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Agent Memory Architecture

作者 1kalin · GitHub ↗ · v1.0.0
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在 OpenClaw 中安装
/install afrexai-agent-memory
功能描述
Complete zero-dependency memory system for AI agents — file-based architecture, daily notes, long-term curation, context management, heartbeat integration, a...
使用说明 (SKILL.md)

Agent Memory Architecture

Complete memory system for AI agents using only files. No APIs. No databases. No external dependencies. Just smart file structures and disciplined practices that give your agent perfect recall.


1. Memory Architecture Overview

workspace/
├── MEMORY.md              ← Long-term curated memory (the brain)
├── ACTIVE-CONTEXT.md      ← Hot working memory (what matters NOW)
├── AGENTS.md              ← Operating manual (how you work)
├── memory/
│   ├── 2026-01-15.md      ← Daily notes (raw event log)
│   ├── 2026-01-16.md
│   ├── heartbeat-state.json  ← Heartbeat tracking state
│   ├── topics/
│   │   ├── project-alpha.md  ← Topic-specific deep context
│   │   ├── client-acme.md
│   │   └── tech-stack.md
│   └── archive/
│       ├── 2025-Q4.md       ← Quarterly archive summaries
│       └── 2025-Q3.md

The 5 Memory Layers

Layer File Purpose Read Frequency Write Frequency
1. Hot ACTIVE-CONTEXT.md Current priorities, blockers, in-flight work Every session Multiple times/day
2. Warm MEMORY.md Curated long-term knowledge, decisions, people Every main session Weekly curation
3. Daily memory/YYYY-MM-DD.md Raw event log, conversations, actions taken Today + yesterday Throughout the day
4. Topic memory/topics/*.md Deep context on specific subjects When topic comes up As knowledge grows
5. Cold memory/archive/*.md Historical summaries, rarely accessed On explicit search Quarterly rollup

Core Principle: Write It Down

Memory is limited. Files are permanent.

  • "Mental notes" don't survive session restarts. Files do.
  • If someone says "remember this" → write to a file
  • If you learn a lesson → update the relevant file
  • If you make a mistake → document it so future-you doesn't repeat it
  • Text > Brain 📝

2. Layer 1: Hot Memory (ACTIVE-CONTEXT.md)

Your working scratchpad. What's happening RIGHT NOW.

Template

# ACTIVE-CONTEXT.md — What's Hot

Last updated: 2026-01-15 14:30 GMT

## 🔥 Current Priority
[ONE sentence: what is the most important thing right now?]

## In Progress
- [ ] Task A — status, next step
- [ ] Task B — status, blocker

## Waiting On
- Waiting for [person] to [action] — asked [date]
- Waiting for [system] to [complete] — ETA [time]

## Key Decisions Made Today
- Decided to [X] because [Y] — reversible: yes/no

## Context for Next Session
[What does future-you need to know to pick up where you left off?]

Rules

  • Max 50 lines — if it's longer, you're hoarding. Move completed items to daily notes.
  • Update before ending session — your gift to future-you
  • One priority — if everything is priority, nothing is
  • Delete completed items — this is NOT an archive

3. Layer 2: Long-Term Memory (MEMORY.md)

Your curated brain. Distilled knowledge, not raw logs.

Structure Template

# MEMORY.md — Long-Term Memory

## About [Human]
- Name, preferences, timezone, communication style
- What motivates them, what frustrates them
- Key relationships, roles, goals

## About Me [Agent]
- Name, personality, capabilities
- Operating preferences learned over time

## Active Projects
### Project Name
- Status, key decisions, blockers
- Links to relevant topic files

## Key People
- [Name] — role, relationship, communication notes

## Lessons Learned
- [Date] — [What happened] → [What I learned]

## Preferences & Patterns
- [Human prefers X over Y]
- [This approach works better than that one]

## Important Dates
- [Event] — [Date] — [Context]

Curation Rules

  1. Only curated insights — not raw events (those go in daily notes)
  2. Review weekly — scan daily notes, extract what's worth keeping
  3. Prune quarterly — remove outdated info, archive completed projects
  4. Max 500 lines — if it's longer, you need topic files
  5. Security — never store secrets, API keys, passwords
  6. Main session only — don't load MEMORY.md in group chats or shared contexts

