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Claude Memory

作者 Kaiji-Z · GitHub ↗ · v1.2.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install claude-memory
功能描述
Structured memory system for OpenClaw agents. Provides a 4-type classification (user/feedback/project/reference), layered architecture (MEMORY.md + topics/ +...
使用说明 (SKILL.md)

Claude Memory

A structured, layered memory architecture for OpenClaw agents. Uses a 4-type classification (user/feedback/project/reference) for organized memory, combined with semantic search and progressive disclosure for token-efficient retrieval.

Architecture

MEMORY.md (≤10KB, injected every turn via system prompt)
├── High-frequency info directly readable (inline)
├── Pointers to details in topics/
└── 4 sections: user | feedback → feedback.md | project | reference

memory/
├── feedback.md     # Corrections AND confirmations from human (MOST IMPORTANT)
├── YYYY-MM-DD.md   # Daily raw notes
└── topics/         # Low-frequency large content
    ├── agent-ids.md
    ├── known-issues.md
    └── ... (domain-specific)

4-Type Classification

Type Purpose Examples
user Human's personal info, preferences, relationships IDs, timezone, family, privacy rules
feedback Corrections AND confirmations from human "Check docs first", "That approach was right"
project Work items, tasks, known issues Active bugs, cron jobs, cleanup history
reference Technical resources, environment Runtime config, connected services, security events

Setup

Run the init script to create the directory structure and template files:

python scripts/init_memory.py \x3Cworkspace-path>

This creates:

  • MEMORY.md with empty section templates
  • memory/feedback.md with format guide
  • memory/topics/ directory
  • Instructions to update AGENTS.md memory rules

After running, edit MEMORY.md sections with actual content and update AGENTS.md to include the memory rules from references/agents-rules.md.

Write Rules

  1. Feedback immediately — When human corrects you, write to memory/feedback.md NOW. Not later, not "I'll remember", NOW.
  2. Record confirmations too — When human validates a non-obvious approach ("yes exactly", "keep doing that"), record it. If you only save corrections, you'll avoid past mistakes but drift away from approaches the human has already approved, becoming overly cautious.
  3. MEMORY.md ≤ 10KB — Hybrid format: inline high-frequency info for direct readability, pointers to topics/ for depth. If it exceeds 10KB, move lower-frequency content to topics/ — the content is preserved, just relocated.
  4. Read before answer — When MEMORY.md says "see topics/X.md", read that file first. Never answer from a one-line summary.
  5. Absolute dates only — Convert relative dates ("yesterday", "last week") to absolute dates (e.g., "2026-04-01") when writing. Memories should remain interpretable months later.
  6. Don't memorize what tools can look up — File paths, git history, code structure, current weather, live data.
  7. Don't memorize ephemeral state — In-progress work, temporary conversation context, things that resolve themselves.

What NOT to Save

  • Information tools can look up in real-time (weather, time, current stock prices)
  • Code patterns, architecture, file paths, project structure — derivable by reading the project
  • Git history, recent changes, who-changed-what — git log is authoritative
  • Debugging solutions or fix recipes — the fix is in the code; the commit has the context
  • Ephemeral task details: in-progress work, temporary state, current conversation context
  • Anything already documented in CLAUDE.md or equivalent project files

Verification Rules

Memories are long-term assets, not consumables. They don't expire. But some memories need verification before use:

  • Personal preferences, relationships, history events — Generally stable. No verification needed.
  • Technical state (service configs, bug status, installed versions) — Verify before acting on them. The world changes.
  • Before recommending based on memory — If the memory names a specific file, check it exists. If it names a function or flag, grep for it. "The memory says X exists" is not the same as "X exists now."

Feedback Format

Each feedback entry follows this structure. Two types: correction (don't do X) and confirmation (keep doing X).

### F###: Rule description (date)
- **Why**: Root cause or context
- **How to apply**: Concrete scenarios

### F###: Confirmed approach description (确认 date)
- **Context**: What was being worked on
- **Why**: What made this approach noteworthy or validated

Heartbeat Maintenance

Memory is an asset, not a consumable. Memories should not be deleted because they're old.

⚠️ Setup requirement: This 4-phase routine MUST be written into HEARTBEAT.md, NOT AGENTS.md. HEARTBEAT.md is injected only during heartbeat polls (token-efficient), while AGENTS.md is loaded every session (wastes token on every message). Write memory write rules in AGENTS.md, write this maintenance routine in HEARTBEAT.md.

During heartbeat polls, follow this 4-phase care routine:

Phase 1 — Catch-up (补漏)

  • Did the human correct or confirm something this session that isn't recorded in memory/feedback.md?
  • Are there significant events from this conversation not yet in daily notes?

Phase 2 — Consolidate (整合)

  • Review recent daily notes (last 1-3 days) for insights worth elevating to MEMORY.md or topics/.
  • Merge new signal into existing topic files rather than creating near-duplicates.
  • Daily notes that have been fully consolidated can be left in place as historical records — do NOT delete them.

Phase 3 — Verify (校验)

  • Pick 1-2 entries from MEMORY.md and spot-check if they're still accurate.
  • Focus on technical state entries (service configs, bug status, versions). Personal info and historical events don't need verification.
  • If a memory conflicts with current reality, update it — don't delete it, correct it.

