agent-memory-setup-v2
Create a 3-tier memory directory structure (HOT/WARM/COLD) for OpenClaw agents and configure the built-in memory-core plugin to use Google Gemini Embeddings 2 (gemini-embedding-2-preview) for semantic memory search. Creates memory/ directories and stub files only — no code execution or external API calls from the setup script. After setup, the agent's memory_search tool uses Gemini's cloud embedding API to index memory files. Requires a free Google Gemini API key. Use when setting up a new agent's memory system or asked about semantic memory search. Triggers on "set up memory", "memory setup", "agent memory", "gemini memory", "semantic search memory", "onboard new agent".
Best use case
agent-memory-setup-v2 is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Create a 3-tier memory directory structure (HOT/WARM/COLD) for OpenClaw agents and configure the built-in memory-core plugin to use Google Gemini Embeddings 2 (gemini-embedding-2-preview) for semantic memory search. Creates memory/ directories and stub files only — no code execution or external API calls from the setup script. After setup, the agent's memory_search tool uses Gemini's cloud embedding API to index memory files. Requires a free Google Gemini API key. Use when setting up a new agent's memory system or asked about semantic memory search. Triggers on "set up memory", "memory setup", "agent memory", "gemini memory", "semantic search memory", "onboard new agent".
Teams using agent-memory-setup-v2 should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/agent-memory-setup-v2/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How agent-memory-setup-v2 Compares
| Feature / Agent | agent-memory-setup-v2 | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Create a 3-tier memory directory structure (HOT/WARM/COLD) for OpenClaw agents and configure the built-in memory-core plugin to use Google Gemini Embeddings 2 (gemini-embedding-2-preview) for semantic memory search. Creates memory/ directories and stub files only — no code execution or external API calls from the setup script. After setup, the agent's memory_search tool uses Gemini's cloud embedding API to index memory files. Requires a free Google Gemini API key. Use when setting up a new agent's memory system or asked about semantic memory search. Triggers on "set up memory", "memory setup", "agent memory", "gemini memory", "semantic search memory", "onboard new agent".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Agent Memory Setup v2 — Gemini Embeddings 2
Create a 3-tier memory directory structure for OpenClaw agents and configure semantic
search using **Google Gemini Embeddings 2**.
## What This Skill Does
1. **Creates directory structure and stub files** via a bash script (no network calls, no env reads, no dependencies)
2. **Provides configuration instructions** for openclaw.json to enable Gemini-based memory search
## Privacy Notice
⚠️ **After setup**, the agent's `memory_search` tool sends memory file content to
Google's Gemini embedding API for vectorization. This is how semantic search works —
files must be embedded to be searchable. The setup script itself makes no external calls.
## Prerequisite
Google Gemini API key — free at https://aistudio.google.com/apikey
## Setup
### Step 1: Create directory structure
```bash
bash scripts/setup_memory_v2.sh /path/to/agent/workspace
```
Creates: `memory/`, `memory/hot/`, `memory/warm/`, stub `.md` files, `heartbeat-state.json`.
### Step 2: Configure openclaw.json
Add under `agents.defaults`:
```json
"memorySearch": { "provider": "gemini" },
"compaction": { "mode": "safeguard" },
"contextPruning": { "mode": "cache-ttl", "ttl": "1h" },
"heartbeat": { "every": "1h" }
```
Set API key: `export GEMINI_API_KEY=your-key`
Enable plugin: `"lossless-claw": { "enabled": true }`
### Step 3: Restart
```bash
openclaw gateway restart
```
## Memory Tiers
- 🔥 **HOT** (`memory/hot/HOT_MEMORY.md`) — Active session state, pending actions
- 🌡️ **WARM** (`memory/warm/WARM_MEMORY.md`) — Stable preferences, references
- ❄️ **COLD** (`MEMORY.md`) — Long-term milestones and distilled lessons
## Optional Plugin
**Lossless Claw** (`@martian-engineering/lossless-claw`) — compacts old context into
expandable summaries to prevent amnesia. Install separately:
`openclaw plugins install @martian-engineering/lossless-claw`Related Skills
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