aAAaqwq Memory Router — Team Unified Knowledge Retrieval Skill
- Author: Daniel Li
- Copyright © Daniel Li. All rights reserved.
Purpose
All team agents (小a/ops/code/quant/data/finance/research/market/pm/content/law) MUST route knowledge retrieval through this standardized pipeline before answering questions about:
- 系统配置、架构、部署
- 历史决策、TODO、进度
- Skill 用法与触发条件
- 项目状态与交付件
- 人物、日期、数字
QMD Feature Map (all features agents should use)
| Feature | Command | When to Use |
|---|---|---|
| Hybrid search | qmd query "<question>" |
DEFAULT: combines BM25 + vector + rerank. Best for most queries |
| Structured query | qmd query $'lex: keyword\nvec: semantic' |
When you need precise keyword AND semantic results |
| BM25 keyword | qmd search "<exact term>" |
Exact term/filename/config key lookup |
| Vector similarity | qmd vsearch "<concept>" |
Conceptual/fuzzy similarity (e.g., "how to deploy") |
| Get document | qmd get path/to/file.md:42 -l 30 |
Read specific lines after search identifies a file |
| Multi-get | qmd multi-get "reports/*.md" |
Batch fetch multiple files (e.g., all reports) |
| Collection filter | qmd query -c skills "<question>" |
Restrict search to one collection for precision |
| Full output | qmd query --full "<question>" |
Get complete document content instead of snippets |
| JSON output | qmd query --json "<question>" |
Machine-readable output for scripts |
| List files | qmd ls skills |
Browse what's indexed in a collection |
| Status | qmd status |
Health check: pending embeds, collection sizes |
| Update index | qmd update |
Re-index after file changes |
| Embed vectors | qmd embed |
Generate embeddings for new/changed files |
| Context notes | qmd context list |
View collection descriptions/usage hints |
| MCP server | qmd mcp |
Expose as MCP tool for IDE/agent integration |
Collections (current)
| Collection | Content | Use For |
|---|---|---|
clawd-memory |
~/clawd/**/*.md (1603 files) |
Memory, docs, reports, runbooks |
daily-memory |
~/clawd/memory/*.md (44 files) |
Daily work logs |
team |
~/.openclaw/agents/**/*.json (860 files) |
Agent configs, models, auth |
openclaw-config |
~/.openclaw/**/*.json (1031 files) |
OpenClaw system config |
projects |
~/clawd/projects/**/*.md (100 files) |
Project PRDs, deliverables |
skills |
~/clawd/skills/**/SKILL.md (468 files) |
All skill documentation |
reports |
~/clawd/reports/*.md (2 files) |
Research & review reports |
Routing Algorithm
Input: user question Q
Step 1 — CLASSIFY
A = "prior decisions / todos / people / dates / what happened"
B = "system config / how-to / skill usage / architecture"
C = "project status / deliverables / PRD"
D = "external facts / live data" (falls through to web)
Step 2 — RETRIEVE (execute ALL applicable, not just one)
if A:
→ qmd query -c daily-memory "<Q>" -n 5
→ qmd query -c clawd-memory "<Q>" -n 5
→ Also check ~/clawd/MEMORY.md directly for TODOs/decisions
if B:
→ qmd query -c openclaw-config "<Q>" -n 5
→ qmd query -c skills "<Q>" -n 5
→ qmd query -c clawd-memory "<Q>" -n 3 (for runbooks/docs)
if C:
→ qmd query -c projects "<Q>" -n 5
→ qmd query -c reports "<Q>" -n 3
if D:
→ qmd query "<Q>" -n 5 (all collections, no filter)
→ If low recall → web_fetch / browser
Step 3 — CITE
Every answer MUST include 1-3 source citations:
- File path: `~/clawd/docs/memory-router.md:15`
- QMD URI: `qmd://skills/geo-agent/SKILL.md`
- Or: "Source: qmd query -c projects 'content factory status'"
Agent Integration (mandatory AGENTS.md section)
Each agent's AGENTS.md must contain:
## 知识库 / Memory Router（强制）
- 你在回答任何「配置/流程/历史/怎么做」类问题前，**必须先检索本地知识库**：
1) 优先 QMD：`qmd query "<问题>"`（必要时加 `--collection openclaw-config|projects|skills|reports|clawd-memory`）
2) 涉及待办/决策/人/日期 → 再查工作区记忆文件（`~/clawd/memory/YYYY-MM-DD.md` 与 `~/clawd/MEMORY.md`）
- 输出时至少引用 1-3 个来源（文件路径或 `qmd://...` URI）。
Health Monitoring
Daily Cron (recommended)
# qmd-health: run daily at 08:00
qmd status | grep -E "Total|Vectors|Pending|Updated"
qmd update
Weekly Embed Refresh
# qmd-embed: run weekly Sunday 03:00
qmd embed
qmd status
Health Metrics to Track
- 覆盖率: Vectors / Total (target: >90%)
- Pending: should be <100 after weekly embed
- Collection freshness: Updated timestamps should be <24h for active collections
- Query latency: hybrid query should return <2s
Troubleshooting
| Problem | Solution |
|---|---|
qmd embed hangs |
Check if node-llama-cpp is compiling (first run). Wait ~10min. |
| CUDA errors | Normal on CPU-only servers. QMD auto-falls back to CPU. |
| Ollama not found | QMD uses node-llama-cpp, NOT Ollama. Ignore Ollama references. |
| Low recall | Try --full flag, or use qmd search (BM25) for exact terms |
| Missing files | Run qmd update to re-index, then qmd embed for new vectors |
Script: memory_router.sh
For automated context injection into agent prompts:
#!/bin/bash
# Usage: ./memory_router.sh "question" [collection]
QUERY="$1"
COLLECTION="${2:-}"
if [ -n "$COLLECTION" ]; then
qmd query -c "$COLLECTION" "$QUERY" -n 5 --line-numbers
else
qmd query "$QUERY" -n 5 --line-numbers
fi
Last updated: 2026-03-03 Maintainer: 小a (CEO)