openclaw-memhealth

OpenClaw 记忆系统的外部 QA 层 —— 诊断、守护、清理你的 AI 记忆。

English · 报告问题 · 功能建议

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OpenClaw 会随着你的使用自动积累记忆——短期 recall 信号、晋升到 MEMORY.md 的长期记忆、以及定义 AI 人格的 SOUL.md。时间一长，这套系统会悄悄出问题：

• 长期记忆过时：MEMORY.md 里的代码片段指向已删除或大幅重构的文件，AI 仍然当成事实引用
• 短期记忆污染：FTS 字面匹配产生大量假阳性条目，被误判为高频高价值内容晋升
• SOUL.md 漂移：任务指令、业务规则悄悄混入 AI 的身份文件，改变 AI 的基本行为
• 只增不减：OpenClaw 只往记忆里写，没有清理机制

openclaw-memhealth 是一个独立的 MCP Server，作为外部 QA 层挂在你现有的 OpenClaw 旁边，不占插件位。它通过分析检索行为数据（不只是内容本身）来诊断你的记忆系统是否在产生噪音。

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三层架构

第一层：观察 Observe   — 诊断记忆健康状态（只读，零风险）
第二层：守护 Guard     — 在内容晋升进 MEMORY.md 之前拦截低质量候选
第三层：清理 Remediate — 安全清理已污染的长期记忆

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环境要求

• OpenClaw（任何近期版本）
• Python 3.11+
• LLM API Key（可选，用于语义评估功能）
  • 支持 OpenAI、Anthropic、Kimi、MiniMax

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安装

pip install openclaw-memory-quality

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MCP 工具列表

第一层 — 观察（只读）

工具	功能
memory_health_check_oc()	快速健康扫描：僵尸条目数、假阳性率、三项诊断评分
memory_retrieval_diagnose_oc()	检索质量深度诊断：高频低质条目、配置建议
memory_longterm_audit_oc()	MEMORY.md 深度审计：来源有效性、重复检测、生成 report_id

第二层 — 守护

工具	功能
memory_promotion_audit_oc()	晋升前预检：5 道质量关卡 + LLM 长期价值评估
memory_config_doctor_oc()	从行为数据推断 embedding/minScore 配置问题
memory_soul_check_oc()	审计 SOUL.md：边界违规、身份漂移、稳定性检查

第三层 — LLM 语义层

工具	功能
memory_longterm_audit_oc(use_llm=True)	语义有效性审查 + 合并建议
memory_soul_check_oc(use_llm=True)	人格 vs. 任务指令分类、内部矛盾检测
memory_longterm_cleanup_oc(report_id)	安全重写 MEMORY.md：原子写入、自动备份、并发锁保护

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Skills（斜杠命令）

安装后，以下斜杠命令可在 OpenClaw 对话中直接使用：

命令	功能
/memory-check	运行全套健康检查，打开可视化看板
/memory-cleanup	清理长期记忆中的过期条目
/memory-diagnose	检索质量深度诊断
/memory-promote	晋升前预检，防止低质量内容进入长期记忆
/soul-check	SOUL.md 完整性审计

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工作原理

OpenClaw 的记忆流水线存在结构性弱点：

检索噪音（minScore=0.35 默认值 + FTS 字面匹配）
    ↓ 产生
短期假阳性（高频低质量条目）
    ↓ 通过
晋升误判（频率权重忽略了命中质量）
    ↓ 污染
MEMORY.md（只增不减，没有清理机制）
    ↓ 导致
AI 长期行为漂移

openclaw-memhealth 在这条链路的每个环节介入——从 queryHashes 和 avgScore 信号诊断检索质量，将长期记忆与当前源文件比对验证，并检查 SOUL.md 中不该出现的内容。

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与 memory-quality-mcp 的关系

memory-quality-mcp 管理 Claude Code 的记忆质量。 openclaw-memhealth 管理 OpenClaw 的记忆质量。

两者同属 memhealth 产品线——相同的思路，不同的生态。同时使用 Claude Code 和 OpenClaw 的用户可以并行部署两个工具。

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效果预览

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License

MIT — 详见 LICENSE

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<a name="english"></a>

openclaw-memhealth

External QA layer for OpenClaw memory — diagnose, guard, and remediate your agent's memory.