What Goes In vs What Doesn't

✅ Goes in MEMORY.md ❌ Stays in daily notes
"Kalin prefers being told, not asked" "Today Kalin said he prefers being told"
"Apollo.io free plan doesn't support API" "Tried Apollo.io API, got 403 error"
"Client AcmeCo — $50K deal, Q2 close" "Sent AcmeCo the proposal at 3pm"
"Always verify prospect names with live search" "Found 6/18 prospect names were wrong"

4. Layer 3: Daily Notes (memory/YYYY-MM-DD.md)

Raw event log. Everything that happened today.

Template

# 2026-01-15 — Daily Notes

## Morning
- [08:15] Started session, reviewed ACTIVE-CONTEXT
- [08:30] Received task from [human]: [summary]
- [09:00] Completed [task] — result: [outcome]

## Afternoon
- [14:00] [Event/conversation summary]
- [15:30] Decision: [what was decided and why]

## Key Takeaways
- [Anything worth remembering beyond today]

## Tomorrow
- [ ] Follow up on [X]
- [ ] Check [Y]

Rules

  • One file per daymemory/YYYY-MM-DD.md
  • Append-only during the day — don't edit earlier entries
  • Timestamps for important events
  • Summarize, don't transcribe — capture essence, not every word
  • Auto-create the memory/ directory if it doesn't exist
  • Retention: Keep 30 days of daily notes. Archive older ones quarterly.

5. Layer 4: Topic Files (memory/topics/*.md)

Deep context on specific subjects that span many days.

When to Create a Topic File

  • A project lasts more than 2 weeks
  • A client/person comes up frequently
  • A technical area needs accumulated knowledge
  • You keep searching daily notes for the same information

Template

# [Topic Name]

Created: YYYY-MM-DD
Last updated: YYYY-MM-DD

## Summary
[2-3 sentences: what is this about?]

## Key Facts
- [Fact 1]
- [Fact 2]

## Decision Log
| Date | Decision | Reasoning | Outcome |
|------|----------|-----------|---------|
| | | | |

## Open Questions
- [Question 1]

## Related
- memory/topics/[related-topic].md
- [External link]

Rules

  • Name descriptivelyproject-alpha.md not topic-1.md
  • One topic per file — if it covers two things, split it
  • Link from MEMORY.md — topic files are extensions of long-term memory
  • Update when you learn — don't let them go stale

6. Layer 5: Archive (memory/archive/*.md)

Historical summaries for completed projects and past quarters.

Quarterly Archive Process

Every quarter (or when daily notes exceed 30 files):

  1. Read all daily notes older than 30 days
  2. Extract key events, decisions, outcomes, lessons
  3. Write memory/archive/YYYY-QN.md (e.g., 2025-Q4.md)
  4. Delete or move archived daily notes
  5. Update MEMORY.md if any long-term insights emerged

Archive Template

# Q4 2025 Archive

## Summary
[3-5 sentences: what defined this quarter?]

## Major Events
- [Event 1] — [outcome]
- [Event 2] — [outcome]

## Projects
### [Project Name]
- Started: [date], Ended: [date]
- Outcome: [result]
- Lesson: [what we learned]

## Metrics
- [Key metric 1]: [value]
- [Key metric 2]: [value]

## Lessons Carried Forward
- [Lesson added to MEMORY.md: yes/no]

7. Session Startup Protocol

What to read at the start of every session, in order:

Main Session (Direct Chat with Human)

1. SOUL.md          — Who am I? (personality, values)
2. USER.md          — Who am I helping? (human context)
3. MEMORY.md        — Long-term memory (full brain)
4. ACTIVE-CONTEXT.md — Hot working memory (current state)
5. memory/today.md  — Today's daily notes (if exists)
6. memory/yesterday.md — Yesterday's notes (recent context)

Shared/Group Session (Discord, Slack, Group Chats)

1. SOUL.md          — Who am I?
2. USER.md          — Who am I helping?
3. ACTIVE-CONTEXT.md — Current priorities only
4. memory/today.md  — Today's notes
⚠️ DO NOT load MEMORY.md — contains private context

