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 (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

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.

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.

{
  "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

        ┌─────────────────┐
        │   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

<pluginDataDir>/
├── 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

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.