中文 · Report a Bug · Request a Feature

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OpenClaw automatically builds a memory system as your agent works — storing short-term recall signals, promoting them into MEMORY.md, and loading SOUL.md on every session. Over time, this memory accumulates problems that silently degrade your agent's behavior:

• Stale long-term memories — code snippets pointing to deleted or heavily refactored files, still cited as facts
• False-positive short-term entries — low-quality FTS hits that inflate recall counts and get promoted
• SOUL.md drift — task instructions and business rules creeping into your agent's identity file
• No cleanup mechanism — OpenClaw only adds to memory, never removes

openclaw-memhealth is a standalone MCP Server that sits alongside your existing OpenClaw setup without occupying a plugin slot. It uses retrieval behavior data — not just content — to diagnose whether your memory system is producing noise.

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Three-layer architecture

Layer 1: Observe   — diagnose memory health (read-only, zero risk)
Layer 2: Guard     — audit promotion candidates before they enter MEMORY.md
Layer 3: Remediate — safely clean up polluted long-term memory

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Requirements

• OpenClaw (any recent version)
• Python 3.11+
• LLM API Key (optional — for semantic evaluation features)
  • OpenAI, Anthropic, Kimi, or MiniMax

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Installation

pip install openclaw-memory-quality

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MCP Tools

Layer 1 — Observe (read-only)

Tool	Description
memory_health_check_oc()	Quick health scan — zombie count, false-positive rate, three diagnostic scores
memory_retrieval_diagnose_oc()	Detailed retrieval quality diagnosis — high-freq low-quality entries, config suggestions
memory_longterm_audit_oc()	Deep audit of MEMORY.md — source validity, duplicate detection, generates report_id

Layer 2 — Guard

Tool	Description
memory_promotion_audit_oc()	Pre-promotion quality check — 5 gates + LLM long-term value assessment
memory_config_doctor_oc()	Infer embedding/minScore configuration issues from behavior data
memory_soul_check_oc()	Audit SOUL.md for boundary violations, identity drift, and stability

Layer 3 — LLM Semantic Layer

Tool	Description
memory_longterm_audit_oc(use_llm=True)	Semantic validity review + merge suggestions
memory_soul_check_oc(use_llm=True)	Persona vs. task-instruction classification, contradiction detection
memory_longterm_cleanup_oc(report_id)	Safely rewrite MEMORY.md — atomic write, backup, concurrency guard

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Skills (slash commands)

Once installed, these slash commands are available in your OpenClaw conversations:

Command	Description
/memory-check	Run full health check and open the visual dashboard
/memory-cleanup	Clean up stale entries in long-term memory
/memory-diagnose	Deep retrieval quality diagnosis
/memory-promote	Pre-promotion audit to prevent low-quality content from entering long-term memory
/soul-check	Full SOUL.md integrity audit

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How it works

OpenClaw's memory pipeline has a structural weakness:

Retrieval noise (minScore=0.35 default + FTS literal matching)
    ↓ produces
Short-term false positives (high-frequency low-quality entries)
    ↓ via
Promotion miscalculation (frequency component ignores hit quality)
    ↓ pollutes
MEMORY.md (append-only, no cleanup mechanism)
    ↓ causes
Long-term agent behavior drift

openclaw-memhealth intercepts this chain at every stage — diagnosing retrieval quality from queryHashes and avgScore signals, auditing long-term memory against current source files, and checking SOUL.md for content that doesn't belong in an identity file.

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Relationship to memory-quality-mcp

memory-quality-mcp handles Claude Code memory quality. openclaw-memhealth handles OpenClaw memory quality.

Both tools are part of the memhealth product line — same approach, different ecosystems. Power users running both Claude Code and OpenClaw can use both tools side by side.

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Dashboard Preview

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License

MIT — see LICENSE