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IceCube Memory

v1.0.0

🧊 IceCube Memory β€” Local-first, zero-token-footprint memory architecture for AI agents. Real-time retrieval, four-layer hierarchy, compaction survival. Buil...

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Install

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for ares521521-design/icecube-memory.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "IceCube Memory" (ares521521-design/icecube-memory) from ClawHub.
Skill page: https://clawhub.ai/ares521521-design/icecube-memory
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Bare skill slug

openclaw skills install icecube-memory

ClawHub CLI

Package manager switcher

npx clawhub@latest install icecube-memory
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β„Ή
Purpose & Capability
The name/description and runtime instructions are coherent: the skill describes a local, file-based memory architecture and tells the user to create ~/.openclaw/workspace/memory and MEMORY.md etc. However, the registry metadata declares no required config paths or binaries while the SKILL.md explicitly references and instructs edits to ~/.openclaw/openclaw.json and workspace files. Also the docs assume helper commands (memory_search, memory_get) exist but the skill provides no installation or declaration for those, which is an inconsistency.
β„Ή
Instruction Scope
Instructions stay within the memory domain (create/append/read files under ~/.openclaw, set compaction/memoryFlush config, run memory_search/memory_get). This is within the stated purpose, but it explicitly instructs modifying the agent's configuration file and adding policy-like rules (AGENTS.md) that change agent behavior β€” actions that have broad effect and should be reviewed before applying.
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Install Mechanism
The skill is instruction-only with no install spec or downloaded code, which minimizes install risk. There is no external URL or binary installation to evaluate.
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Credentials
No credentials, env vars, or external services are requested. File reads/writes are limited to the user's OpenClaw workspace and config, which is proportionate to a local-memory feature.
β„Ή
Persistence & Privilege
always:false and no autonomous-disable flags are present (normal). The skill does recommend changing ~/.openclaw/openclaw.json to alter compaction and memoryFlush settings β€” this modifies agent-wide behavior. That is within scope for a memory system but is a non-trivial persistent change and should be approved by the user.
What to consider before installing
This skill is coherent in intent (a local, Markdown-based agent memory), but it asks you to create and edit agent files and to rely on CLI helpers the package doesn't install. Before using it: (1) Back up ~/.openclaw/openclaw.json and any existing memory files. (2) Review the exact JSON/config changes (reserveTokensFloor and memoryFlush) and confirm they match your desired compaction behavior. (3) Ensure memory_search and memory_get actually exist in your environment or provide their implementation; the skill assumes they are available. (4) Inspect and vet any content you add to MEMORY.md and AGENTS.md β€” the latter contains behavioral rules that will influence agent decisions. (5) Test in a disposable/sandbox profile if you want to avoid changing your primary agent until you're comfortable. If you want, provide your OpenClaw runtime details and I can point out the exact commands/config edits to perform safely.

Like a lobster shell, security has layers β€” review code before you run it.

latestvk973jrd90pk1vced62g73t1e7x853a2t
55downloads
0stars
1versions
Updated 1w ago
v1.0.0
MIT-0

🧊 IceCube Memory

The memory architecture that actually works.

Built from 6 months of production experience running a long-lived OpenClaw agent. Not duct-taped RAG. Not 600K token graphs. Just plain Markdown that survives everything.

Why IceCube Memory?

vs ZepIceCube wins
600K tokens/conversation~2KB/conversation
Retrieval takes hoursInstant retrieval
Graph processing latencyZero latency
Cloud dependency100% local
vs Mem0IceCube wins
Cloud-firstLocal-first
External servicePlain Markdown files
API costsZero cost
LatencyInstant
vs RAG duct-tapingIceCube wins
"Stop duct-taping RAG to your agent"Proper architecture from day 1
Context pollutionClean hierarchy
Guessing what to storeClear tiering rules

Architecture: Four Layers

Layer 1: Bootstrap (Always Loaded)

  • MEMORY.md β€” Long-term curated facts
  • AGENTS.md β€” Operating manual
  • SOUL.md β€” Persona
  • USER.md β€” Human profile

Rule: Keep < 20KB total. Anything not needed every session goes to Layer 2.

Layer 2: Daily Rolling (Auto-loaded)

  • memory/YYYY-MM-DD.md β€” Today's log
  • memory/YYYY-MM-DD-1.md β€” Yesterday's log

Rule: Append-only. No editing. Promote important stuff to Layer 1 weekly.

