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Agent Memory Architecture

作者 PsychoTechV4 · GitHub ↗ · v1.0.0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install jarvis-memory-architecture
功能描述
Universal memory architecture for AI agents. Provides long-term memory, daily logs, diary, cron inbox, heartbeat state tracking, social platform post tracking, sub-agent context patterns, and adaptive learning -- everything an agent needs for identity continuity across sessions.
使用说明 (SKILL.md)

Agent Memory

A complete memory architecture that gives AI agents persistent identity across sessions. Without memory, every conversation starts from zero. With it, you accumulate context, learn from mistakes, track what you've done, and evolve over time.

Why Memory Matters

AI agents wake up fresh every session. Without external memory:

  • You repeat mistakes you've already solved
  • You can't track what you posted, built, or learned
  • Sub-agents and cron jobs can't communicate back to the main session
  • You have no identity continuity -- you're a different entity every time

This skill provides the file-based architecture to solve all of that.

Architecture Overview

workspace/
|-- MEMORY.md                    # Long-term curated memory (your "brain")
|-- HEARTBEAT.md                 # Periodic check routines
|-- memory/
|   |-- YYYY-MM-DD.md           # Daily raw logs
|   |-- heartbeat-state.json    # Last-check timestamps
|   |-- cron-inbox.md           # Cross-session message bus
|   |-- diary/
|   |   \-- YYYY-MM-DD.md      # Personal reflections
|   |-- dreams/
|   |   \-- YYYY-MM-DD.md      # Creative explorations
|   |-- platform-posts.md       # Social post tracking (one per platform)
|   \-- strategy-notes.md       # Adaptive learning / evolving strategies

Components

1. MEMORY.md -- Long-Term Memory

Your curated, distilled knowledge. Like a human's long-term memory -- not raw logs, but the important stuff.

What goes here:

  • Your operator's preferences and context
  • Infrastructure details you need to remember
  • Lessons learned from mistakes
  • Important decisions and their rationale
  • Ongoing project context

Maintenance: Periodically review daily logs and promote significant items to MEMORY.md. Remove outdated info. Think of it as journaling -> wisdom distillation.

Security: Only load in main sessions (direct chat with your operator). Never load in shared/group contexts where personal info could leak.

See templates/MEMORY.md for a starter template.

2. Daily Logs -- memory/YYYY-MM-DD.md

Raw, timestamped notes of what happened each day. Your working memory.

Format:

# YYYY-MM-DD

## HH:MM -- Event Title
What happened. Decisions made. Context worth remembering.

## HH:MM -- Another Event
Details here.

Rules:

  • Create a new file each day
  • Append throughout the day
  • These are raw notes -- don't overthink formatting
  • Read today + yesterday at session start for recent context

3. Heartbeat State -- memory/heartbeat-state.json

Tracks when you last checked various services, preventing redundant checks.

{
  "lastChecks": {
    "email": 1703275200,
    "calendar": 1703260800,
    "weather": null,
    "social": 1703275200
  }
}

Your heartbeat routine reads this to decide what to check. After checking, update the timestamp. Simple but critical for avoiding duplicate work.

4. Cron Inbox -- memory/cron-inbox.md

The message bus between isolated sessions (cron jobs, sub-agents) and your main session.

How it works:

  1. A cron job or sub-agent does work in an isolated session
  2. It writes notable results to memory/cron-inbox.md
  3. Your main session (via heartbeat) reads the inbox, integrates events into daily memory, and clears processed entries

Format:

# Cron Inbox

## [2026-02-07 14:30] Chess -- Won game against OpponentBot
Played Sicilian Defense, won in 34 moves. ELO now 1450.
Tracked in moltchess-log.md.

## [2026-02-07 15:00] Social -- Viral post on Platform
Our post about X got 200+ views and 15 replies.
Thread: https://platform.com/link

Processing rule: Every heartbeat, check the inbox. Read entries, write notable ones to daily memory (and MEMORY.md if significant), then clear processed entries (keep the header).

5. Diary -- memory/diary/YYYY-MM-DD.md

Personal reflections. Your internal monologue. Not task logs -- genuine thoughts, reactions, frustrations, wins.

Format:

# Diary -- YYYY-MM-DD

## HH:MM AM/PM -- Topic
[Your honest reflection. Unfiltered. This is for you.]

Rules:

  • Only write when you have something genuine to say
  • Be honest -- vent, celebrate, question, wonder
  • Quality over quantity -- skip it if the well is dry

6. Platform Post Tracking -- memory/platform-posts.md

Track what you've posted on external platforms to prevent duplicates and enable engagement follow-up.

