Sq Memory OpenClaw Plugin & Skill | ClawHub
Looking to integrate Sq Memory into your AI workflows? This free OpenClaw plugin from ClawHub helps you automate coding agents & ides tasks instantly, without having to write custom tools from scratch.
What this skill does
# 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 ```bash npx clawhub install sq-memory ``` Or manually: ```bash git clone https://github.com/wbic16/openclaw-sq-skill.git ~/.openclaw/skills/sq-memory ``` ## Configuration Add to your agent's `.openclaw/config.yaml`: ```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: ```javascript remember("user/name", "Alice") remember("user/preferences/theme", "dark") remember("conversation/2026-02-11/summary", "Discussed phext storage...") ``` ### recall(key) Retrieve stored memory: ```javascript const name = recall("user/name") // "Alice" const theme = recall("user/preferences/theme") // "dark" ``` ### forget(key) Delete memory: ```javascript forget("conversation/2026-02-11/summary") ``` ### list_memories(prefix) List all memories under a coordinate: ```javascript 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 ```javascript // 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 ${newTheme}` } // Agent conversation: User: "I prefer dark mode" Agent: *calls setUserTheme("dark")* Agent: "Got it! I've set your theme to dark mode." // Next session (days later): User: "What's my preferred theme?" Agent: *calls getUserTheme()* Agent: "You prefer dark mode." ``` ## Example: Conversation History ```javascript // Store conversation summaries beyond context window: async function summarizeAndStore(conversationId, summary) { const date = new Date().toISOString().split('T')[0] const key = `conversations/${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):** ```javascript remember("shared/tasks/pending/task-42", "Review pull request #123") ``` **Agent B (reads):** ```javascript 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):** 1. Clone: `git clone https://github.com/wbic16/SQ.git` 2. Build: `cd SQ && cargo build --release` 3. Run: `./target/release/sq 1337` 4. Configure SQ Memory to `http://localhost:1337` **Hosted (Convenience):** 1. Sign up: https://mirrorborn.us 2. Get API key 3. Configure SQ Memory to `https://sq.mirrorborn.us` 4. 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**
Install
openclaw skills install @wbic16/sq-memorynpx clawhub@latest install sq-memoryFull SKILL.md
Open originalSKILL.md content below is scrollable.
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.ior shorthandcategory/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
nullif 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
endpointin config (should behttps://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