Phase 4 — Tidy (整理)

  • If MEMORY.md exceeds 10KB, relocate lower-frequency content to topics/. The content is preserved, just moved to a more appropriate home.
  • Ensure topic files have a timestamp annotation: > Created: YYYY-MM-DD | Updated: YYYY-MM-DD
  • Add pointers in MEMORY.md for any newly created topic files.

Design Decisions

  • MEMORY.md as hybrid (content + pointers) — High-frequency info is directly readable for warmth and efficiency. Deep details live in topics/. Not a pure index — an agent's memory should feel like a person's, not a database.
  • Memories don't expire — Unlike a coding agent where project state changes fast, a daily assistant's memories are long-term assets. Old memories get relocated (to topics/) if MEMORY.md needs space, never deleted.
  • Feedback includes confirmations — Recording only corrections makes the agent overly cautious. Validated approaches ("yes, keep doing that") are just as important to remember.
  • Verification is selective — Personal preferences and history are stable facts. Only technical state (configs, versions, bug status) needs re-verification.
  • Feedback as separate file — Highest-priority memory. Must never be buried under daily notes.
  • Topics for depth — Low-frequency but detailed content lives in topics/ to keep MEMORY.md lean.
  • No auto-extraction — Requires OpenClaw runtime support. Currently manual via rules.

Advanced

For detailed AGENTS.md rules, see references/agents-rules.md. For troubleshooting, see references/encoding-notes.md.

安全使用建议
This skill creates and manages persistent local files containing personal preferences, confirmations, and project data — review and secure the workspace you pass to the init script (permissions, backups, encryption). Confirm your deployment enforces the SKILL.md's 'ONLY load in main session / DO NOT load in shared contexts' rule, otherwise private MEMORY.md content could be exposed. Review scripts/init_memory.py (it's small and benign) and the provided references to ensure the write rules meet your privacy policy. Finally, note the skill has unknown provenance (no homepage, owner ID only); if provenance matters to you, ask the publisher for more information before installing.
功能分析
Type: OpenClaw Skill Name: claude-memory Version: 1.2.0 The skill bundle provides a structured local memory management system for OpenClaw agents using Markdown files. The initialization script (scripts/init_memory.py) safely creates a directory hierarchy and templates within a user-defined workspace without any network calls or suspicious execution. The instructions for the agent (SKILL.md and references/agents-rules.md) are focused on organizational efficiency and include security-conscious guidelines to prevent personal data leakage in shared environments.
能力评估
Purpose & Capability
Name/description match the actual artifacts: an instruction-heavy SKILL.md plus a small init script that creates memory files. Nothing requested (no env vars, no binaries, no external endpoints) is out of scope for a local memory system.
Instruction Scope
Instructions direct the agent to create and persist personal and project data under a workspace (MEMORY.md, memory/feedback.md, daily notes, topics/). This is expected for a memory skill, but it means the agent will be asked to write potentially sensitive personal data to disk and to read those files when answering. The SKILL.md also recommends 'injecting MEMORY.md every turn via system prompt' and strict rules about not loading memories in shared contexts; that behavior is coherent but relies on correct platform enforcement.
Install Mechanism
No install spec. Only an instruction-only skill plus a small, local Python initializer (scripts/init_memory.py) that creates templates. No downloads or external packages.
Credentials
The skill requests no environment variables, credentials, or external config paths. It does intentionally ask the agent to store human preferences and other personal info in workspace files — that is proportionate to the stated purpose but is a privacy concern the user should acknowledge.
Persistence & Privilege
always:false (no forced global inclusion). The skill instructs creating persistent files in the agent workspace and instructs the agent to write feedback 'NOW' on corrections — expected for this feature but increases the amount of persistent personal data on disk. The skill does not modify other skills or system-wide config.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install claude-memory
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /claude-memory 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.2.0
Heartbeat Maintenance 现在明确要求将 4 阶段记忆照料流程写入 HEARTBEAT.md 而非 AGENTS.md,避免每次 session 加载浪费 token
v2.0.0
Major update: memories are long-term assets (never deleted, only relocated), MEMORY.md limit 3KB→10KB hybrid format, feedback now records confirmations too, 4-phase heartbeat care routine (catch-up/consolidate/verify/tidy), selective verification (personal vs technical), what-NOT-to-save rules, topic timestamp annotations. Based on analysis of Claude Code leaked source (autoDream/memdir).
v1.1.0
- Skill renamed from "memory-system" to "claude-memory". - Increased allowed size of MEMORY.md from 3KB to 10KB for injected high-frequency memory. - No other functional or rule changes.
v1.0.0
Initial release
元数据
Slug claude-memory
版本 1.2.0
许可证 MIT-0
累计安装 1
当前安装数 1
历史版本数 4
常见问题

Claude Memory 是什么?

Structured memory system for OpenClaw agents. Provides a 4-type classification (user/feedback/project/reference), layered architecture (MEMORY.md + topics/ +... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 351 次。

如何安装 Claude Memory?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install claude-memory」即可一键安装,无需额外配置。

Claude Memory 是免费的吗?

是的,Claude Memory 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Claude Memory 支持哪些平台?

Claude Memory 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Claude Memory?

由 Kaiji-Z(@kaiji-z)开发并维护,当前版本 v1.2.0。

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