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Agent Memory Setup v2 (Gemini Embeddings 2)

Name: Agent Memory Setup v2 (Gemini Embeddings 2)
Author: autosolutionsai-didac

作者 autosolutionsai-didac · GitHub ↗ · v1.0.4 · MIT-0

cross-platform ✓ 安全检测通过

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当前安装

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在 OpenClaw 中安装

/install agent-memory-setup-v2

功能描述

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...

使用说明 (SKILL.md)

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

安全使用建议

This skill is small and coherent, but before installing consider: (1) After setup, your agent will send memory file contents to Google Gemini for embedding — do not store sensitive secrets or personal data in these memory files unless you accept that external transmission. (2) Use a dedicated Google API key/project to limit blast radius and billing exposure; treat GEMINI_API_KEY as secret and rotate/revoke if needed. (3) Review and commit the openclaw.json changes yourself (the skill instructs you to edit it) rather than blindly applying them. (4) The optional 'lossless-claw' plugin is a third-party component — inspect it before installing. (5) The provided script only writes local files to the workspace you specify; run it against a test workspace first if you want to verify behavior.

功能分析

Type: OpenClaw Skill Name: agent-memory-setup-v2 Version: 1.0.4 The skill bundle is designed to initialize a 3-tier memory directory structure for OpenClaw agents and provide configuration steps for Gemini-based semantic search. The bash script `scripts/setup_memory_v2.sh` only creates local directories and stub markdown files, with no network calls or data exfiltration logic. The instructions in `SKILL.md` are transparent about data being sent to Google's API for embeddings as part of the intended functionality.

能力评估

✓ Purpose & Capability

Name/description match the delivered artifacts: a small setup script that creates HOT/WARM/COLD memory files and SKILL.md instructions to configure OpenClaw to use Gemini embeddings. No unrelated binaries, installs, or credentials are requested.

✓ Instruction Scope

SKILL.md instructs only to run the provided bash script (which only writes local files), edit openclaw.json, export GEMINI_API_KEY, and restart the gateway. It warns that subsequent embedding calls will send memory content to Google's API—no instructions read unrelated system files or exfiltrate data.

✓ Install Mechanism

No install spec; the skill is instruction-only plus a single local script. The script is a small, readable bash file and does not download or execute remote code.

✓ Credentials

The only credential the skill requires in practice is the GEMINI_API_KEY described in the README; that is appropriate for enabling a cloud embedding provider. No other secrets or unrelated environment variables are requested.

✓ Persistence & Privilege

always:false and the skill does not alter other skills or system-wide config automatically. It needs a manual edit of openclaw.json and a manual gateway restart; normal autonomous invocation settings are unchanged.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install agent-memory-setup-v2
安装完成后，直接呼叫该 Skill 的名称或使用 /agent-memory-setup-v2 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v1.0.4

Remove AGENTS template, strip to minimal scope: directory creation + config docs only. Add explicit privacy notice.

v1.0.3

Remove 'Don't ask permission' guidance from AGENTS template, add explicit memory safety section about not writing sensitive data and only loading MEMORY.md in private sessions

v1.0.2

Declare external Gemini API usage transparently, remove non-standard package.json, clean metadata to match AgentSkills spec

v1.0.1

Security hardening: removed env var reads, plugin introspection, and config file path references from setup script to reduce false positive flags

v1.0.0

Initial release: 3-tier memory system using Google Gemini Embeddings 2 for semantic search instead of local QMD

元数据

Slug agent-memory-setup-v2

版本 1.0.4

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 5

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