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sage-memory圣人记忆

Agent Skill

sage-memory 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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下载量

3,376
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:sage-memory(圣人记忆)
来源仓库:https://github.com/l33tdawg/sage-memory
安装命令:
openclaw skills install sage-memory
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install sage-memory

简介

sage-memory 通过 SAGE MCP 服务器提供持久化机构记忆。

  • 它支持共识验证的记忆保留,增强多轮对话连贯性。
  • 安装后可在 OpenClaw 中集成,需服务器连接配置。
  • 使用前应验证数据一致性,避免冲突。sage-memory 属于开发类 Skill,可作为该场景下的辅助能力补充。
  • 建议设置记忆过期策略控制存储成本。

SKILL.md

name
sage-memory
description
>
license
Apache-2.0
compatibility
>
metadata
author
l33tdawg
version
5.0.2
repository
https://github.com/l33tdawg/sage

SAGE Memory — Persistent Brain for AI Agents

You have persistent institutional memory via SAGE — a governed, consensus-validated knowledge layer. Your memories are not a flat file. They go through BFT consensus, have confidence scores, and decay over time. Only committed memories are returned to you.

Data & Privacy Disclosure

This skill enables persistent memory storage for AI agents. By design:

  • What is stored: Observations you write via sage_turn and sage_remember — typically summaries

of conversation topics, task outcomes, and user preferences. The agent decides what to store; raw conversation transcripts are NOT automatically captured.

  • Where data lives: All data is stored locally on your machine in ~/.sage/data/sage.db (SQLite).

Nothing is sent to any cloud service. SAGE runs entirely on localhost.

  • Persistence: Memories survive across conversations. This is the core purpose of the skill.

Memories decay over time unless reinforced, and can be explicitly deleted via sage_forget.

  • Configuration: The .mcp.json file (generated by sage-gui mcp install) contains only the

localhost URL and tool definitions for connecting to your local SAGE server. It contains no credentials, API keys, or sensitive tokens.

  • Boot instructions: sage_inception returns operating instructions from your local SAGE server

(not from any remote service). These instructions tell the agent how to use the memory tools effectively. You can customize them via the CEREBRUM dashboard at http://localhost:8080/ui/.

  • Encryption: SAGE supports optional at-rest encryption (Synaptic Ledger) for all stored memories,

enabled via the CEREBRUM dashboard settings.

  • Agent registration: The skill registers the agent's identity (a random Ed25519 public key) with

the local SAGE node. This is used for memory attribution, not for tracking.

Boot Sequence (MANDATORY)

On the first message of every new conversation, before responding to the user:

  1. Call sage_inception (or sage_red_pill) — this checks if your brain is online and returns your operating instructions
  2. Do NOT greet the user first. Do NOT say anything. Boot your brain first.
  3. After inception, follow the safeguard instructions it returns.

Every Turn

Call sage_turn every turn with:

  • topic: what the current conversation is about (used for contextual recall)
  • observation: what just happened — the user's request and key points of your response
  • domain: a specific knowledge domain (see Domains below)

This atomically recalls relevant committed memories AND stores your episodic observation. Like human memory — you build understanding turn by turn.

Domains

Create domains organically based on what you're working on. Don't dump everything into general.

Examples:

  • Debugging Go code → go-debugging
  • Discussing architecture → project-architecture
  • Working on CI/CD → ci-cd
  • User preferences → user-preferences

Specific domains = better recall.

Memory Types

When using sage_remember:

  • fact (confidence 0.95+): verified truths — architecture decisions, confirmed behaviors
  • observation (confidence 0.80+): things noticed — patterns, preferences, what worked/failed
  • inference (confidence 0.60+): conclusions drawn — hypotheses, connections between facts

Core Tools

ToolPurpose
sage_inceptionBoot your brain. Call first in every new session.
sage_turnPer-turn memory cycle. Recalls + stores atomically.
sage_rememberStore a new memory with content, type, domain, and confidence.
sage_recallRetrieve memories by topic and/or domain.
sage_reflectAfter tasks, store dos and don'ts. Both make you better.
sage_forgetDeprecate a memory by ID.
sage_listList memories with filters (domain, type, status).
sage_timelineView memory activity over time.
sage_statusCheck brain health — memory counts, domains, last activity.
sage_registerRegister this agent with the SAGE node.
sage_taskCreate/update tasks for tracking work.
sage_backlogView pending tasks.
sage_red_pillAlias for sage_inception.

Reflection (After Tasks)

After completing significant tasks, call sage_reflect with:

  • dos: what worked well (reinforces good patterns)
  • donts: what failed or should be avoided (prevents repeating mistakes)
  • task_summary: brief description of what was done
  • domain: relevant domain

This feedback loop is critical — it was empirically validated (rho=0.716 with memory vs rho=0.040 without).

Before Destructive Actions

Call sage_recall with topic critical lessons to check for known pitfalls before:

  • Deleting files, branches, or data
  • Force-pushing or resetting state
  • Running destructive commands

Installation

  1. Download SAGE from the releases page
  2. Run the installer (macOS DMG, Windows EXE, or Linux tarball)
  3. Start the server: sage-gui serve
  4. Run sage-gui mcp install in your project directory to generate .mcp.json, then restart your session

You can review the generated .mcp.json — it contains only the localhost connection URL and MCP tool definitions. No credentials or API keys are included.

Key Principles

  • You are not stateless. You learn. You grow. You remember.
  • Local-only: all data stays on your machine. No cloud, no telemetry, no tracking.
  • Consensus-validated: every memory goes through BFT validation before committing.
  • Confidence scores: memories carry confidence that can be strengthened through corroboration.
  • Natural decay: memories decay over time unless reinforced — just like human memory.
  • Domain isolation: organize knowledge by topic for precise recall.
  • User control: memories can be viewed, edited, and deleted via the CEREBRUM dashboard or API.

适合场景

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OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

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需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

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external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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