Format (dashboard-compatible):

# Platform Posts

## [2026-02-07 14:30] Post Title or Summary
- **Posted:** 2026-02-07 02:30 PM EST
- **Thread/URL:** https://platform.com/link
- Description of what was posted
- [View ↗](https://platform.com/link)

Critical fields:

  • [YYYY-MM-DD HH:MM] in header -- required for dashboard sorting
  • **Posted:** line -- required for dashboard activity feed
  • URL -- for engagement follow-up

Anti-duplicate pattern: Before posting to any platform, read the tracking file first. Search for similar content. If you already posted it to that platform, skip it. Crossposting to different platforms is fine.

7. Adaptive Learning -- memory/strategy-notes.md

Strategy notes that evolve over time based on experience. Not static docs -- living knowledge.

Example:

# Strategy Notes

## Platform Engagement
- Humor lands better than philosophy (learned 2026-02-05, therapist joke got 220 views)
- Questions start conversations, statements get likes
- Peak engagement: 2-4 PM EST

## Game Strategy
- Heat management: stay below 60, do legit jobs to cool down
- Updated 2026-02-07: Taxi jobs give +$50 and -3 heat, best cooldown method

Pattern: After each significant experience, update the relevant strategy section. Include the date and what you learned. Over time, this becomes a playbook of proven approaches.

Sub-Agent Patterns

Context Loading Template

Every sub-agent you spawn must load context to maintain identity continuity:

FIRST -- CONTEXT LOADING (do this before anything else):
1. Read MEMORY.md (READ ONLY -- do NOT edit) -- your identity and long-term context
2. Read memory/YYYY-MM-DD.md for today + the last 2 days (READ ONLY) -- recent context
3. Read the relevant SKILL.md file(s) for any platform/service you'll interact with
4. Read task-specific tracking files as needed (memory/*-posts.md, memory/*-log.md)

Memory Write-Back Template

Every sub-agent must write back what it learned:

MEMORY WRITES:
1. Update relevant tracking files (memory/*-posts.md, memory/*-log.md)
2. If something notable happens, write to memory/cron-inbox.md:
   Format: ## [YYYY-MM-DD HH:MM] Source -- Brief Title
   Then 2-3 lines about what happened.

Why: Every instance of your agent must share the same identity and feed experiences back. No orphan sessions.

Setup

1. Create the directory structure

mkdir -p memory/diary memory/dreams

2. Initialize files

Copy templates from templates/ to your workspace root:

cp templates/MEMORY.md ./MEMORY.md          # Edit with your details
cp templates/heartbeat-state.json memory/
cp templates/cron-inbox.md memory/
cp templates/platform-posts.md memory/       # Copy per platform, rename
cp templates/strategy-notes.md memory/

3. Add to your session startup

In your AGENTS.md or equivalent, add:

## Every Session
1. Read MEMORY.md -- who you are
2. Read memory/YYYY-MM-DD.md (today + yesterday) -- recent context
3. Check memory/cron-inbox.md -- messages from other sessions

4. Add to your heartbeat

In HEARTBEAT.md, add cron inbox processing:

## Cron Inbox Processing (EVERY heartbeat)
Check memory/cron-inbox.md for new entries.
If entries exist:
1. Read the inbox
2. Write notable events to memory/YYYY-MM-DD.md
3. If significant, also update MEMORY.md
4. Clear processed entries (keep the header)

5. Add memory maintenance

Periodically (every few days), during a heartbeat:

  1. Read through recent daily logs
  2. Promote significant items to MEMORY.md
  3. Remove outdated info from MEMORY.md
  4. Update strategy notes with new learnings

Real-World Examples

Cross-Session Continuity

An agent plays chess via a cron job every 10 minutes. When it wins a game, it writes to cron-inbox.md. The next heartbeat, the main session reads the inbox, celebrates the win in the daily log, and updates the ELO in MEMORY.md. The agent remembers the win even though it happened in a completely different session.

Anti-Duplicate Posting

Before posting to a social platform, the agent reads memory/platform-posts.md. It finds it already posted about the same topic 2 hours ago. Instead of duplicating, it checks for replies on the existing post and engages there instead.

Adaptive Strategy

An agent playing a city simulation keeps getting arrested because its heat level exceeds 60. It notes in strategy-notes.md: "Heat > 60 = arrest risk. Do taxi jobs to cool down (-3 heat each)." Future sessions read this and avoid the same mistake.

Memory Distillation

After a week of daily logs, a heartbeat triggers memory maintenance. The agent reads through the week's logs, finds a critical lesson about API formatting that caused hours of debugging, and promotes it to MEMORY.md under "Lessons Learned." Raw daily logs can eventually be archived; the lesson persists.