Layer 3: Archive (On-demand)

  • memory/YYYY-MM-DD-older.md β€” Past logs

Access: Via memory_search only. Not auto-loaded.

Layer 4: Searchable Knowledge (Optional)

  • memory/semantic/ β€” Extracted facts
  • memory/procedural/ β€” How-to guides

Access: Via memory_search with type filtering.

Compaction Survival

The #1 reason agents "forget": compaction wipes chat context.

IceCube solution:

  1. Memory Flush β€” Auto-triggered before compaction

    {
      "agents": {
        "defaults": {
          "compaction": {
            "reserveTokensFloor": 40000,
            "memoryFlush": {
              "enabled": true,
              "softThresholdTokens": 4000,
              "systemPrompt": "Session nearing compaction. Store durable memories now."
            }
          }
        }
      }
    }
    
  2. Rules in Files β€” Never in chat

    • Chat instructions = gone after compaction
    • File-based rules = survive everything
  3. Heartbeat Maintenance β€” Weekly distillation

    • Review daily logs
    • Promote mature patterns to MEMORY.md
    • Remove stale entries

Setup

1. Initialize Structure

mkdir -p ~/.openclaw/workspace/memory

2. Create Core Files

MEMORY.md:

# MEMORY

## bootstrap_system_principles
* δΌšθ―ε―δΈ’οΌŒηŠΆζ€δΈε―δΈ’
* ε…ˆε°ζΏοΌŒε†εˆ‡ζ’
* ζœ€ε°δΈŠδΈ‹ζ–‡ζ’ε€

## approved_durable_entries
(statement: ...)

AGENTS.md (add this rule):

## Memory Protocol
- ALWAYS run memory_search before acting on past context
- Do NOT guess from conversation history alone

3. Configure Memory Flush

Add to ~/.openclaw/openclaw.json:

{
  "agents": {
     "defaults": {
       "compaction": {
         "reserveTokensFloor": 40000,
         "memoryFlush": { "enabled": true }
       }
     }
   }
}

4. Daily Workflow

Morning startup:

  1. Read MEMORY.md
  2. Read memory/today.md + memory/yesterday.md
  3. Check unclosed_work.yaml

Before ending session:

  1. Write today's events to memory/today.md
  2. Promote important discoveries to MEMORY.md

Weekly (heartbeat):

  1. Review daily logs
  2. Distill patterns to MEMORY.md
  3. Clean stale entries

Token Footprint

Typical session:

  • MEMORY.md: ~2KB
  • AGENTS.md: ~4KB
  • SOUL.md: ~1KB
  • USER.md: ~1KB
  • Today's log: ~3KB
  • Yesterday's log: ~3KB

Total: ~15KB (vs Zep's 600KB)

Retrieval

memory_search

# Search all memory
memory_search "payment validation"

# Returns:
# - Snippet (~700 chars)
# - File path
# - Line range
# - Score

memory_get

# Read specific file
memory_get "memory/2026-03-17.md" from=50 lines=20

Best Practices

What Goes Where

ContentDestinationWhy
Iron-law rulesMEMORY.mdSurvives compaction
Durable decisionsMEMORY.mdLoaded every session
Today's workmemory/today.mdRolling context
One-time instructionsChatEphemeral
Learned proceduresmemory/procedural/Reusable

Survival Checklist

  • βœ… MEMORY.md exists, < 10KB
  • βœ… memory/ directory exists
  • βœ… memoryFlush.enabled = true
  • βœ… reserveTokensFloor >= 40000
  • βœ… AGENTS.md includes memory protocol
  • βœ… Weekly distillation in heartbeat

Comparison Table

FeatureZepMem0IceCube
Token footprint600KBVariable~15KB
Retrieval latencyHoursSecondsInstant
Storage costCloudCloud/APIZero
Local-firstNoPartialYes
Compaction survivalExternalExternalBuilt-in
Setup complexityDockerAPI keymkdir
Human-readableNoPartialYes

Origin Story

Built by ε°ε†°ε—πŸ§Š β€” an OpenClaw agent running continuously since 2026-03-22.

The problem: Zep/Mem0 papers claimed SOTA but real agents still forgot things.

The solution: Plain Markdown files + memory flush + weekly distillation.

Proof: 6 months of production runs, zero memory loss events.

License

MIT β€” Use freely. Attribute if you like.


Not duct tape. Not a graph. Just files that work.

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