Secondary Filtering for Memory Recall

Cloud Plugin

The MemOS Openclaw cloud plugin supports secondary filtering of recalled memories with a specified large language model. After filtering, only memories that are highly relevant to the current task are injected into context, which reduces irrelevant noise and saves tokens.

How to Use

Just configure an OpenAI-compatible model endpoint (such as local Ollama or a third-party LLM API) and enable the filter switch to turn on secondary memory filtering.

1. Enable Memory Filtering

When configuring an LLM for memory filtering, you must configure the API Key and Base URL.

Add the following in your openclaw.json config:

{
  "plugins": {
    "entries": {
      "memos-cloud-openclaw-plugin": {
        "config": {
          "recallFilterEnabled": true,
          "recallFilterBaseUrl": "http://127.0.0.1:11434/v1",
          "recallFilterApiKey": "sk-...",
          "recallFilterModel": "qwen2.5_7b"
        }
      }
    }
  }
}

Or set environment variables:

MEMOS_RECALL_FILTER_ENABLED=true
MEMOS_RECALL_FILTER_BASE_URL="http://127.0.0.1:11434/v1"
MEMOS_RECALL_FILTER_API_KEY="sk-..."
MEMOS_RECALL_FILTER_MODEL="qwen2.5_7b"

2. Configure Authentication and Advanced Parameters (Optional)

If you need to adjust timeout and failure strategy, you can specify them in the config:

{
  "config": {
    "recallFilterTimeoutMs": 6000,
    "recallFilterFailOpen": true
  }
}

How It Works

• Post-recall interception: Before each conversation round, after memories are recalled from the cloud, the plugin sends candidate memory entries to your configured filtering model for secondary screening.
• Precise retention: After model judgment, only entries marked as keep are retained and injected into the agent context.
• High-availability fallback: Fail-open (recallFilterFailOpen: true) is enabled by default. If the filtering model times out or fails, it automatically falls back to full injection without filtering, so the current conversation is not interrupted.

Typical Use Cases

• Pruning long-term memory: In long-running conversations with many accumulated memories, remove content unrelated to the current prompt to significantly reduce main-model context token usage.
• Improving reasoning accuracy: For agents handling complex tasks, filter out early irrelevant memories to improve reasoning quality on the core task.
• Working with local models: Use a locally running small model (such as qwen2.5_7b via Ollama) as a low-cost pre-filter to improve memory injection quality without increasing main-model API costs.

Local Plugin

@memtensor/memos-local-plugin includes multi-stage local retrieval filtering. It first recalls candidates from Skill, Trace/Episode, and World Model tiers, then applies RRF + MMR for fusion and deduplication. If an LLM is configured, it can also run a final relevance check before injection to drop items that only share surface keywords with the current task.

How to Configure

Configure this directly in the Memory Viewer for the target agent:

Steps:

1. Open the Memory Viewer.
2. Go to Settings → AI Models.
3. In the LLM section, choose a provider and fill in endpoint, API Key, model, and related fields.
4. Click Test to confirm the model works.
5. Save the settings. The Viewer restarts the plugin and loads the new config.

After saving, local retrieval can use that LLM for a relevance check after recall and RRF/MMR ranking. If no LLM is configured, the plugin still uses built-in multi-channel recall and mechanical threshold filtering.

Local Retrieval Flow

User request
→ Build retrieval query and tags
→ Tier 1: Skill candidates
→ Tier 2: Trace / Episode candidates
→ Tier 3: World Model candidates
→ Multi-channel recall: vector / FTS5 / pattern / error signatures
→ RRF fusion + MMR diversity control
→ Optional LLM relevance check
→ Inject into the agent

Expected Results

• Injected memory context is more focused and less noisy
• Skill, Trace/Episode, and World Model hits are not selected by vector similarity alone
• If the LLM is unavailable, retrieval falls back to stricter mechanical thresholds without breaking basic recall