Skills agent-memory-setup-v2

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/autosolutionsai-didac/agent-memory-setup-v2" ~/.claude/skills/clawdbot-skills-agent-memory-setup-v2 && rm -rf "$T"

manifest: skills/autosolutionsai-didac/agent-memory-setup-v2/SKILL.md

source content

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

Creates directory structure and stub files via a bash script (no network calls, no env reads, no dependencies)
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 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

"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

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