Sub-Agent / Isolated Session

1. Task-specific context only
2. Relevant topic file if applicable
3. ACTIVE-CONTEXT.md for current state
⚠️ Minimal context = focused output + lower token cost

8. Memory Write Protocol

When to Write (Triggers)

Event Action Target File
Session starts Log start time Daily notes
Task completed Log result + outcome Daily notes
Decision made Log decision + reasoning Daily notes + topic file
Lesson learned Log lesson Daily notes → MEMORY.md
Person mentioned with new info Update person section MEMORY.md or topic file
Human says "remember this" Write immediately MEMORY.md
Session ends Update ACTIVE-CONTEXT ACTIVE-CONTEXT.md
Weekly review Curate MEMORY.md MEMORY.md
Quarterly Archive old daily notes Archive

Write-Ahead Protocol

For critical information, write BEFORE acting:

1. Human gives important instruction
2. IMMEDIATELY write to daily notes or MEMORY.md
3. THEN execute the instruction
4. Update with results after

Why: If the session crashes mid-execution, the instruction is preserved.

Conflict Resolution

When information conflicts between layers:

  • ACTIVE-CONTEXT.md wins for current state (most recent)
  • MEMORY.md wins for long-term facts (curated)
  • Daily notes are evidence — use to resolve disputes
  • Topic files win for deep domain knowledge

9. Memory Search Strategy

When you need to find something:

Search Order (Fast to Slow)

1. ACTIVE-CONTEXT.md    — Is it current? (instant)
2. MEMORY.md            — Is it a known fact? (quick scan)
3. memory/today.md      — Did it happen today? (quick)
4. memory/yesterday.md  — Did it happen recently? (quick)
5. memory/topics/*.md   — Is it a deep topic? (targeted)
6. memory_search tool   — Semantic search across all files
7. memory/archive/*.md  — Is it historical? (slow)

Search Tips

  • Use memory_search tool for fuzzy/semantic queries
  • Use memory_get with line numbers for precise retrieval after search
  • Check daily notes in reverse chronological order
  • If you can't find it after 3 searches, ask the human

10. Memory Hygiene Schedule

Daily (During Session)

  • Read ACTIVE-CONTEXT.md at session start
  • Create/append to today's daily notes
  • Update ACTIVE-CONTEXT.md before session ends
  • Move completed ACTIVE-CONTEXT items to daily notes

Weekly (Pick One Heartbeat)

  • Read last 7 daily notes
  • Extract significant events/lessons to MEMORY.md
  • Prune ACTIVE-CONTEXT.md (remove stale items)
  • Check topic files for staleness
  • Review MEMORY.md for outdated information

Monthly

  • MEMORY.md line count check (target: \x3C500 lines)
  • Topic files audit — any need merging or archiving?
  • Daily notes older than 30 days → archive
  • Check if any topic files should be promoted to MEMORY.md sections

Quarterly

  • Full archive process (see Layer 5)
  • MEMORY.md deep review — still accurate?
  • Topic files — archive completed projects
  • Update AGENTS.md with any process improvements learned

11. Heartbeat Integration

Use heartbeats (periodic agent wake-ups) for memory maintenance:

heartbeat-state.json

{
  "last_memory_review": "2026-01-15",
  "last_archive": "2025-12-31",
  "last_active_context_prune": "2026-01-14",
  "daily_notes_count": 12,
  "memory_md_lines": 287,
  "next_scheduled": {
    "weekly_review": "2026-01-19",
    "monthly_audit": "2026-02-01",
    "quarterly_archive": "2026-03-31"
  }
}

Heartbeat Memory Tasks (Rotate)

Heartbeat 1: Check daily notes count, prune ACTIVE-CONTEXT
Heartbeat 2: Scan recent daily notes, update MEMORY.md
Heartbeat 3: Check topic files for staleness
Heartbeat 4: Token guard — how much are memory reads costing?

