腾讯DB智能代理

1.3 Enable short-term compression (optional, requires version ≥ 0.3.4)

{
  "memory-tencentdb": {
    "config": {
      "offload": {
        "enabled": true
      }
    }
  }
}

Step 1 — Register the slot in your plugin config

Add the slots field so OpenClaw routes context-offload requests to this plugin:

{
  "plugins": {
    "slots": {
      "contextEngine": "memory-tencentdb"
    }
  }
}

Step 2 — Apply the runtime patch

For the best results, run the patch script below. It hooks after-tool-call messages so they can be offloaded and recovered correctly:

bash scripts/openclaw-after-tool-call-messages.patch.sh

💡 The patch only needs to be applied once per OpenClaw installation. After upgrading OpenClaw, re-run the script to re-apply.

1.1 Install the plugin

openclaw plugins install @tencentdb-agent-memory/memory-tencentdb
openclaw gateway restart

Please use the native OpenClaw command to upgrade the plugin. This approach prevents the plugin from being disabled caused by semantic version ranges.

> openclaw plugins update @tencentdb-agent-memory/memory-tencentdb
> 

============ docker run Flags ============

--name hermes-memory Container name, for later docker exec / logs / stop

Enter the Docker build directory (already cloned the repo and at the repo root)

cd docker/opensource

Build

docker build -f Dockerfile.hermes -t hermes-memory .

Quick Start

🎬 Demos

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3. Production-Ready Engineering: Not a Demo

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1.2 Zero-config to enable

Defaults to a local SQLite + sqlite-vec backend.

// ~/.openclaw/openclaw.json
{
  "memory-tencentdb": {
    "enabled": true
  }
}

Once enabled, TencentDB Agent Memory automatically handles conversation capture, memory extraction, scene aggregation, persona generation, and recall before the next turn.

============ Configuration Parameters ============

-e MODEL_* Inject the config parameters above as env vars

🔒 Gateway Security (optional)

The Hermes Gateway listens on :8420 and exposes capture / search / recall HTTP endpoints. Two opt-in switches let you turn it from "open localhost sidecar" into "authenticated network service". Both default to off so existing deployments keep working unchanged.

When apiKey is unset, the gateway prints a startup WARN. If it is bound to a non-loopback host (e.g. 0.0.0.0) without an apiKey, a second louder warning is emitted.

Clients call protected routes with a Bearer token:

curl -H "Authorization: Bearer $TDAI_GATEWAY_API_KEY" \
     -H "Content-Type: application/json" \
     -d '{"query":"...","session_key":"..."}' \
     http://127.0.0.1:8420/recall

GET /health stays open without a token so orchestrator probes (docker healthcheck, kubectl liveness) keep working.

🔧 Configurable Parameters

Every field has a sensible default — it runs with zero configuration. When you want to tune, peel back the layers based on how deep you go.

<details> <summary><b>🟢 Level 1 · Daily tuning</b> (covers 90% of use cases)</summary>

</details>

<details> <summary><b>🟡 Level 2 · Advanced tuning</b> (long task / long session)</summary>

</details>

<details> <summary><b>🔴 Level 3 · Full parameter reference</b> (ops / custom models / remote embedding)</summary>

For all fields, types, and constraints see openclaw.plugin.json。

- embedding.* — remote embedding service (OpenAI-compatible API) - embedding.sendDimensions (default true): whether to include the dimensions field in the request body. OpenAI text-embedding-3-* models rely on it for Matryoshka truncation, but some self-hosted / OSS models (e.g. BGE-M3) do not support custom dimensions and will reject the request with HTTP 400 does not support matryoshka representation. Set it to false for those backends, e.g.:

    {
      "embedding": {
        "enabled": true,
        "provider": "openai",
        "baseUrl": "http://your-host:your-port/v1",
        "apiKey": "<KEY>",
        "model": "bge-m3",
        "dimensions": 1024,
        "sendDimensions": false
      }
    }
    

</details>

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MODEL_API_KEY LLM API key (required) — replace with your own credential

MODEL_BASE_URL LLM endpoint, defaults to Tencent Cloud LKE (Large Model Knowledge Engine)

MODEL_PROVIDER Provider type: "custom" works for any OpenAI-compatible endpoint

MODEL_API_KEY="your-api-key" MODEL_BASE_URL="https://api.lkeap.cloud.tencent.com/v1" MODEL_NAME="deepseek-v3.2" MODEL_PROVIDER="custom"

Hermes plugin side

The Hermes memory_tencentdb plugin is a client of the Gateway. To make it talk to a Gateway that has auth enabled, set:

export MEMORY_TENCENTDB_GATEWAY_API_KEY="<same-secret-as-gateway>"

The plugin will then attach Authorization: Bearer <key> to every request it sends to the Gateway. If the variable is unset, the plugin sends no auth header — which matches the Gateway's legacy default and is fine for a Gateway that has not opted into TDAI_GATEWAY_API_KEY.

Important: the plugin only handles the client half. Whether the Gateway actually enforces a Bearer check is decided on the Gateway side (TDAI_GATEWAY_API_KEY / server.apiKey). Configure the same secret on both ends — the plugin does not propagate the secret across, since the Gateway might be started by Docker, systemd, or any other means outside the plugin's control.

If MEMORY_TENCENTDB_GATEWAY_API_KEY is unset, the plugin also looks at TDAI_GATEWAY_API_KEY as a fallback — handy when both processes share an env file and the operator only wants to set one variable name. The Gateway never reads MEMORY_TENCENTDB_GATEWAY_API_KEY; that name is plugin-side only.

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