memory-router

name	memory-router
description	Team-wide memory routing skill — routes agent queries to the optimal knowledge source (QMD hybrid search, daily memory, MEMORY.md) and enforces citation. Use when any agent needs to retrieve prior work, system config, skill docs, project status, or decisions. Triggers on "查知识库", "memory router", "qmd query", "find in docs", "what was decided", "how does X work", "项目状态", "之前的决策".
author	Daniel Li

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

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Last updated: 2026-03-03 Maintainer: 小a (CEO)