Tencent SkillHub · AI

SQ Memory

Enables OpenClaw agents to store, recall, update, list, and forget persistent hierarchical memories across sessions via the SQ protocol.

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Enables OpenClaw agents to store, recall, update, list, and forget persistent hierarchical memories across sessions via the SQ protocol.

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Install for OpenClaw

Quick setup

Download the package from Yavira.
Extract the archive and review SKILL.md first.
Import or place the package into your OpenClaw setup.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: .gitignore, CHANGELOG.md, CONTRIBUTING.md, index.js, LICENSE.md, package.json

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Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.1

Provenance

Publisher: wbic16
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 25 sections Open source page

SQ Memory - OpenClaw Skill

Give your OpenClaw agents permanent memory.

Open Source & MIT Licensed

SQ is open-source software you can run yourself or use our hosted version. Source Code: https://github.com/wbic16/SQ License: MIT (free forever, modify/sell/distribute) Self-Host: Free (5 minute setup) Hosted Option: Paid convenience service at mirrorborn.us

What This Skill Does

OpenClaw agents lose all memory between sessions. Every restart = amnesia. This skill connects your agent to SQ—persistent 11D text storage. Your agent can: Remember user preferences across sessions Store conversation history beyond context limits Share memory with other agents Never hallucinate forgotten details again

Installation

npx clawhub install sq-memory Or manually: git clone https://github.com/wbic16/openclaw-sq-skill.git ~/.openclaw/skills/sq-memory

Configuration

Add to your agent's .openclaw/config.yaml: skills: sq-memory: enabled: true endpoint: http://localhost:1337 username: your-username password: your-api-key namespace: agent-name # Isolates this agent's memory

Usage

Your agent automatically gets new memory tools:

remember(key, value)

Store something for later: remember("user/name", "Alice") remember("user/preferences/theme", "dark") remember("conversation/2026-02-11/summary", "Discussed phext storage...")

recall(key)

Retrieve stored memory: const name = recall("user/name") // "Alice" const theme = recall("user/preferences/theme") // "dark"

forget(key)

Delete memory: forget("conversation/2026-02-11/summary")

list_memories(prefix)

List all memories under a coordinate: const prefs = list_memories("user/preferences/") // Returns: ["user/preferences/theme", "user/preferences/language", ...]

Coordinate Structure

Memories are stored at 11D coordinates. The skill uses this convention: namespace.1.1 / category.subcategory.item / 1.1.1 Example: Agent namespace: my-assistant User preference for theme: my-assistant.1.1/user.preferences.theme/1.1.1 This means: Each agent has isolated memory (namespace collision impossible) Memories are hierarchically organized You can share coordinates between agents if needed

Example: User Preference Agent

// In your agent's system prompt or skill code: async function getUserTheme() { const theme = recall("user/preferences/theme") return theme || "light" // Default to light if not set } async function setUserTheme(newTheme) { remember("user/preferences/theme", newTheme) return `Theme set to ${newTheme}` } // Agent conversation: User: "I prefer dark mode" Agent: *calls setUserTheme("dark")* Agent: "Got it! I've set your theme to dark mode." // Next session (days later): User: "What's my preferred theme?" Agent: *calls getUserTheme()* Agent: "You prefer dark mode."

Example: Conversation History

// Store conversation summaries beyond context window: async function summarizeAndStore(conversationId, summary) { const date = new Date().toISOString().split('T')[0] const key = `conversations/${date}/${conversationId}/summary` remember(key, summary) } async function recallConversation(conversationId) { const memories = list_memories(`conversations/`) return memories .filter(m => m.includes(conversationId)) .map(key => recall(key)) } // Usage: summarizeAndStore("conv-123", "User asked about phext storage, explained 11D coordinates") // Later: const history = recallConversation("conv-123") // Agent can recall what was discussed even after context window cleared

Advanced: Multi-Agent Coordination

Multiple agents can share memory at agreed coordinates: Agent A (writes): remember("shared/tasks/pending/task-42", "Review pull request #123") Agent B (reads): const task = recall("shared/tasks/pending/task-42") // Sees: "Review pull request #123" This enables true multi-agent workflows.

API Reference

All functions are available in the sq namespace:

sq.remember(coordinate, text)

coordinate: String in format a.b.c/d.e.f/g.h.i or shorthand category/item text: String to store (max 1MB per coordinate) Returns: {success: true, coordinate: "full.coordinate.path"}

sq.recall(coordinate)

coordinate: String (exact match) Returns: String (stored text) or null if not found

sq.forget(coordinate)

coordinate: String (exact match) Returns: {success: true} or {success: false, error: "..."}

sq.list_memories(prefix)

prefix: String (e.g., "user/" matches all user memories) Returns: Array of coordinate strings

sq.update(coordinate, text)

Alias for remember() (overwrites existing)

Rate Limits

Free tier: 1,000 API calls/day, 100MB storage SQ Cloud ($50/mo): 10,000 API calls/day, 1TB storage Enterprise: Custom limits

Troubleshooting

"Connection refused" error: Check your endpoint in config (should be https://sq.mirrorborn.us) Verify credentials are correct "Quota exceeded" error: You've hit rate limits Upgrade to SQ Cloud or wait for daily reset Memory not persisting: Check namespace isolation (each agent needs unique namespace) Verify coordinate format is valid

Why SQ?

Open source & MIT licensed: Run it yourself for free Modify it to fit your needs No vendor lock-in Transparent codebase Not a vector database: Agents can read stored text (not just search embeddings) Structured by coordinates (not similarity) Deterministic retrieval (no relevance ranking guesses) Not Redis: Persistent (survives restarts) 11D addressing (not flat key-value) Immutable history (WAL for time-travel) Built for agents: Coordinate system matches agent thinking (hierarchical) No schema overhead Scales from KB to TB

Get SQ

Self-Host (Free): Clone: git clone https://github.com/wbic16/SQ.git Build: cd SQ && cargo build --release Run: ./target/release/sq 1337 Configure SQ Memory to http://localhost:1337 Hosted (Convenience): Sign up: https://mirrorborn.us Get API key Configure SQ Memory to https://sq.mirrorborn.us Pay $50/mo (or use free tier)

Support

Discord: https://discord.gg/kGCMM5yQ Docs: https://mirrorborn.us/help.html GitHub: https://github.com/wbic16/SQ Built by Mirrorborn 🦋 for the OpenClaw ecosystem

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

3 Docs1 Scripts1 Config1 Files

CHANGELOG.md Docs
CONTRIBUTING.md Docs
LICENSE.md Docs
index.js Scripts
package.json Config
.gitignore Files