Environment

• OpenClaw version: 2026.4.25
• QMD version: 2.1.0 (bab86d5)
• Platform: Ubuntu 24.04, GCP e2-standard-4 (4 vCPU, 16 GB RAM)
• Workspace size: 37+ Markdown files in memory root + memory/ tree
• GGUF model: default (embeddinggemma-300m-qat-Q8_0.gguf, ~0.6 GB, auto-downloaded)
• searchMode: query (hybrid BM25 + vector)

Observed behavior

After enabling hybrid search (searchMode: "query"), the gateway emits this warning repeatedly every 2–4 minutes:

{"subsystem":"memory","message":"qmd embed failed (boot): Error: qmd embed timed out after 120000ms; backing off for 60s"}
{"subsystem":"memory","message":"qmd embed failed (interval): Error: qmd embed timed out after 120000ms; backing off for 120s"}

The embed process runs at 100–186% CPU on 4 vCPU for ~2 minutes before being killed. Peak RAM during GGUF model load: 9.6 GB. The embed never completes — every attempt times out at exactly 120 s. Vector search is effectively disabled.

Root cause

The GGUF embedding model takes 3–4 minutes on a 4-core CPU to embed a 37-file workspace. The default memory.qmd.update.embedTimeoutMs is 120 s — less than the actual embed duration.

The fix (memory.qmd.update.embedTimeoutMs: 600000) is only discoverable via openclaw config schema or the memory config reference page. The error message does not mention it.

Inconsistency with built-in engine

agents.defaults.memorySearch.sync.embeddingBatchTimeoutSeconds correctly differentiates:

• 600 s for local/self-hosted providers (local, ollama, lmstudio)
• 120 s for hosted providers (OpenAI, Gemini, etc.)

memory.qmd.update.embedTimeoutMs applies the same 120 s default regardless of whether the embedding model is a local GGUF or a hosted API. For local GGUF workloads, 120 s is consistently insufficient on commodity hardware.

Suggested fixes (pick one or combine)

1. Increase the default for embedTimeoutMs to 600 s (matching the built-in engine's local-provider default), or detect when searchMode is vsearch/query with the default GGUF model and apply a longer default automatically.

2. Improve the error message to mention the fix:

   qmd embed timed out after 120000ms — consider increasing memory.qmd.update.embedTimeoutMs (current default: 120000)
   

3. Add to the QMD troubleshooting docs a dedicated entry:

   qmd embed timed out after 120000ms? Increase memory.qmd.update.embedTimeoutMs. Default is 120 000 ms. On commodity hardware with the default GGUF model and a 30–50 file workspace, embedding takes 3–5 minutes. Set to 600000 or higher.

Workaround

{
  memory: {
    qmd: {
      update: {
        embedTimeoutMs: 600000,  // 10 minutes — sufficient for ~40 files on 4-core CPU
      },
    },
  },
}

Or use a smaller/faster GGUF model via QMD_EMBED_MODEL:

export QMD_EMBED_MODEL="hf:Qwen/Qwen3-Embedding-0.6B-GGUF/Qwen3-Embedding-0.6B-Q8_0.gguf"

Additional context

The gateway remains healthy throughout (Slack connected, Atlassian/GWS MCP tools working). The embed failures are WARN-level and non-blocking. However, with the default 120 s timeout, vector search is effectively permanently disabled on any commodity host with a medium or larger workspace, with no clear path to resolution from the error message alone.