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// Skill profile

Agent Memory Setup v2 — Gemini Embeddings 2

name: agent-memory-setup-v2

by autosolutionsai-didac · published 2026-04-01

API集成
Total installs
0
Stars
★ 0
Last updated
2026-04
// Install command
$ claw add gh:autosolutionsai-didac/autosolutionsai-didac-agent-memory-setup-v2
View on GitHub
// Full documentation

---

name: agent-memory-setup-v2

description: >

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".

---

# 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 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`

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