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adchina2025

MemCore 记忆核心

by adchina2025 · GitHub ↗ · v1.0.3 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install memcore-memory
Description
为 OpenClaw 打造的五层自适应记忆检索系统,四层自进化记忆模型(L1轨迹→L2模式→L3世界模型→技能结晶),反馈驱动的价值评分,自动维护与健康诊断。适用场景:(1) 带降级保障的记忆搜索,(2) 带历史故障诊断的健康检查,(3) 每日散会流程自动模式归纳,(4) 生成启动简报替代全量MEMORY.md注...
README (SKILL.md)

MemCore 记忆核心

为 OpenClaw 打造的五层自适应记忆系统,自进化。替代全量 MEMORY.md 注入 — 节省 92% token。

Five-tier adaptive memory system with self-evolution for OpenClaw. Drop-in replacement for full MEMORY.md injection — 92% token savings.

Quick Commands

# Startup: generate brief (≤500 tokens, replaces full MEMORY.md)
python3 scripts/memcore/cli.py brief

# Search: 5-tier adaptive fallback
python3 scripts/memcore/cli.py search "\x3Cquery>" -n 5

# Daily maintenance (index new logs → induce patterns → decay → refresh brief)
python3 scripts/memcore/cli.py run-all

# Stats overview
python3 scripts/memcore/cli.py stats

# Feedback: log whether retrieved memory was useful
python3 scripts/memcore/cli.py feedback-log \x3Ctrace_id> used|skipped|good|bad

Startup Integration

On each session start, replace full MEMORY.md loading with:

1. Read SOUL.md → USER.md → MEMORY_BRIEF.md (auto-generated) → .anatomy.md → yesterday+today memory
2. MEMORY.md loaded on-demand only via: python3 scripts/memcore/cli.py search "\x3Ckeywords>"

The brief generator picks top 5 active patterns, top 3 recent lessons, and current taskboard items — all under 500 tokens.

散会 Integration

Add step 3 to 散会 flow:

python3 scripts/memcore/cli.py index && \
python3 scripts/memcore/cli.py induce && \
python3 scripts/memcore/cli.py feedback

This auto-indexes today's log, induces new patterns, and decays stale traces.

Health Check Integration

When running daily health checks (09:00), append MemCore diagnostic:

# Get system stats
python3 scripts/memcore/cli.py stats

# If anomalies found, search historical similar faults
python3 scripts/memcore/cli.py search "\x3Canomaly keywords>" -n 3

Safety rule: Report only. Never auto-fix. Let human decide.

Search & Feedback Flow

After every memory_search call, log feedback:

# If the retrieved memory was helpful:
python3 scripts/memcore/cli.py feedback-log \x3Ctrace_id> used

# If irrelevant:
python3 scripts/memcore/cli.py feedback-log \x3Ctrace_id> skipped

This trains the retrieval system — high-value traces rise, stale ones decay.

Cron Setup

Two 12-hour maintenance jobs (systemEvent, main session, wakeMode: next-heartbeat):

Time (CST) Cron expression Action
04:00 0 4 * * * index + induce + feedback + brief
16:00 0 16 * * * same

These are SQLite-only, no model calls, near-zero resource cost.

Rollback

cp memcore_backup_\x3Cdate>/AGENTS.md ~/.openclaw/workspace/
cp memcore_backup_\x3Cdate>/MEMORY.md ~/.openclaw/workspace/

Original MEMORY.md is never modified. All MemCore data lives in separate SQLite files under ~/.openclaw/.

Detailed Reference

Usage Guidance
Install only if you are comfortable with MemCore indexing your OpenClaw memory/workspace files into local SQLite databases and generating a startup MEMORY_BRIEF.md. Review the generated brief and any scheduled cron/systemEvent jobs, and avoid storing secrets or highly sensitive information in files that MemCore indexes.
Capability Analysis
Type: OpenClaw Skill Name: memcore-memory Version: 1.0.3 MemCore is a legitimate memory management and optimization system for OpenClaw agents, designed to reduce token usage by replacing full memory logs with a structured retrieval system. The bundle uses SQLite databases to index agent traces and patterns, and includes a multi-tier search engine in `retriever.py` that safely utilizes `subprocess.run` for local file searching. No evidence of data exfiltration, malicious persistence, or harmful prompt injection was found; all operations are local to the agent's workspace and follow the stated purpose of enhancing memory efficiency.
Capability Assessment
Purpose & Capability
The file reads OpenClaw memory/workspace materials, indexes them into SQLite, and generates a startup brief; this is aligned with the stated memory-retrieval purpose, but it handles potentially private agent memory.
Instruction Scope
The instructions ask users to integrate MemCore into startup, search feedback, daily wrap-up, and health-check workflows. This is disclosed and purpose-aligned, but it means retrieved or summarized memory can influence future sessions.
Install Mechanism
Registry metadata says there is no install spec, but the package includes a user-run install script that copies Python modules and, if memory logs exist, runs initial indexing and brief generation. This is not hidden, but users should run it knowingly.
Credentials
The shown code stays local under ~/.openclaw/workspace and ~/.openclaw SQLite files, with no credentials or network endpoints evident in the provided artifacts; the access is still broad within the OpenClaw memory/workspace context.
Persistence & Privilege
The skill documents optional cron/systemEvent maintenance and persistent SQLite databases. This is appropriate for a memory system, but users should decide whether scheduled background updates are acceptable.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install memcore-memory
  3. After installation, invoke the skill by name or use /memcore-memory
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.3
memcore-memory 1.0.3 - No file changes detected in this version. - No updates to documentation, code, or functionality. - Version bump only; behavior remains unchanged.
v1.0.2
- Metadata updated: displayName field removed from SKILL.md. - No functional or instructional changes; usage and documentation remain the same.
v1.0.1
No user-facing changes in this version. - No file changes were detected. - Functionality and documentation remain unchanged.
v1.0.0
- Initial release of MemCore: a five-tier adaptive memory retrieval system for OpenClaw agents. - Replaces full MEMORY.md injection with automated startup briefs, saving up to 92% tokens. - Features self-evolving four-layer memory model, feedback-driven scoring, auto-maintenance, and health diagnostics. - Provides CLI tools for searching memory with fallback, daily pattern induction, health checks, and feedback logging. - Integrates with startup, 散会, and health check workflows; easy rollback and minimal resource cost.
Metadata
Slug memcore-memory
Version 1.0.3
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 4
Frequently Asked Questions

What is MemCore 记忆核心?

为 OpenClaw 打造的五层自适应记忆检索系统,四层自进化记忆模型(L1轨迹→L2模式→L3世界模型→技能结晶),反馈驱动的价值评分,自动维护与健康诊断。适用场景:(1) 带降级保障的记忆搜索,(2) 带历史故障诊断的健康检查,(3) 每日散会流程自动模式归纳,(4) 生成启动简报替代全量MEMORY.md注... It is an AI Agent Skill for Claude Code / OpenClaw, with 73 downloads so far.

How do I install MemCore 记忆核心?

Run "/install memcore-memory" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is MemCore 记忆核心 free?

Yes, MemCore 记忆核心 is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does MemCore 记忆核心 support?

MemCore 记忆核心 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created MemCore 记忆核心?

It is built and maintained by adchina2025 (@adchina2025); the current version is v1.0.3.

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