External Reranker Plugin for OpenClaw Memory-Core: Repurpose Old GPUs
A developer has released a plugin for OpenClaw that allows memory-core to use an external reranker, moving the MMR algorithm into a plugin and adding a new memory-external-reranker plugin. If you've got an old GPU collecting dust, this is a practical way to put it to work.
How It Works
The design refactors MMR into a separate plugin called memory-mmr, and adds memory-external-reranker as another plugin. With no changes to openclaw.json, the new memory-mmr plugin loads automatically if hybrid memory is enabled. To enable the external reranker, follow the docs in the repo. There's also a fallback to MMR if the reranker is unavailable.
Tested Setup
- Embeddings model:
Qwen/Qwen3-Embedding-4B-GGUF:Q8_0 - Reranker model:
mradermacher/Qwen3-Reranker-4B-GGUF:Q8_0 - Fallback: MMR
- GPU: RTX 3060 (12GB VRAM) — both models run together comfortably
Why Not QMD?
QMD runs locally with no ability to offload the model. If you run OpenClaw in Docker on a host without a usable GPU (or don't want CPU inference), QMD is a performance hit. This plugin lets you use external reranking with models served elsewhere (including that old RTX 3060 sitting idle).
Is It Worth It?
The author reports that memory recall feels improved, though they don't have metrics to quantify it. For setups with a spare GPU or separate inference server, this plugin offers a clean way to offload reranking without changing core memory-core behavior.
Repo and Branch
The plugin is in the external-reranker branch of the fork: github.com/michmill1970/openclaw/tree/external-reranker. The author keeps the fork close to main, updating a few times a day.
📖 Read the full source: r/openclaw
👀 See Also
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