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