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[中文](README_CN.md)
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<h1 align="center">TencentDB Agent Memory</h1>
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<p align="center">AI without memory is just a tool. AI with memory becomes an asset.</p>
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<p align="center">
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<a href="https://www.npmjs.com/package/@tencentdb-agent-memory/memory-tencentdb"><img src="https://img.shields.io/badge/OpenClaw-Plugin-6C63FF?logo=npm&logoColor=white" alt="OpenClaw Plugin" /></a>
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<a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-2EA043?logo=opensourceinitiative&logoColor=white" alt="MIT License" /></a>
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</p>
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**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.
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## Benchmark
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Evaluated on [PersonaMem](https://github.com/jiani-huang/PersonaMem) (UPenn, COLM 2025) — 589 questions, 20 actors.
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| Category | OpenClaw Native Memory | TencentDB Agent Memory |
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| :--- | :---: | :---: |
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| Recall Update Reason | 70.97% | **88.89%** |
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| Preference Evolution | 66.67% | **83.45%** |
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| Personalized Recommendation | 46.67% | **76.36%** |
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| Scenario Generalization | 31.58% | **78.95%** |
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| Recall User Facts | 29.63% | **79.07%** |
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| Recall Facts | 25.00% | **76.47%** |
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| Creative Suggestion | 24.00% | **45.16%** |
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| **Overall** | **47.85%** | **76.10%** |
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## Highlights
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- **OpenClaw native plugin** — package name `@tencentdb-agent-memory/memory-tencentdb`, one command to install
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- **4-layer memory pipeline**: L0 Raw Dialogue → L1 Structured Memory → L2 Scenario Synthesis → L3 User Profile
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- **Hybrid recall**: supports `keyword`, `embedding`, and `hybrid` strategies
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- **Two retrieval tools**: `tdai_memory_search` (structured memory) and `tdai_conversation_search` (raw conversations)
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- **Local-first storage**: JSONL + SQLite, data is directly inspectable on disk
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- **Operational features**: deduplication, checkpoint, backup, scheduled cleanup, metrics logging
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- **MIT License**
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## Quick Start
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### Requirements
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- Node.js `>= 22.16.0`
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- OpenClaw `>= 2026.3.13`
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### Install
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```bash
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openclaw plugins install @tencentdb-agent-memory/memory-tencentdb
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```
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Once installed, the plugin hooks into the OpenClaw conversation lifecycle and automatically handles conversation capture, memory recall, and L1/L2/L3 processing.
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### Development from Source
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No build step required. Node.js 22.16+ natively supports TypeScript type stripping, and OpenClaw loads `.ts` source files directly.
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```bash
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git clone https://github.com/TencentCloud/TencentDB-Agent-Memory.git
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cd TencentDB-Agent-Memory
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npm install
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openclaw plugins install --link .
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```
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`install --link` registers the current directory as a local plugin in OpenClaw. Source changes take effect after restarting the Gateway.
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### Optional: Enable Embedding Recall
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To use vector retrieval or hybrid recall, add an embedding configuration. Currently supports remote embedding services compatible with the OpenAI API.
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```jsonc
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{
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"plugins": {
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"entries": {
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"memory-tencentdb": {
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"enabled": true,
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"config": {
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"embedding": { // Embedding model config (not LLM model)
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"enabled": true, // Enable vector search
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"provider": "openai", // Only OpenAI-compatible API is supported
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"baseUrl": "https://xxx", // API Base URL
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"apiKey": "xxx", // API Key
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"model": "text-embedding-3-large", // Model name
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"dimensions": 1024 // Vector dimensions (must match the chosen model)
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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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## Architecture
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```text
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┌─────────────────┐
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│ L3 Profile │ Preferences & behavioral patterns
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├─────────────────┤
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│ L2 Scenarios │ Cross-session task / scenario blocks
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├─────────────────┤
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│ L1 Structured │ Facts, constraints, preferences, decisions
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├─────────────────┤
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│ L0 Dialogue │ Complete conversation records
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└─────────────────┘
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```
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Each layer serves a different purpose:
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- **L0** preserves raw conversations for replay and precise retrieval
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- **L1** extracts high-value information for direct recall
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- **L2** organizes scattered memories into scenario blocks across sessions
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- **L3** maintains a user profile for long-term preference modeling
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## Lifecycle
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| Stage | Trigger | Action |
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|---|---|---|
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| Recall | `before_prompt_build` | Recall relevant memory and inject into context |
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| L0 | `agent_end` | Write raw conversation logs |
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| L1 | Scheduled | Extract structured memory, deduplicate, persist |
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| L2 | After L1 | Update scenario blocks |
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| L3 | Threshold reached | Generate or refresh user profile |
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| Shutdown | `gateway_stop` | Clean up resources |
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The plugin also registers two tools for the Agent to call directly:
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- `tdai_memory_search`: queries L1 structured memory. Useful for questions like "what does the user prefer" or "what constraints were confirmed earlier".
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- `tdai_conversation_search`: queries L0 raw conversations. Useful when exact original wording is needed.
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## Retrieval
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Three recall strategies:
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| Strategy | Implementation |
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| `keyword` | FTS5 full-text search with jieba for Chinese tokenization |
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| `embedding` | sqlite-vec vector similarity search |
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| `hybrid` | Merged keyword and vector results |
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All backed by SQLite.
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## Configuration
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Grouped by capability:
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| Config Group | Purpose |
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|---|---|
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| `capture` | L0 conversation capture, exclusion rules, retention |
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| `extraction` | L1 extraction, deduplication, per-run limit |
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| `persona` | L2/L3 trigger frequency, scenario limit, backup count |
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| `pipeline` | L1/L2/L3 scheduling |
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| `recall` | Auto-recall toggle, result count, threshold, strategy |
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| `embedding` | Vector retrieval service configuration |
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| `report` | Metrics logging |
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Minimum configuration is just installing the plugin. Add `embedding` and scheduling parameters for better recall quality.
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## Data Directory
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```text
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<pluginDataDir>/
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├── conversations/ # L0 raw conversations
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├── records/ # L1 structured memory
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├── scene_blocks/ # L2 scenario blocks
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├── .metadata/ # checkpoints, indexes, metadata
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└── .backup/ # backups
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```
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## Scope
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This repository is the core OpenClaw plugin implementation.
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**Includes**: plugin entry and lifecycle hooks, 4-layer memory pipeline, retrieval tools and auto-recall, JSONL + SQLite local storage, checkpoint / backup / cleanup / logging.
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### Code Structure
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```text
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TencentDB-Agent-Memory/
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├── index.ts # Plugin registration, tool registration, lifecycle hooks
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├── openclaw.plugin.json
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├── package.json
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├── CHANGELOG.md
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└── src/
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├── hooks/ # Auto-recall and auto-capture
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├── conversation/ # L0 conversation management
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├── record/ # L1 extraction and persistence
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├── scene/ # L2 scenario synthesis
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├── persona/ # L3 user profile
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├── store/ # SQLite / FTS / vector retrieval
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├── tools/ # Retrieval tool registration
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├── prompts/ # Prompt templates
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├── report/ # Metrics reporting
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└── utils/
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```
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## License
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MIT. See [LICENSE](LICENSE).
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