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TencentDB Agent Memory

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

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.