OpenClaw SiliconFlow Memory#

Goal#

Configure OpenClaw memory_search to use SiliconFlow embeddings reliably, then verify indexing and retrieval.

Use this skill to:

• switch memory embeddings to SiliconFlow
• standardize on BAAI/bge-m3
• set fallback: "none" for clearer debugging
• add curated external Markdown paths to semantic memory
• diagnose failed indexing or failed retrieval

Workflow#

1. Inspect the memory config surface first#

Before changing config, inspect the relevant schema paths:

• agents.defaults.memorySearch
• agents.defaults.memorySearch.remote
• agents.defaults.memorySearch.extraPaths

Then inspect the current config with gateway config.get so the patch is based on the latest hash.

Important:

• Configure memory under agents.defaults.memorySearch, not top-level memorySearch.
• For OpenAI-compatible embedding endpoints, set remote.apiKey explicitly.

2. Apply the recommended memorySearch config#

Use gateway config.patch and keep the configuration explicit.

Recommended baseline:

json5

agents: {
  defaults: {
    memorySearch: {
      provider: "openai",
      model: "BAAI/bge-m3",
      fallback: "none",
      remote: {
        baseUrl: "https://api.siliconflow.cn/v1/",
        apiKey: "YOUR_SILICONFLOW_API_KEY"
      }
    }
  }
}

Why this baseline works well:

• provider: "openai" matches SiliconFlow's OpenAI-compatible embeddings API
• model: "BAAI/bge-m3" is a strong default for mixed Chinese/English Markdown recall
• fallback: "none" avoids silently switching providers during debugging
• explicit remote.baseUrl and remote.apiKey remove ambiguity

3. Add external knowledge paths conservatively#

When indexing a local knowledge base, prefer curated paths instead of adding the entire repo at once.

Good first candidates:

• source notes
• concepts
• project overviews
• indexes
• README-style top-level knowledge summaries

Avoid adding these in the first pass unless the user explicitly wants them indexed:

• inbox or capture folders
• daily journals
• templates
• archives
• noisy exports or generated files

Example:

json5

agents: {
  defaults: {
    memorySearch: {
      extraPaths: [
        "D:/Knowledge/README.md",
        "D:/Knowledge/Notes/02-Sources",
        "D:/Knowledge/Notes/03-Concepts",
        "D:/Knowledge/Notes/05-Projects",
        "D:/Knowledge/Notes/09-Indexes"
      ]
    }
  }
}

4. Validate in this order#

After config reload/restart, validate in a fixed order:

1. openclaw memory status --deep
2. openclaw memory index --force
3. openclaw memory search "<known phrase from indexed docs>"
4. tool-layer memory_search with the same query

Success signs:

• Embeddings: ready
• Indexed: N/N files increases as expected
• Dirty: no
• retrieval returns snippets from the intended external paths

5. Use known text for verification#

Do not validate with vague keywords only.

Prefer a phrase that appears verbatim in the indexed notes, such as:

• a README opening sentence
• a project title
• a section heading
• a unique domain phrase

This makes it easier to distinguish:

• indexing failure
• retrieval quality issues
• query mismatch

Common failures#

404 page not found#

Interpret this as: the endpoint likely does not provide embeddings at the configured route.

Typical cause:

• using a chat/completions proxy that does not expose /embeddings

Action:

• switch to SiliconFlow's embeddings endpoint
• confirm remote.baseUrl is https://api.siliconflow.cn/v1/
• confirm the embedding model name is valid

fetch failed#

Interpret this as: the request could not complete at all.

Typical causes:

• bad API key
• unreachable endpoint
• transient network issue
• provider auto-detection picked the wrong backend

Action:

• make provider, model, remote.baseUrl, and remote.apiKey explicit
• retry openclaw memory status --deep

EBUSY: resource busy or locked#

Interpret this as: the SQLite memory store is locked during reindex.

Action order:

1. retry the index once after current memory operations settle
2. restart OpenClaw and retry
3. if the lock persists, reboot the machine and retry

Do not assume config is wrong just because reindex failed with EBUSY.

Recommended operating style#

• keep the embedding provider explicit
• keep fallback: "none" during setup and debugging
• add knowledge paths in small batches
• validate with both CLI and tool-layer retrieval
• prefer high-signal Markdown sources over raw dumps

Completion checklist#

Before declaring success, confirm all of the following:

• SiliconFlow API key is configured
• BAAI/bge-m3 is active
• openclaw memory status --deep shows Embeddings: ready
• reindex succeeds
• indexed file count reflects the intended extra paths
• at least one query returns a result from the external knowledge base