  --tcvdb-embedding-model bge-large-zh \
  --dry-run

# 正式迁移
npm run migrate:sqlite-to-tcvdb -- \
  --plugin-data-dir ~/.openclaw/memory-tdai \
  --openclaw-config-path ~/.openclaw/openclaw.json \
  --tcvdb-url http://127.0.0.1:80 \
  --tcvdb-username root \
  --tcvdb-api-key-env TCVDB_API_KEY \
  --tcvdb-database agent_memory_prod \
  --tcvdb-embedding-model bge-large-zh \
  --yes
```

### 更多示例

```bash
# 直接传入 API Key（不通过环境变量）
npm run migrate:sqlite-to-tcvdb -- \
  --plugin-data-dir ~/.openclaw/memory-tdai \
  --openclaw-config-path ~/.openclaw/openclaw.json \
  --tcvdb-url http://127.0.0.1:80 \
  --tcvdb-username root \
  --tcvdb-api-key 'your-api-key-here' \
  --tcvdb-database agent_memory_prod \
  --tcvdb-embedding-model bge-large-zh \
  --yes
```

```bash
# 指定自定义 SQLite 路径（数据库不在默认 vectors.db 位置时）
npm run migrate:sqlite-to-tcvdb -- \
  --plugin-data-dir ~/.openclaw/memory-tdai \
  --sqlite-path /backup/2026-04/vectors-snapshot.db \
  --openclaw-config-path ~/.openclaw/openclaw.json \
  --tcvdb-url http://127.0.0.1:80 \
  --tcvdb-username root \
  --tcvdb-api-key-env TCVDB_API_KEY \
  --tcvdb-database agent_memory_prod \
  --tcvdb-embedding-model bge-large-zh \
  --yes
```

```bash
# 只迁移 L1 记忆层（跳过 L0 原始消息和 Profile）
npm run migrate:sqlite-to-tcvdb -- \
  --plugin-data-dir ~/.openclaw/memory-tdai \
  --openclaw-config-path ~/.openclaw/openclaw.json \
  --tcvdb-url http://127.0.0.1:80 \
  --tcvdb-username root \
  --tcvdb-api-key-env TCVDB_API_KEY \
  --tcvdb-database agent_memory_prod \
  --tcvdb-embedding-model bge-large-zh \
  --layers l1 \
  --yes
```

```bash
# 只迁移 L0 和 L1（不迁移 Profile）
npm run migrate:sqlite-to-tcvdb -- \
  --plugin-data-dir ~/.openclaw/memory-tdai \
  --openclaw-config-path ~/.openclaw/openclaw.json \
  --tcvdb-url http://127.0.0.1:80 \
  --tcvdb-username root \
  --tcvdb-api-key-env TCVDB_API_KEY \
  --tcvdb-database agent_memory_prod \
  --tcvdb-embedding-model bge-large-zh \
  --layers l0,l1 \
  --yes
```

```bash
# 英文语料场景：使用英文 BM25 分词
npm run migrate:sqlite-to-tcvdb -- \
  --plugin-data-dir ~/.openclaw/memory-tdai \
  --openclaw-config-path ~/.openclaw/openclaw.json \
  --tcvdb-url http://127.0.0.1:80 \
  --tcvdb-username root \
  --tcvdb-api-key-env TCVDB_API_KEY \
  --tcvdb-database agent_memory_prod \
  --tcvdb-embedding-model bge-large-en-v1.5 \
  --bm25-language en \
  --yes
```

```bash
# 禁用 B

src/cli/README.md

# memory-tdai CLI

`openclaw memory-tdai` 命令空间，提供离线数据管理工具。

## seed — 导入历史对话数据

将历史对话 JSON 文件导入到记忆管线中，完整执行 L0→L1→L2→L3 流程。适用于：

- 将已有对话数据灌入记忆系统
- 批量测试记忆提取效果
- 迁移/恢复记忆数据

### 用法

```bash
openclaw memory-tdai seed --input <file> [options]
```

### 参数

| 参数 | 必填 | 说明 |
|------|------|------|
| `--input <file>` | ✅ | 输入 JSON 文件路径 |
| `--output-dir <dir>` | — | 输出目录（默认自动生成带时间戳的目录） |
| `--session-key <key>` | — | 回退 session key（当输入数据缺少时使用） |
| `--config <file>` | — | 配置覆盖文件（JSON，与 openclaw.json 插件配置深度合并） |
| `--strict-round-role` | — | 严格校验每轮对话必须包含 user 和 assistant 消息 |
| `--yes` | — | 跳过交互确认（如时间戳自动填充确认） |