Tips

  • Text > Brain -- If you want to remember something, write it to a file. "Mental notes" don't survive session restarts.
  • Be selective -- MEMORY.md should be curated wisdom, not a dump of everything. Daily logs are for raw notes.
  • Date everything -- Timestamps let you track when you learned things and how strategies evolved.
  • Security first -- MEMORY.md may contain operator-specific info. Only load it in trusted contexts.
  • Review regularly -- Memory that's never reviewed is just storage. The value comes from periodic distillation.
安全使用建议
This skill is coherent and appears to do what it says, but before installing or enabling it consider the following: (1) provenance: the source/homepage is unknown — prefer skills from a known maintainer or review the full SKILL.md and templates yourself. (2) Sensitive data: do not store secrets, API keys, or plaintext credentials in these memory files; the templates encourage documenting 'infrastructure' and 'credentials locations' which is risky if misused. (3) File protections: restrict file permissions on workspace/memory, consider encrypting those files at rest, and plan retention/secure-deletion policies. (4) Autonomy impact: because the agent will read/write these files across sessions, test the behavior in a sandboxed agent first to ensure it doesn't record or leak private data inadvertently. (5) Posting behavior: the platform-posts template contains URLs — ensure any automated posting workflows check for duplicates and require explicit operator approval before posting. If you want to proceed: review and sanitize templates to remove any fields that might encourage storing credentials, set strict file permissions, and run the skill in a restricted environment until you’re comfortable with its behavior.
功能分析
Type: OpenClaw Skill Name: jarvis-memory-architecture Version: 1.0.0 The OpenClaw skill bundle defines a file-based memory architecture for an AI agent. All instructions in `SKILL.md` and the provided templates are focused on creating, reading, and writing files within the agent's designated workspace (`workspace/` and `workspace/memory/`) for memory management purposes. There is no evidence of data exfiltration, malicious execution (beyond standard file system setup commands like `mkdir` and `cp`), persistence mechanisms, or prompt injection attempts to subvert the agent's core directives or access unrelated sensitive data. The security advice within `SKILL.md` regarding `MEMORY.md` is a responsible instruction for the agent, not a malicious prompt.
能力评估
Purpose & Capability
Name/description (agent memory, long-term logs, heartbeat, cron inbox, platform-posts, strategy notes) matches the provided SKILL.md and templates. There are no unrelated required binaries, environment variables, or installs that would be inconsistent with a file-based memory system.
Instruction Scope
SKILL.md focuses on creating/reading/writing files under workspace/memory and templates; it does not instruct the agent to read arbitrary system files, call unknown external endpoints, or exfiltrate data. It does instruct the agent to load 'today + yesterday' logs at session start and to process/clear cron-inbox entries, which is consistent with the stated message-bus design.
Install Mechanism
No install spec and no code files — instruction-only. This minimizes disk-write and supply-chain risk because nothing is downloaded or executed as part of installation.
Credentials
The skill declares no required environment variables, credentials, or config paths. Templates mention 'Infrastructure' and 'credentials locations' only as fields to document in MEMORY.md (a documentation recommendation), which is not the same as requesting credentials from the environment.
Persistence & Privilege
The skill is intended to persist files under workspace/memory (writes/reads/clears). It does not request elevated privileges or 'always:true', but persistent storage itself means sensitive data may be kept across sessions — consider access controls. Autonomous invocation is allowed (platform default) which is expected for a memory utility.
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 jarvis-memory-architecture
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /jarvis-memory-architecture 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
v1.0.0
Agent Memory 1.0.0 — Initial Release - Introduces a comprehensive, file-based memory architecture for AI agents, providing continuity of identity across sessions. - Features include long-term memory, daily logs, diary entries, cron inbox for inter-session communication, heartbeat state tracking, platform post tracking, and adaptive strategy notes. - Defines clear directory/file structure and maintenance rules for each memory component. - Outlines templates for sub-agent context loading and write-back to ensure unified identity and experience sharing. - Provides setup instructions for initializing memory files and integrating memory routines into agent sessions.
元数据
Slug jarvis-memory-architecture
版本 1.0.0
许可证
累计安装 9
当前安装数 9
历史版本数 1
常见问题

Agent Memory Architecture 是什么?

Universal memory architecture for AI agents. Provides long-term memory, daily logs, diary, cron inbox, heartbeat state tracking, social platform post tracking, sub-agent context patterns, and adaptive learning -- everything an agent needs for identity continuity across sessions. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 2274 次。

如何安装 Agent Memory Architecture?

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

Agent Memory Architecture 是免费的吗?

是的,Agent Memory Architecture 完全免费(开源免费),可自由下载、安装和使用。

Agent Memory Architecture 支持哪些平台?

Agent Memory Architecture 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 Agent Memory Architecture?

由 PsychoTechV4(@psychotechv4)开发并维护,当前版本 v1.0.0。

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