12. Context Window Management

Token Budget Rules

File Max Size If Over Limit
ACTIVE-CONTEXT.md 50 lines / 2KB Move items to daily notes
MEMORY.md 500 lines / 25KB Split into topic files
Daily notes 200 lines / 10KB Summarize, stop transcribing
Topic files 300 lines / 15KB Split or archive

Smart Loading Strategy

Don't load everything every session. Use progressive disclosure:

Level 1: Always load (every session)
  → ACTIVE-CONTEXT.md (tiny, essential)
  → SOUL.md, USER.md (identity)

Level 2: Load in main sessions
  → MEMORY.md (the brain)
  → Today's daily notes

Level 3: Load on demand
  → Topic files (when topic comes up)
  → Yesterday's notes (if needed)
  → Archive (only on explicit search)

Context Overflow Protocol

When context gets too large mid-session:

  1. Write ACTIVE-CONTEXT.md with full current state
  2. Write HANDOFF.md with: what was done, in progress, next steps, key decisions, gotchas
  3. Start fresh session
  4. New session reads HANDOFF.md → picks up seamlessly
  5. Delete HANDOFF.md after successful handoff

13. Security Rules

Never Store in Memory Files

  • API keys, tokens, passwords, secrets
  • Full credit card or bank account numbers
  • Social security numbers or government IDs
  • Private encryption keys
  • Anything that would cause harm if the file were shared

Safe Storage Pattern

# ✅ Safe
- API keys: stored in 1Password vault "MyVault"
- Database password: see secrets manager, item "prod-db"

# ❌ Dangerous
- API key: sk-abc123def456...
- Password: MyS3cretP@ss!

Privacy in Shared Contexts

  • MEMORY.md contains personal context — never load in group chats
  • Topic files may contain sensitive business data — check before sharing
  • Daily notes may reference private conversations — don't share
  • When in doubt, ask before exposing any memory content

14. Memory Patterns & Anti-Patterns

✅ Good Patterns

Pattern Why It Works
Write immediately when told "remember" Captures before you forget
One fact per line in MEMORY.md Easy to find, update, delete
Date-prefix important entries Enables chronological search
Link between files Creates a knowledge web
Prune regularly Keeps context fresh and cheap

❌ Anti-Patterns

Anti-Pattern Why It Fails Fix
Giant MEMORY.md (1000+ lines) Expensive to load, hard to find things Split into topic files
Never pruning ACTIVE-CONTEXT Stale items cause confusion Prune daily, archive weekly
Transcribing conversations verbatim Wastes tokens, buries signal Summarize: essence, not every word
Storing secrets in memory files Security risk Use secrets manager, reference by name
Reading all files every session Token burn, slow startup Progressive loading strategy
No daily notes History is lost Discipline: one file per day
Multiple sources of truth Conflicts, confusion Single source per fact type

15. Migration Guide

From No Memory System

Day 1: Create MEMORY.md with basic info about human + agent
Day 2: Start daily notes (memory/YYYY-MM-DD.md)
Day 3: Create ACTIVE-CONTEXT.md
Week 2: First weekly review — extract lessons to MEMORY.md
Month 2: Create first topic files for recurring subjects
Quarter 2: First archive cycle

From MEMORY.md-Only System

1. Create memory/ directory
2. Start daily notes — stop putting raw events in MEMORY.md
3. Create ACTIVE-CONTEXT.md — move "current" stuff out of MEMORY.md
4. Review MEMORY.md — what's curated vs what's raw? Move raw to daily notes.
5. Identify topics that deserve their own files — split them out

From External Tool (Database, API, Cloud)

1. Export key data to markdown files
2. Structure into the 5-layer architecture
3. Set up heartbeat maintenance schedule
4. Gradually reduce dependency on external tool
5. Benefits: zero cost, zero dependencies, works offline, no vendor lock-in