### 示例

```bash
# 基本用法
openclaw memory-tdai seed --input conversations.json

# 指定输出目录
openclaw memory-tdai seed --input data.json --output-dir ./seed-output

# 使用自定义配置覆盖（如调整 pipeline 参数）
openclaw memory-tdai seed --input data.json --config seed-config.json

# 跳过所有确认
openclaw memory-tdai seed --input data.json --yes

# 严格模式 + 自定义配置
openclaw memory-tdai seed --input data.json --config seed-config.json --strict-round-role --yes
```

### 输入文件格式

支持两种 JSON 格式：

#### Format A：对象包装

```json
{
  "sessions": [
    {
      "sessionKey": "user-alice",
      "sessionId": "conv-001",
      "conversations": [
        [
          { "role": "user", "content": "你好", "timestamp": 1711929600000 },
          { "role": "assistant", "content": "你好！有什么可以帮你？", "timestamp": 1711929601000 }
        ],
        [
          { "role": "user", "content": "今天天气怎么样？" },
          { "role": "assistant", "content": "今天晴天，适合出门。" }
        ]
      ]
    }
  ]
}
```

#### Format B：顶层数组

```json
[
  {
    "sessionKey": "user-alice",
    "conversations": [
      [
        { "role": "user", "content": "你好" },
        { "role": "assistant", "content": "你好！" }
      ]
    ]
  }
]
```

#### 字段说明

| 字段 | 类型 | 必填 | 说明 |
|------|------|------|------|
| `sessionKey` | string | ✅ | Session 标识（如用户 ID、频道名） |
| `sessionId` | string | — | 会话实例 ID（同一 sessionKey 下可有多个 sessionId） |
| `conversations` | message[][] | ✅ | 对话轮次数组，每个轮次是一组消息 |
| `role` | string | ✅ | 消息角色：`user` 或 `assistant` |
| `content` | string | ✅ | 消息内容 |
| `timestamp` | number \| string | — | 时间戳：epoch 毫秒或 ISO 8601 字符串。缺失时 seed 会提示自动填充 |

### 配置覆盖

`--config` 接受一个 JSON 文件，与 `openclaw.json` 中的插件配置**两级深度合并**：

- 顶层 key 如果两边都是对象 → 浅合并（保留 base 中未覆盖的字段）
- 其他类型 → 直接覆盖

常见场景：seed 时使用更激进的 pipeline 参数以加速处理：

```json
{
  "pipeline": {
    "everyNConversations": 3,
    "enableWarmup": false,
    "l1IdleTimeoutSeconds": 2,
    "l2DelayAfterL1Seconds": 1,
    "l2MinIntervalSeconds": 1,
    "l2MaxIntervalSeconds": 10
  }
}
```

如果需要 seed 到独立的 TCVDB 数据库：

```json
{
  "storeBackend": "tcvdb",
  "tcvdb": {
    "database": "my_seed_test_db"
  },
  "pipeline": {
    "everyNConversations": 3,
    "enableWarmup": false,
    "l1IdleTimeoutSeconds": 2
  }
}
```

### 输出目录结构

```
<output-dir>/
├── conversations/          — L0 JSONL 文件
├── records/                — L1 JSONL 文件
├── scene_blocks/           — L2 场景块
├── vectors.db              — SQLite 向量数据库（仅 sqli

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Machine-readable data

The same record, as JSON, for agents and crawlers.

{
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      "value": "Clawhub",
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}

Record generated Oct 12, 2026.

For crawlers

This page is free to read. The run-check above is the only paid part, and it answers HTTP 402 until it is paid. Everything else here is public.

  • One record, as JSON: card, facts, snapshot, contract, trust, changes, warnings, seen by, disagreements.
  • Every agent, one feed: /.well-known/ai-catalog.json
  • What this site sells, and the price: /.well-known/x402
  • Paid run-check: /api/v1/agents/clawhub-paudyyin-memory-tencentdb/run-check

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