How do I install the Memvault skill?

Run npx skillmds@latest add lord1egypt/memvault in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Memvault safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Memvault?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Memvault free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Memvault?

Lord1Egypt (@lord1egypt) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Memvault by lord1egypt — Agent Skill

Name: Memvault
Rating: 4.2 (5 reviews)
Author: Lord1Egypt

Memvault

Production-ready long-term memory server for AI agents with Ebbinghaus decay and strength-weighted retrieval. Use when you need persistent memory across agent sessions, memory decay (forgetting curve), memory statistics, or multi-agent memory tracking. Triggers: long-term memory, remember, recall, memory decay, Ebbinghaus, agent memory, memvault. Requires Docker.

Lord1Egypt Updated Sep 8, 2026 2 repo stars

File contents

MemVault — Long-term Memory for AI Agents

Quick Start

# Install (one command — Docker handles everything)
bash scripts/install.sh

# Verify
memvault health

Usage

# Store a memory
memvault memorize-text <user_id> "<message>" "[reply]"

# Retrieve memories (strength-weighted)
memvault retrieve <user_id> "<query>"

# Run daily decay (memories fade like human memory)
memvault decay <user_id>

# Check stats
memvault stats <user_id>

API Endpoints

Method	Endpoint	Description
POST	`/memorize`	Store conversation → extract facts/events/knowledge
POST	`/retrieve`	Strength-weighted vector search (`similarity × strength`)
POST	`/decay`	Ebbinghaus forgetting curve (run daily via cron)
GET	`/stats`	Memory distribution, access patterns, agent breakdown
GET	`/health`	Service health

How It Works

Store: Conversations → LLM extracts facts → embedded → stored in pgvector
Retrieve: Query embedded → cosine similarity × memory strength → ranked results
Decay: strength = exp(-rate × days / (1 + damping × ln(1 + access_count)))
Access boost: Each retrieval increments access_count, slowing decay

Fading memories (strength < 0.1) are excluded from search.

Configuration

All via environment variables in .env (created by install script):

MEMVAULT_LLM_BASE_URL — Default: Ollama local. Set to OpenAI/Groq/etc URL if preferred
MEMVAULT_LLM_MODEL — Default: qwen2.5:3b
MEMVAULT_TRANSLATION — Set true + MEMVAULT_TRANSLATION_LANG for auto-translation
MEMVAULT_PORT — Default: 8002

Daily Cron Setup

Add Ebbinghaus decay to your agent's cron:

0 3 * * *  curl -s -X POST 'http://127.0.0.1:8002/decay?user_id=YOUR_USER_ID'

TOOLS.md Snippet

## MemVault 🧠
memvault memorize-text "<user_id>" "<content>" "<context>"
memvault retrieve "<user_id>" "<query>"
memvault decay <user_id>
memvault stats <user_id>
- API: 127.0.0.1:8002

Multi-Agent Memory

Tag memories by source agent:

curl -X POST http://localhost:8002/memorize -H "Content-Type: application/json" \
  -d '{"conversation": [
    {"role": "metadata", "content": "{\"source_agent\": \"research-bot\"}"},
    {"role": "user", "content": "Found new papers on transformers"}
  ], "user_id": "team"}'

Troubleshooting

"Connection refused" → Run docker compose -f ~/.openclaw/workspace/skills/memvault/docker-compose.yml up -d
Slow memorize → Normal, LLM extraction takes 5-15s per conversation
No results from retrieve → Check memvault stats — if total=0, nothing stored yet
All memories fading → Reduce decay_rate: curl -X POST 'http://localhost:8002/decay?decay_rate=0.05'

---name: memvaultdescription: Production-ready long-term memory server for AI agents with Ebbinghaus decay and strength-weighted retrieval. Use when you need persistent memory across agent sessions, memory decay (forgetting curve), memory statistics, or multi-agent memory tracking. Triggers: long-term memory, remember, recall, memory decay, Ebbinghaus, agent memory, memvault. Requires Docker.---# MemVault — Long-term Memory for AI Agents## Quick Start```bash# Install (one command — Docker handles everything)bash scripts/install.sh# Verifymemvault health```## Usage```bash# Store a memorymemvault memorize-text <user_id> "<message>" "[reply]"# Retrieve memories (strength-weighted)memvault retrieve <user_id> "<query>"# Run daily decay (memories fade like human memory)memvault decay <user_id># Check statsmemvault stats <user_id>```## API Endpoints| Method | Endpoint | Description ||--------|----------|-------------|| POST | `/memorize` | Store conversation → extract facts/events/knowledge || POST | `/retrieve` | Strength-weighted vector search (`similarity × strength`) || POST | `/decay` | Ebbinghaus forgetting curve (run daily via cron) || GET | `/stats` | Memory distribution, access patterns, agent breakdown || GET | `/health` | Service health |## How It Works1. **Store**: Conversations → LLM extracts facts → embedded → stored in pgvector2. **Retrieve**: Query embedded → cosine similarity × memory strength → ranked results3. **Decay**: `strength = exp(-rate × days / (1 + damping × ln(1 + access_count)))`4. **Access boost**: Each retrieval increments `access_count`, slowing decayFading memories (strength < 0.1) are excluded from search.## ConfigurationAll via environment variables in `.env` (created by install script):- `MEMVAULT_LLM_BASE_URL` — Default: Ollama local. Set to OpenAI/Groq/etc URL if preferred- `MEMVAULT_LLM_MODEL` — Default: `qwen2.5:3b`- `MEMVAULT_TRANSLATION` — Set `true` + `MEMVAULT_TRANSLATION_LANG` for auto-translation- `MEMVAULT_PORT` — Default: `8002`## Daily Cron SetupAdd Ebbinghaus decay to your agent's cron:```0 3 * * * curl -s -X POST 'http://127.0.0.1:8002/decay?user_id=YOUR_USER_ID'```## TOOLS.md Snippet```markdown## MemVault 🧠memvault memorize-text "<user_id>" "<content>" "<context>"memvault retrieve "<user_id>" "<query>"memvault decay <user_id>memvault stats <user_id>- API: 127.0.0.1:8002```## Multi-Agent MemoryTag memories by source agent:```bashcurl -X POST http://localhost:8002/memorize -H "Content-Type: application/json" \ -d '{"conversation": [ {"role": "metadata", "content": "{\"source_agent\": \"research-bot\"}"}, {"role": "user", "content": "Found new papers on transformers"} ], "user_id": "team"}'```## Troubleshooting- **"Connection refused"** → Run `docker compose -f ~/.openclaw/workspace/skills/memvault/docker-compose.yml up -d`- **Slow memorize** → Normal, LLM extraction takes 5-15s per conversation- **No results from retrieve** → Check `memvault stats` — if total=0, nothing stored yet- **All memories fading** → Reduce decay_rate: `curl -X POST 'http://localhost:8002/decay?decay_rate=0.05'`

Frequently asked questions