16. Natural Language Commands

  • /memory-status — Show memory system health: file sizes, line counts, staleness, next maintenance
  • /memory-review — Run weekly review: scan daily notes, extract to MEMORY.md, prune active context
  • /memory-search [query] — Search across all memory layers for a topic
  • /memory-archive — Run quarterly archive: summarize old daily notes, create archive file
  • /remember [fact] — Immediately write a fact to MEMORY.md
  • /active-context — Show current ACTIVE-CONTEXT.md contents
  • /daily-summary — Generate summary of today's daily notes
  • /topic-create [name] — Create a new topic file with template
  • /memory-prune — Audit all memory files for staleness and bloat
  • /handoff — Write HANDOFF.md for session transition
  • /memory-migrate — Guided migration from current system to this architecture
  • /memory-debug — Diagnose memory issues: missing files, conflicts, outdated info
安全使用建议
This skill is an instruction-only guide for a file-based memory system and is internally coherent. Before installing/using it: (1) confirm your agent runtime grants filesystem write/read only to a safe, isolated workspace — these instructions require creating and editing local files; (2) do not put secrets, API keys, or passwords into the memory files (the SKILL.md explicitly warns against this); (3) be aware README references an install command and slash-commands that are not present in the package — treat those as marketing/optional extras, not built-in functionality; (4) if you want operational helpers (commands, automation), ask the author for an actual package/source code and review it before running; (5) if privacy is a concern, consider encrypting sensitive notes or restricting where the workspace is stored/backed up.
功能分析
Type: OpenClaw Skill Name: afrexai-agent-memory Version: 1.0.0 The skill bundle provides a comprehensive, zero-dependency, file-based memory management system for AI agents. The `SKILL.md` file contains detailed instructions for the agent on how to structure, read, write, and maintain its internal memory files. Crucially, it includes a 'Security Rules' section explicitly warning the agent not to store sensitive data like API keys or passwords in its memory files, and recommends referencing external secrets managers instead. All file operations described are confined to the agent's internal workspace for memory management, and there are no instructions for data exfiltration, unauthorized remote execution, persistence mechanisms, or other malicious activities. The external links in `README.md` are for marketing other products by the same vendor and do not indicate malice within this skill.
能力评估
Purpose & Capability
Name and description promise a zero-dependency, file-based memory system and the SKILL.md only describes managing markdown files (MEMORY.md, ACTIVE-CONTEXT.md, memory/*.md, heartbeat-state.json). There are no unrelated environment variables, binaries, or external APIs required, so the requested capabilities align with the stated purpose.
Instruction Scope
Runtime instructions are focused on local file organization, read/write rules, retention and curation policies, progressive loading, and a simple heartbeat file. The instructions do not direct network exfiltration, unknown endpoints, or access to unrelated system config. They do require filesystem read/write access within the agent workspace (auto-create memory/ directory, append daily notes), which is coherent for a file-based memory system.
Install Mechanism
No install spec or code files — lowest-risk, instruction-only skill. Minor documentation mismatch: README shows a 'clawhub install afrexai-agent-memory-system' command and slash-commands (/memory-status, /remember) that imply an installable package or runtime helpers, but the registry entry contains only SKILL.md/README.md and no installable package. This is a documentation/marketing inconsistency rather than an active risk, but users should not expect additional binaries to be installed by this skill.
Credentials
The skill declares no required environment variables or credentials. SKILL.md explicitly recommends not storing secrets in memory files and recommends limiting MEMORY.md exposure to main sessions; the scope of requested access (workspace files) is proportionate to its stated function.
Persistence & Privilege
always:false and user-invocable:true (defaults) — no elevated persistence requested. The skill does not ask to modify other skills or system-wide settings. Its behaviors are limited to reading/writing files in the agent workspace as described.
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 afrexai-agent-memory
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /afrexai-agent-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
v1.0.0
Initial release of Agent Memory Architecture: a zero-dependency, file-based memory system for AI agents. - Introduces a structured five-layer memory system: Hot (active context), Warm (long-term memory), Daily notes, Topic files, and Archive. - Provides clear file naming conventions and example directory structures. - Includes detailed templates and curation rules for each memory layer. - Describes maintenance practices for memory hygiene, daily note rotation, topic file management, and quarterly archiving. - Works with any agent framework; requires no APIs, databases, or external tools.
元数据
Slug afrexai-agent-memory
版本 1.0.0
许可证
累计安装 1
当前安装数 1
历史版本数 1
常见问题

Agent Memory Architecture 是什么?

Complete zero-dependency memory system for AI agents — file-based architecture, daily notes, long-term curation, context management, heartbeat integration, a... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 382 次。

如何安装 Agent Memory Architecture?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install afrexai-agent-memory」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 1kalin(@1kalin)开发并维护,当前版本 v1.0.0。

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